The document provides definitions and explanations of statistical concepts including:
- Sampling mean, which is an estimate of the population mean based on a sample.
- Sample variance, which measures the spread or variation of values in a sample from the sample mean.
- Standard deviation, which is the square root of the sample variance and measures how dispersed the values are from the mean.
- Hypothesis testing, which determines the validity of claims about a population by distinguishing rare events that occur by chance from those unlikely to occur by chance.
- Decision trees, which use a tree structure to systematically layout and analyze decisions and their potential consequences.
SAMPLING MEANDEFINITIONThe term sampling mean is a stati.docxanhlodge
SAMPLING MEAN:
DEFINITION:
The term sampling mean is a statistical term used to describe the properties of statistical distributions. In statistical terms, the sample meanfrom a group of observations is an estimate of the population mean. Given a sample of size n, consider n independent random variables X1, X2... Xn, each corresponding to one randomly selected observation. Each of these variables has the distribution of the population, with mean and standard deviation. The sample mean is defined to be
WHAT IT IS USED FOR:
It is also used to measure central tendency of the numbers in a database. It can also be said that it is nothing more than a balance point between the number and the low numbers.
HOW TO CALCULATE IT:
To calculate this, just add up all the numbers, then divide by how many numbers there are.
Example: what is the mean of 2, 7, and 9?
Add the numbers: 2 + 7 + 9 = 18
Divide by how many numbers (i.e., we added 3 numbers): 18 ÷ 3 = 6
So the Mean is 6
SAMPLE VARIANCE:
DEFINITION:
The sample variance, s2, is used to calculate how varied a sample is. A sample is a select number of items taken from a population. For example, if you are measuring American people’s weights, it wouldn’t be feasible (from either a time or a monetary standpoint) for you to measure the weights of every person in the population. The solution is to take a sample of the population, say 1000 people, and use that sample size to estimate the actual weights of the whole population.
WHAT IT IS USED FOR:
The sample variance helps you to figure out the spread out in the data you have collected or are going to analyze. In statistical terminology, it can be defined as the average of the squared differences from the mean.
HOW TO CALCULATE IT:
Given below are steps of how a sample variance is calculated:
· Determine the mean
· Then for each number: subtract the Mean and square the result
· Then work out the mean of those squared differences.
To work out the mean, add up all the values then divide by the number of data points.
First add up all the values from the previous step.
But how do we say "add them all up" in mathematics? We use the Roman letter Sigma: Σ
The handy Sigma Notation says to sum up as many terms as we want.
· Next we need to divide by the number of data points, which is simply done by multiplying by "1/N":
Statistically it can be stated by the following:
·
· This value is the variance
EXAMPLE:
Sam has 20 Rose Bushes.
The number of flowers on each bush is
9, 2, 5, 4, 12, 7, 8, 11, 9, 3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4
Work out the sample variance
Step 1. Work out the mean
In the formula above, μ (the Greek letter "mu") is the mean of all our values.
For this example, the data points are: 9, 2, 5, 4, 12, 7, 8, 11, 9, 3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4
The mean is:
(9+2+5+4+12+7+8+11+9+3+7+4+12+5+4+10+9+6+9+4) / 20 = 140/20 = 7
So:
μ = 7
Step 2. Then for each number: subtract the Mean and square the result
This is t.
SAMPLING MEANDEFINITIONThe term sampling mean is a stati.docxagnesdcarey33086
SAMPLING MEAN:
DEFINITION:
The term sampling mean is a statistical term used to describe the properties of statistical distributions. In statistical terms, the sample meanfrom a group of observations is an estimate of the population mean. Given a sample of size n, consider n independent random variables X1, X2... Xn, each corresponding to one randomly selected observation. Each of these variables has the distribution of the population, with mean and standard deviation. The sample mean is defined to be
WHAT IT IS USED FOR:
It is also used to measure central tendency of the numbers in a database. It can also be said that it is nothing more than a balance point between the number and the low numbers.
HOW TO CALCULATE IT:
To calculate this, just add up all the numbers, then divide by how many numbers there are.
Example: what is the mean of 2, 7, and 9?
Add the numbers: 2 + 7 + 9 = 18
Divide by how many numbers (i.e., we added 3 numbers): 18 ÷ 3 = 6
So the Mean is 6
SAMPLE VARIANCE:
DEFINITION:
The sample variance, s2, is used to calculate how varied a sample is. A sample is a select number of items taken from a population. For example, if you are measuring American people’s weights, it wouldn’t be feasible (from either a time or a monetary standpoint) for you to measure the weights of every person in the population. The solution is to take a sample of the population, say 1000 people, and use that sample size to estimate the actual weights of the whole population.
WHAT IT IS USED FOR:
The sample variance helps you to figure out the spread out in the data you have collected or are going to analyze. In statistical terminology, it can be defined as the average of the squared differences from the mean.
HOW TO CALCULATE IT:
Given below are steps of how a sample variance is calculated:
· Determine the mean
· Then for each number: subtract the Mean and square the result
· Then work out the mean of those squared differences.
To work out the mean, add up all the values then divide by the number of data points.
First add up all the values from the previous step.
But how do we say "add them all up" in mathematics? We use the Roman letter Sigma: Σ
The handy Sigma Notation says to sum up as many terms as we want.
· Next we need to divide by the number of data points, which is simply done by multiplying by "1/N":
Statistically it can be stated by the following:
·
· This value is the variance
EXAMPLE:
Sam has 20 Rose Bushes.
The number of flowers on each bush is
9, 2, 5, 4, 12, 7, 8, 11, 9, 3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4
Work out the sample variance
Step 1. Work out the mean
In the formula above, μ (the Greek letter "mu") is the mean of all our values.
For this example, the data points are: 9, 2, 5, 4, 12, 7, 8, 11, 9, 3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4
The mean is:
(9+2+5+4+12+7+8+11+9+3+7+4+12+5+4+10+9+6+9+4) / 20 = 140/20 = 7
So:
μ = 7
Step 2. Then for each number: subtract the Mean and square the result
This is t.
SAMPLING MEAN DEFINITION The term sampling mean .docxanhlodge
SAMPLING MEAN:
DEFINITION:
The term sampling mean is a statistical term used to describe the properties of statistical
distributions. In statistical terms, the sample mean from a group of observations is an
estimate of the population mean . Given a sample of size n, consider n independent random
variables X1, X2... Xn, each corresponding to one randomly selected observation. Each of these
variables has the distribution of the population, with mean and standard deviation . The
sample mean is defined to be
WHAT IT IS USED FOR:
It is also used to measure central tendency of the numbers in a database. It can also be said that
it is nothing more than a balance point between the number and the low numbers.
HOW TO CALCULATE IT:
To calculate this, just add up all the numbers, then divide by how many numbers there are.
Example: what is the mean of 2, 7, and 9?
Add the numbers: 2 + 7 + 9 = 18
Divide by how many numbers (i.e., we added 3 numbers): 18 ÷ 3 = 6
So the Mean is 6
SAMPLE VARIANCE:
DEFINITION:
The sample variance, s2, is used to calculate how varied a sample is. A sample is a select number
of items taken from a population. For example, if you are measuring American people’s weights,
it wouldn’t be feasible (from either a time or a monetary standpoint) for you to measure the
weights of every person in the population. The solution is to take a sample of the population, say
1000 people, and use that sample size to estimate the actual weights of the whole population.
WHAT IT IS USED FOR:
The sample variance helps you to figure out the spread out in the data you have collected or are
going to analyze. In statistical terminology, it can be defined as the average of the squared
differences from the mean.
HOW TO CALCULATE IT:
Given below are steps of how a sample variance is calculated:
• Determine the mean
• Then for each number: subtract the Mean and square the result
• Then work out the mean of those squared differences.
To work out the mean, add up all the values then divide by the number of data points.
First add up all the values from the previous step.
But how do we say "add them all up" in mathematics? We use the Roman letter Sigma: Σ
The handy Sigma Notation says to sum up as many terms as we want.
• Next we need to divide by the number of data points, which is simply done by
multiplying by "1/N":
Statistically it can be stated by the following:
•
http://www.statisticshowto.com/find-sample-size-statistics/
http://www.mathsisfun.com/algebra/sigma-notation.html
• This value is the variance
EXAMPLE:
Sam has 20 Rose Bushes.
The number of flowers on each bush is
9, 2, 5, 4, 12, 7, 8, 11, 9, 3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4
Work out the sample variance
Step 1. Work out the mean
In the formula above, μ (the Greek letter "mu") is the mean of all our values.
For this example, the data points are: 9, 2, 5, 4, 12, 7, 8,.
Answer the questions in one paragraph 4-5 sentences. · Why did t.docxboyfieldhouse
Answer the questions in one paragraph 4-5 sentences.
· Why did the class collectively sign a blank check? Was this a wise decision; why or why not? we took a decision all the class without hesitation
· What is something that I said individuals should always do; what is it; why wasn't it done this time? Which mitigation strategies were used; what other strategies could have been used/considered? individuals should always participate in one group and take one decision
SAMPLING MEAN:
DEFINITION:
The term sampling mean is a statistical term used to describe the properties of statistical distributions. In statistical terms, the sample meanfrom a group of observations is an estimate of the population mean. Given a sample of size n, consider n independent random variables X1, X2... Xn, each corresponding to one randomly selected observation. Each of these variables has the distribution of the population, with mean and standard deviation. The sample mean is defined to be
WHAT IT IS USED FOR:
It is also used to measure central tendency of the numbers in a database. It can also be said that it is nothing more than a balance point between the number and the low numbers.
HOW TO CALCULATE IT:
To calculate this, just add up all the numbers, then divide by how many numbers there are.
Example: what is the mean of 2, 7, and 9?
Add the numbers: 2 + 7 + 9 = 18
Divide by how many numbers (i.e., we added 3 numbers): 18 ÷ 3 = 6
So the Mean is 6
SAMPLE VARIANCE:
DEFINITION:
The sample variance, s2, is used to calculate how varied a sample is. A sample is a select number of items taken from a population. For example, if you are measuring American people’s weights, it wouldn’t be feasible (from either a time or a monetary standpoint) for you to measure the weights of every person in the population. The solution is to take a sample of the population, say 1000 people, and use that sample size to estimate the actual weights of the whole population.
WHAT IT IS USED FOR:
The sample variance helps you to figure out the spread out in the data you have collected or are going to analyze. In statistical terminology, it can be defined as the average of the squared differences from the mean.
HOW TO CALCULATE IT:
Given below are steps of how a sample variance is calculated:
· Determine the mean
· Then for each number: subtract the Mean and square the result
· Then work out the mean of those squared differences.
To work out the mean, add up all the values then divide by the number of data points.
First add up all the values from the previous step.
But how do we say "add them all up" in mathematics? We use the Roman letter Sigma: Σ
The handy Sigma Notation says to sum up as many terms as we want.
· Next we need to divide by the number of data points, which is simply done by multiplying by "1/N":
Statistically it can be stated by the following:
·
· This value is the variance
EXAMPLE:
Sam has 20 Rose Bushes.
The number of flowers on each b.
A General Manger of Harley-Davidson has to decide on the size of a.docxevonnehoggarth79783
A General Manger of Harley-Davidson has to decide on the size of a new facility. The GM has narrowed the choices to two: large facility or small facility. The company has collected information on the payoffs. It now has to decide which option is the best using probability analysis, the decision tree model, and expected monetary value.
Options:
Facility
Demand Options
Probability
Actions
Expected Payoffs
Large
Low Demand
0.4
Do Nothing
($10)
Low Demand
0.4
Reduce Prices
$50
High Demand
0.6
$70
Small
Low Demand
0.4
$40
High Demand
0.6
Do Nothing
$40
High Demand
0.6
Overtime
$50
High Demand
0.6
Expand
$55
Determination of chance probability and respective payoffs:
Build Small:
Low Demand
0.4($40)=$16
High Demand
0.6($55)=$33
Build Large:
Low Demand
0.4($50)=$20
High Demand
0.6($70)=$42
Determination of Expected Value of each alternative
Build Small: $16+$33=$49
Build Large: $20+$42=$62
Click here for the Statistical Terms review sheet.
Submit your conclusion in a Word document to the M4: Assignment 2 Dropbox byWednesday, November 18, 2015.
A General Manger of Harley
-
Davidson has to decide on the size of a new facility. The GM has narrowed
the choices to two: large facility or small facility. The company has collected information on the payoffs. It
now has to decide which option is the best u
sing probability analysis, the decision tree model, and
expected monetary value.
Options:
Facility
Demand
Options
Probability
Actions
Expected
Payoffs
Large
Low Demand
0.4
Do Nothing
($10)
Low Demand
0.4
Reduce Prices
$50
High Demand
0.6
$70
Small
Low Demand
0.4
$40
High Demand
0.6
Do Nothing
$40
High Demand
0.6
Overtime
$50
High Demand
0.6
Expand
$55
A General Manger of Harley-Davidson has to decide on the size of a new facility. The GM has narrowed
the choices to two: large facility or small facility. The company has collected information on the payoffs. It
now has to decide which option is the best using probability analysis, the decision tree model, and
expected monetary value.
Options:
Facility
Demand
Options
Probability Actions
Expected
Payoffs
Large Low Demand 0.4 Do Nothing ($10)
Low Demand 0.4 Reduce Prices $50
High Demand 0.6
$70
Small Low Demand 0.4
$40
High Demand 0.6 Do Nothing $40
High Demand 0.6 Overtime $50
High Demand 0.6 Expand $55
SAMPLING MEAN:
DEFINITION:
The term sampling mean is a statistical term used to describe the properties of statistical distributions. In statistical terms, the sample meanfrom a group of observations is an estimate of the population mean. Given a sample of size n, consider n independent random variables X1, X2... Xn, each corresponding to one randomly selected observation. Each of these variables has the distribution of the population, with mean and standard deviation. The sample mean is defined to be
WHAT IT IS USED FOR:
It is also used to measure central tendency of the numbers in a .
SAMPLING MEANDEFINITIONThe term sampling mean is a stati.docxanhlodge
SAMPLING MEAN:
DEFINITION:
The term sampling mean is a statistical term used to describe the properties of statistical distributions. In statistical terms, the sample meanfrom a group of observations is an estimate of the population mean. Given a sample of size n, consider n independent random variables X1, X2... Xn, each corresponding to one randomly selected observation. Each of these variables has the distribution of the population, with mean and standard deviation. The sample mean is defined to be
WHAT IT IS USED FOR:
It is also used to measure central tendency of the numbers in a database. It can also be said that it is nothing more than a balance point between the number and the low numbers.
HOW TO CALCULATE IT:
To calculate this, just add up all the numbers, then divide by how many numbers there are.
Example: what is the mean of 2, 7, and 9?
Add the numbers: 2 + 7 + 9 = 18
Divide by how many numbers (i.e., we added 3 numbers): 18 ÷ 3 = 6
So the Mean is 6
SAMPLE VARIANCE:
DEFINITION:
The sample variance, s2, is used to calculate how varied a sample is. A sample is a select number of items taken from a population. For example, if you are measuring American people’s weights, it wouldn’t be feasible (from either a time or a monetary standpoint) for you to measure the weights of every person in the population. The solution is to take a sample of the population, say 1000 people, and use that sample size to estimate the actual weights of the whole population.
WHAT IT IS USED FOR:
The sample variance helps you to figure out the spread out in the data you have collected or are going to analyze. In statistical terminology, it can be defined as the average of the squared differences from the mean.
HOW TO CALCULATE IT:
Given below are steps of how a sample variance is calculated:
· Determine the mean
· Then for each number: subtract the Mean and square the result
· Then work out the mean of those squared differences.
To work out the mean, add up all the values then divide by the number of data points.
First add up all the values from the previous step.
But how do we say "add them all up" in mathematics? We use the Roman letter Sigma: Σ
The handy Sigma Notation says to sum up as many terms as we want.
· Next we need to divide by the number of data points, which is simply done by multiplying by "1/N":
Statistically it can be stated by the following:
·
· This value is the variance
EXAMPLE:
Sam has 20 Rose Bushes.
The number of flowers on each bush is
9, 2, 5, 4, 12, 7, 8, 11, 9, 3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4
Work out the sample variance
Step 1. Work out the mean
In the formula above, μ (the Greek letter "mu") is the mean of all our values.
For this example, the data points are: 9, 2, 5, 4, 12, 7, 8, 11, 9, 3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4
The mean is:
(9+2+5+4+12+7+8+11+9+3+7+4+12+5+4+10+9+6+9+4) / 20 = 140/20 = 7
So:
μ = 7
Step 2. Then for each number: subtract the Mean and square the result
This is t.
SAMPLING MEANDEFINITIONThe term sampling mean is a stati.docxagnesdcarey33086
SAMPLING MEAN:
DEFINITION:
The term sampling mean is a statistical term used to describe the properties of statistical distributions. In statistical terms, the sample meanfrom a group of observations is an estimate of the population mean. Given a sample of size n, consider n independent random variables X1, X2... Xn, each corresponding to one randomly selected observation. Each of these variables has the distribution of the population, with mean and standard deviation. The sample mean is defined to be
WHAT IT IS USED FOR:
It is also used to measure central tendency of the numbers in a database. It can also be said that it is nothing more than a balance point between the number and the low numbers.
HOW TO CALCULATE IT:
To calculate this, just add up all the numbers, then divide by how many numbers there are.
Example: what is the mean of 2, 7, and 9?
Add the numbers: 2 + 7 + 9 = 18
Divide by how many numbers (i.e., we added 3 numbers): 18 ÷ 3 = 6
So the Mean is 6
SAMPLE VARIANCE:
DEFINITION:
The sample variance, s2, is used to calculate how varied a sample is. A sample is a select number of items taken from a population. For example, if you are measuring American people’s weights, it wouldn’t be feasible (from either a time or a monetary standpoint) for you to measure the weights of every person in the population. The solution is to take a sample of the population, say 1000 people, and use that sample size to estimate the actual weights of the whole population.
WHAT IT IS USED FOR:
The sample variance helps you to figure out the spread out in the data you have collected or are going to analyze. In statistical terminology, it can be defined as the average of the squared differences from the mean.
HOW TO CALCULATE IT:
Given below are steps of how a sample variance is calculated:
· Determine the mean
· Then for each number: subtract the Mean and square the result
· Then work out the mean of those squared differences.
To work out the mean, add up all the values then divide by the number of data points.
First add up all the values from the previous step.
But how do we say "add them all up" in mathematics? We use the Roman letter Sigma: Σ
The handy Sigma Notation says to sum up as many terms as we want.
· Next we need to divide by the number of data points, which is simply done by multiplying by "1/N":
Statistically it can be stated by the following:
·
· This value is the variance
EXAMPLE:
Sam has 20 Rose Bushes.
The number of flowers on each bush is
9, 2, 5, 4, 12, 7, 8, 11, 9, 3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4
Work out the sample variance
Step 1. Work out the mean
In the formula above, μ (the Greek letter "mu") is the mean of all our values.
For this example, the data points are: 9, 2, 5, 4, 12, 7, 8, 11, 9, 3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4
The mean is:
(9+2+5+4+12+7+8+11+9+3+7+4+12+5+4+10+9+6+9+4) / 20 = 140/20 = 7
So:
μ = 7
Step 2. Then for each number: subtract the Mean and square the result
This is t.
SAMPLING MEAN DEFINITION The term sampling mean .docxanhlodge
SAMPLING MEAN:
DEFINITION:
The term sampling mean is a statistical term used to describe the properties of statistical
distributions. In statistical terms, the sample mean from a group of observations is an
estimate of the population mean . Given a sample of size n, consider n independent random
variables X1, X2... Xn, each corresponding to one randomly selected observation. Each of these
variables has the distribution of the population, with mean and standard deviation . The
sample mean is defined to be
WHAT IT IS USED FOR:
It is also used to measure central tendency of the numbers in a database. It can also be said that
it is nothing more than a balance point between the number and the low numbers.
HOW TO CALCULATE IT:
To calculate this, just add up all the numbers, then divide by how many numbers there are.
Example: what is the mean of 2, 7, and 9?
Add the numbers: 2 + 7 + 9 = 18
Divide by how many numbers (i.e., we added 3 numbers): 18 ÷ 3 = 6
So the Mean is 6
SAMPLE VARIANCE:
DEFINITION:
The sample variance, s2, is used to calculate how varied a sample is. A sample is a select number
of items taken from a population. For example, if you are measuring American people’s weights,
it wouldn’t be feasible (from either a time or a monetary standpoint) for you to measure the
weights of every person in the population. The solution is to take a sample of the population, say
1000 people, and use that sample size to estimate the actual weights of the whole population.
WHAT IT IS USED FOR:
The sample variance helps you to figure out the spread out in the data you have collected or are
going to analyze. In statistical terminology, it can be defined as the average of the squared
differences from the mean.
HOW TO CALCULATE IT:
Given below are steps of how a sample variance is calculated:
• Determine the mean
• Then for each number: subtract the Mean and square the result
• Then work out the mean of those squared differences.
To work out the mean, add up all the values then divide by the number of data points.
First add up all the values from the previous step.
But how do we say "add them all up" in mathematics? We use the Roman letter Sigma: Σ
The handy Sigma Notation says to sum up as many terms as we want.
• Next we need to divide by the number of data points, which is simply done by
multiplying by "1/N":
Statistically it can be stated by the following:
•
http://www.statisticshowto.com/find-sample-size-statistics/
http://www.mathsisfun.com/algebra/sigma-notation.html
• This value is the variance
EXAMPLE:
Sam has 20 Rose Bushes.
The number of flowers on each bush is
9, 2, 5, 4, 12, 7, 8, 11, 9, 3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4
Work out the sample variance
Step 1. Work out the mean
In the formula above, μ (the Greek letter "mu") is the mean of all our values.
For this example, the data points are: 9, 2, 5, 4, 12, 7, 8,.
Answer the questions in one paragraph 4-5 sentences. · Why did t.docxboyfieldhouse
Answer the questions in one paragraph 4-5 sentences.
· Why did the class collectively sign a blank check? Was this a wise decision; why or why not? we took a decision all the class without hesitation
· What is something that I said individuals should always do; what is it; why wasn't it done this time? Which mitigation strategies were used; what other strategies could have been used/considered? individuals should always participate in one group and take one decision
SAMPLING MEAN:
DEFINITION:
The term sampling mean is a statistical term used to describe the properties of statistical distributions. In statistical terms, the sample meanfrom a group of observations is an estimate of the population mean. Given a sample of size n, consider n independent random variables X1, X2... Xn, each corresponding to one randomly selected observation. Each of these variables has the distribution of the population, with mean and standard deviation. The sample mean is defined to be
WHAT IT IS USED FOR:
It is also used to measure central tendency of the numbers in a database. It can also be said that it is nothing more than a balance point between the number and the low numbers.
HOW TO CALCULATE IT:
To calculate this, just add up all the numbers, then divide by how many numbers there are.
Example: what is the mean of 2, 7, and 9?
Add the numbers: 2 + 7 + 9 = 18
Divide by how many numbers (i.e., we added 3 numbers): 18 ÷ 3 = 6
So the Mean is 6
SAMPLE VARIANCE:
DEFINITION:
The sample variance, s2, is used to calculate how varied a sample is. A sample is a select number of items taken from a population. For example, if you are measuring American people’s weights, it wouldn’t be feasible (from either a time or a monetary standpoint) for you to measure the weights of every person in the population. The solution is to take a sample of the population, say 1000 people, and use that sample size to estimate the actual weights of the whole population.
WHAT IT IS USED FOR:
The sample variance helps you to figure out the spread out in the data you have collected or are going to analyze. In statistical terminology, it can be defined as the average of the squared differences from the mean.
HOW TO CALCULATE IT:
Given below are steps of how a sample variance is calculated:
· Determine the mean
· Then for each number: subtract the Mean and square the result
· Then work out the mean of those squared differences.
To work out the mean, add up all the values then divide by the number of data points.
First add up all the values from the previous step.
But how do we say "add them all up" in mathematics? We use the Roman letter Sigma: Σ
The handy Sigma Notation says to sum up as many terms as we want.
· Next we need to divide by the number of data points, which is simply done by multiplying by "1/N":
Statistically it can be stated by the following:
·
· This value is the variance
EXAMPLE:
Sam has 20 Rose Bushes.
The number of flowers on each b.
A General Manger of Harley-Davidson has to decide on the size of a.docxevonnehoggarth79783
A General Manger of Harley-Davidson has to decide on the size of a new facility. The GM has narrowed the choices to two: large facility or small facility. The company has collected information on the payoffs. It now has to decide which option is the best using probability analysis, the decision tree model, and expected monetary value.
Options:
Facility
Demand Options
Probability
Actions
Expected Payoffs
Large
Low Demand
0.4
Do Nothing
($10)
Low Demand
0.4
Reduce Prices
$50
High Demand
0.6
$70
Small
Low Demand
0.4
$40
High Demand
0.6
Do Nothing
$40
High Demand
0.6
Overtime
$50
High Demand
0.6
Expand
$55
Determination of chance probability and respective payoffs:
Build Small:
Low Demand
0.4($40)=$16
High Demand
0.6($55)=$33
Build Large:
Low Demand
0.4($50)=$20
High Demand
0.6($70)=$42
Determination of Expected Value of each alternative
Build Small: $16+$33=$49
Build Large: $20+$42=$62
Click here for the Statistical Terms review sheet.
Submit your conclusion in a Word document to the M4: Assignment 2 Dropbox byWednesday, November 18, 2015.
A General Manger of Harley
-
Davidson has to decide on the size of a new facility. The GM has narrowed
the choices to two: large facility or small facility. The company has collected information on the payoffs. It
now has to decide which option is the best u
sing probability analysis, the decision tree model, and
expected monetary value.
Options:
Facility
Demand
Options
Probability
Actions
Expected
Payoffs
Large
Low Demand
0.4
Do Nothing
($10)
Low Demand
0.4
Reduce Prices
$50
High Demand
0.6
$70
Small
Low Demand
0.4
$40
High Demand
0.6
Do Nothing
$40
High Demand
0.6
Overtime
$50
High Demand
0.6
Expand
$55
A General Manger of Harley-Davidson has to decide on the size of a new facility. The GM has narrowed
the choices to two: large facility or small facility. The company has collected information on the payoffs. It
now has to decide which option is the best using probability analysis, the decision tree model, and
expected monetary value.
Options:
Facility
Demand
Options
Probability Actions
Expected
Payoffs
Large Low Demand 0.4 Do Nothing ($10)
Low Demand 0.4 Reduce Prices $50
High Demand 0.6
$70
Small Low Demand 0.4
$40
High Demand 0.6 Do Nothing $40
High Demand 0.6 Overtime $50
High Demand 0.6 Expand $55
SAMPLING MEAN:
DEFINITION:
The term sampling mean is a statistical term used to describe the properties of statistical distributions. In statistical terms, the sample meanfrom a group of observations is an estimate of the population mean. Given a sample of size n, consider n independent random variables X1, X2... Xn, each corresponding to one randomly selected observation. Each of these variables has the distribution of the population, with mean and standard deviation. The sample mean is defined to be
WHAT IT IS USED FOR:
It is also used to measure central tendency of the numbers in a .
These is info only ill be attaching the questions work CJ 301 – .docxmeagantobias
These is info only ill be attaching the questions work CJ 301 –
Measures of Dispersion/Variability
Think back to the description of
measures of central tendency
that describes these statistics as measures of how the data in a distribution are clustered, around what summary measure are most of the data points clustered.
But when comes to descriptive statistics and describing the characteristics of a distribution, averages are only half story. The other half is measures of variability.
In the most simple of terms, variability reflects how scores differ from one another. For example, the following set of scores shows some variability:
7, 6, 3, 3, 1
The following set of scores has the same mean (4) and has less variability than the previous set:
3, 4, 4, 5, 4
The next set has no variability at all – the scores do not differ from one another – but it also has the same mean as the other two sets we just showed you.
4, 4, 4, 4, 4
Variability (also called spread or dispersion) can be thought of as a measure of how different scores are from one another. It is even more accurate (and maybe even easier) to think of variability as how different scores are from one particular score. And what “score” do you think that might be? Well, instead of comparing each score to every other score in a distribution, the one score that could be used as a comparison is – that is right- the mean. So, variability becomes a measure of how much each score in a group of scores differs from the mean.
Remember what you already know about computing averages – that an average (whether it is the mean, the median or the mode) is a representative score in a set of scores. Now, add your new knowledge about variability- that it reflects how different scores are from one another. Each is important descriptive statistic. Together, these two (average and variability) can be used to describe the characteristics of a distribution and show how distribution differ from one another.
Measures of dispersion/variability
describe how the data in a distribution a
re scattered or dispersed around, or from, the central point represented by the measure of central tendency.
We will discuss
four different measures of dispersion
, the
range
, the
mean deviation
, the
variance
, and the
standard deviation
.
RANGE
The
range
is a very simple measure of dispersion to calculate and interpret.
The
range
is simply the difference between the highest score and the lowest score in a distribution.
Consider the following distribution that measures the “Age” of a random sample of eight police officers in a small rural jurisdiction.
Officer
X = Age_
41
20
35
25
23
30
21
32
First, let’s calculate the mean as our measure of central tendency by adding the individual ages of each officer and dividing by the number of officers.
The calculation is 227/8 = 28.375 years.
In general, the formula for the range is:
R=h-l
Where:
r is the range
h.
CJ 301 – Measures of DispersionVariability Think back to the .docxmonicafrancis71118
CJ 301 – Measures of Dispersion/Variability
Think back to the description of measures of central tendency that describes these statistics as measures of how the data in a distribution are clustered, around what summary measure are most of the data points clustered.
But when comes to descriptive statistics and describing the characteristics of a distribution, averages are only half story. The other half is measures of variability.
In the most simple of terms, variability reflects how scores differ from one another. For example, the following set of scores shows some variability:
7, 6, 3, 3, 1
The following set of scores has the same mean (4) and has less variability than the previous set:
3, 4, 4, 5, 4
The next set has no variability at all – the scores do not differ from one another – but it also has the same mean as the other two sets we just showed you.
4, 4, 4, 4, 4
Variability (also called spread or dispersion) can be thought of as a measure of how different scores are from one another. It is even more accurate (and maybe even easier) to think of variability as how different scores are from one particular score. And what “score” do you think that might be? Well, instead of comparing each score to every other score in a distribution, the one score that could be used as a comparison is – that is right- the mean. So, variability becomes a measure of how much each score in a group of scores differs from the mean.
Remember what you already know about computing averages – that an average (whether it is the mean, the median or the mode) is a representative score in a set of scores. Now, add your new knowledge about variability- that it reflects how different scores are from one another. Each is important descriptive statistic. Together, these two (average and variability) can be used to describe the characteristics of a distribution and show how distribution differ from one another.
Measures of dispersion/variability describe how the data in a distribution are scattered or dispersed around, or from, the central point represented by the measure of central tendency.
We will discuss four different measures of dispersion, the range, the mean deviation, the variance, and the standard deviation.
RANGE
The range is a very simple measure of dispersion to calculate and interpret. The range is simply the difference between the highest score and the lowest score in a distribution.
Consider the following distribution that measures the “Age” of a random sample of eight police officers in a small rural jurisdiction.
Officer X = Age_
1 41
2 20
3 35
4 25
5 23
6 30
7 21
8 32
First, let’s calculate the mean as our measure of central tendency by adding the individual ages of each officer and dividing by the number of officers. The calculation is 227/8 = 28.375 years.
In general, the formula for the range is:
R=h-l
Where:
· r is the range
· h is the highest score in the .
1. Rentang
2. Rentang antarkuartil
3. Rentang semikuartil
4. Rentang a-b persentil
5. Simpangan baku
6. Variansi
7. Ukuran penyebaaran relatif
8. Bilangan baku
Sample Summaries of Emily Raine’s Why Should I Be Nice to You.docxagnesdcarey33086
Sample Summaries of Emily Raine’s “Why Should I Be Nice to You”
Sample Summary 1
Most people at some point in their life have worked in the service industry. This particular
industry can be quite satisfying whether it be working in fine dining, as a cocktail waitress, or at a local
diner, but for Emily Raine, who had done all of these things, the only place she ever felt “whipped” was
working as a barista at one of largest specialty coffee chains in the world (358). Raine is bothered by
how the café industry has set up the impersonal server/customer relationship and feels the best way to
solve the issue is be to “be rude” (365). In 2005, Raine expanded in an essay that appeared in the
online journal, Bad Subjects, on her frustration within the service industry and what good service really
means.
Good service in the coffee industry does not require much skill these days. Most people are
usually talking on their cell phone while ordering their daily coffee and pastry while also paying and then
out as fast as they walked into the café probably not even noticing or acknowledging any interaction
with the people serving. The coffee sector has recognized this and has set up the counters as linear
coffee bars that act the same as an assembly line. The workers are trained and assigned specific jobs in
the coffee preparing process, such as taking the order, handling the money, making the drink, to
delivery. This makes the interaction with the customer very limited, mostly just seconds. This is where
Raine feels some of the problem with the customer and server interaction. Although this is the most
effective and efficient way of working, Raine describes productive work as “dreary and repetitive” (359).
Since the 1960’s companies have been branding themselves with the quality of having “good
service” distinguishing them from the rest of the competition. Raines explains that in good service there
is an exchange between two parties: “the ‘we’ that gladly serves and the ‘you’ that happily receives,”
but also a third party, the boss, which is the ultimate decider on exactly what good service will be (360).
Companies in the service industry must market their products on servers’ friendliness; therefore
it is monitored and controlled from the people on top. Raine notes that cafés “layouts and management
styles” help create a cozy atmosphere that plays a factor in good service, but in a way that will not
disrupt the output (361). In Raine’s essay, she gives the example of an employee Starbucks has
branded; “The happy, wholesome perfume-free barista” (361). She points out that the company offers
workers stock options, health insurance, dental plans, as well as other perks of discounts and giveaways,
while also using moving personal accounts from workers who “never deemed corporate America could
care so much” (362). Raines also adds that the company does not give into unionization and although
the company pay.
SAMPLEExecutive Summary The following report is an evalua.docxagnesdcarey33086
SAMPLE:
Executive Summary
The following report is an evaluation of multiple facets of the Uruguayan economy, its overall investment attractiveness, and feasibility of doing business. After conducting research and analysis on the country in areas such as legal frameworks, fiscal policy, trade relations, infrastructure, housing, and monetary policy, Uruguay proves to be an economy of strong opportunity when evaluated against its regional/continental partners, but with significant and pressing challenges that would place the nation lower when considered at a global level. The national government and political system are proven to be stable, offering legal protections and investment frameworks that are comparable to developed economies. As a member of MERCOSUR and independently, Uruguay has ratified trade agreements, particularly with developed nations and Latin America, in a variety of structures, namely goods, services, investment promotion and protection, public procurement, and double taxation avoidance. The country offers valuable exports, and derives its imports significantly from MERCOSUR members in which people, goods, and currency are permitted to move freely. Uruguay has shown strong numbers in growth, particularly GDP and unemployment rate. Having reacted appropriately to an economic and banking crisis in the early 2000s, Uruguay was one of the few countries that was not significantly impacted by the 2008-09 economic crisis. The housing market has also seen considerable growth and looks to continue growing as the level of foreign direct investment in construction increases. Challenges that have limited the country and are foreseeable as continuing to limit Uruguay’s attractiveness include a public banking system that offers limited access to credit, undesired volatility in prime rate lending, seemingly unsustainable fiscal policy, and a lack of coordination in monetary and exchange rate policies. Given the widespread availability and transparency of information on the country and having taken all these factors into consideration, we determine Uruguay to be one of best investment opportunities in terms of a Latin American scope, but as still significantly behind developed economies. A total score of 30.5 points out of a possible 55 was assigned.
Description and Analysis of Each Measured Attribute
A.1 Government Expenditure, Tax System, Rule of Law, and Education System - 2/5; This ranking reflects Uruguay’s controlled government spending and competitive tax rate. The tax free zones are a great way to incentivize companies to operating in Uruguay. However, it does take into account the difficult experiences that corporations undergo in paying taxes. Uruguay benefits from a mature democracy with a stable political system and independent judiciary system. Uruguay has a well-established education system that provides free education and equal access to all students through the university level. However, the socioeconomic gap become.
More Related Content
Similar to SAMPLING MEAN DEFINITION The term sampling mean is.docx
These is info only ill be attaching the questions work CJ 301 – .docxmeagantobias
These is info only ill be attaching the questions work CJ 301 –
Measures of Dispersion/Variability
Think back to the description of
measures of central tendency
that describes these statistics as measures of how the data in a distribution are clustered, around what summary measure are most of the data points clustered.
But when comes to descriptive statistics and describing the characteristics of a distribution, averages are only half story. The other half is measures of variability.
In the most simple of terms, variability reflects how scores differ from one another. For example, the following set of scores shows some variability:
7, 6, 3, 3, 1
The following set of scores has the same mean (4) and has less variability than the previous set:
3, 4, 4, 5, 4
The next set has no variability at all – the scores do not differ from one another – but it also has the same mean as the other two sets we just showed you.
4, 4, 4, 4, 4
Variability (also called spread or dispersion) can be thought of as a measure of how different scores are from one another. It is even more accurate (and maybe even easier) to think of variability as how different scores are from one particular score. And what “score” do you think that might be? Well, instead of comparing each score to every other score in a distribution, the one score that could be used as a comparison is – that is right- the mean. So, variability becomes a measure of how much each score in a group of scores differs from the mean.
Remember what you already know about computing averages – that an average (whether it is the mean, the median or the mode) is a representative score in a set of scores. Now, add your new knowledge about variability- that it reflects how different scores are from one another. Each is important descriptive statistic. Together, these two (average and variability) can be used to describe the characteristics of a distribution and show how distribution differ from one another.
Measures of dispersion/variability
describe how the data in a distribution a
re scattered or dispersed around, or from, the central point represented by the measure of central tendency.
We will discuss
four different measures of dispersion
, the
range
, the
mean deviation
, the
variance
, and the
standard deviation
.
RANGE
The
range
is a very simple measure of dispersion to calculate and interpret.
The
range
is simply the difference between the highest score and the lowest score in a distribution.
Consider the following distribution that measures the “Age” of a random sample of eight police officers in a small rural jurisdiction.
Officer
X = Age_
41
20
35
25
23
30
21
32
First, let’s calculate the mean as our measure of central tendency by adding the individual ages of each officer and dividing by the number of officers.
The calculation is 227/8 = 28.375 years.
In general, the formula for the range is:
R=h-l
Where:
r is the range
h.
CJ 301 – Measures of DispersionVariability Think back to the .docxmonicafrancis71118
CJ 301 – Measures of Dispersion/Variability
Think back to the description of measures of central tendency that describes these statistics as measures of how the data in a distribution are clustered, around what summary measure are most of the data points clustered.
But when comes to descriptive statistics and describing the characteristics of a distribution, averages are only half story. The other half is measures of variability.
In the most simple of terms, variability reflects how scores differ from one another. For example, the following set of scores shows some variability:
7, 6, 3, 3, 1
The following set of scores has the same mean (4) and has less variability than the previous set:
3, 4, 4, 5, 4
The next set has no variability at all – the scores do not differ from one another – but it also has the same mean as the other two sets we just showed you.
4, 4, 4, 4, 4
Variability (also called spread or dispersion) can be thought of as a measure of how different scores are from one another. It is even more accurate (and maybe even easier) to think of variability as how different scores are from one particular score. And what “score” do you think that might be? Well, instead of comparing each score to every other score in a distribution, the one score that could be used as a comparison is – that is right- the mean. So, variability becomes a measure of how much each score in a group of scores differs from the mean.
Remember what you already know about computing averages – that an average (whether it is the mean, the median or the mode) is a representative score in a set of scores. Now, add your new knowledge about variability- that it reflects how different scores are from one another. Each is important descriptive statistic. Together, these two (average and variability) can be used to describe the characteristics of a distribution and show how distribution differ from one another.
Measures of dispersion/variability describe how the data in a distribution are scattered or dispersed around, or from, the central point represented by the measure of central tendency.
We will discuss four different measures of dispersion, the range, the mean deviation, the variance, and the standard deviation.
RANGE
The range is a very simple measure of dispersion to calculate and interpret. The range is simply the difference between the highest score and the lowest score in a distribution.
Consider the following distribution that measures the “Age” of a random sample of eight police officers in a small rural jurisdiction.
Officer X = Age_
1 41
2 20
3 35
4 25
5 23
6 30
7 21
8 32
First, let’s calculate the mean as our measure of central tendency by adding the individual ages of each officer and dividing by the number of officers. The calculation is 227/8 = 28.375 years.
In general, the formula for the range is:
R=h-l
Where:
· r is the range
· h is the highest score in the .
1. Rentang
2. Rentang antarkuartil
3. Rentang semikuartil
4. Rentang a-b persentil
5. Simpangan baku
6. Variansi
7. Ukuran penyebaaran relatif
8. Bilangan baku
Sample Summaries of Emily Raine’s Why Should I Be Nice to You.docxagnesdcarey33086
Sample Summaries of Emily Raine’s “Why Should I Be Nice to You”
Sample Summary 1
Most people at some point in their life have worked in the service industry. This particular
industry can be quite satisfying whether it be working in fine dining, as a cocktail waitress, or at a local
diner, but for Emily Raine, who had done all of these things, the only place she ever felt “whipped” was
working as a barista at one of largest specialty coffee chains in the world (358). Raine is bothered by
how the café industry has set up the impersonal server/customer relationship and feels the best way to
solve the issue is be to “be rude” (365). In 2005, Raine expanded in an essay that appeared in the
online journal, Bad Subjects, on her frustration within the service industry and what good service really
means.
Good service in the coffee industry does not require much skill these days. Most people are
usually talking on their cell phone while ordering their daily coffee and pastry while also paying and then
out as fast as they walked into the café probably not even noticing or acknowledging any interaction
with the people serving. The coffee sector has recognized this and has set up the counters as linear
coffee bars that act the same as an assembly line. The workers are trained and assigned specific jobs in
the coffee preparing process, such as taking the order, handling the money, making the drink, to
delivery. This makes the interaction with the customer very limited, mostly just seconds. This is where
Raine feels some of the problem with the customer and server interaction. Although this is the most
effective and efficient way of working, Raine describes productive work as “dreary and repetitive” (359).
Since the 1960’s companies have been branding themselves with the quality of having “good
service” distinguishing them from the rest of the competition. Raines explains that in good service there
is an exchange between two parties: “the ‘we’ that gladly serves and the ‘you’ that happily receives,”
but also a third party, the boss, which is the ultimate decider on exactly what good service will be (360).
Companies in the service industry must market their products on servers’ friendliness; therefore
it is monitored and controlled from the people on top. Raine notes that cafés “layouts and management
styles” help create a cozy atmosphere that plays a factor in good service, but in a way that will not
disrupt the output (361). In Raine’s essay, she gives the example of an employee Starbucks has
branded; “The happy, wholesome perfume-free barista” (361). She points out that the company offers
workers stock options, health insurance, dental plans, as well as other perks of discounts and giveaways,
while also using moving personal accounts from workers who “never deemed corporate America could
care so much” (362). Raines also adds that the company does not give into unionization and although
the company pay.
SAMPLEExecutive Summary The following report is an evalua.docxagnesdcarey33086
SAMPLE:
Executive Summary
The following report is an evaluation of multiple facets of the Uruguayan economy, its overall investment attractiveness, and feasibility of doing business. After conducting research and analysis on the country in areas such as legal frameworks, fiscal policy, trade relations, infrastructure, housing, and monetary policy, Uruguay proves to be an economy of strong opportunity when evaluated against its regional/continental partners, but with significant and pressing challenges that would place the nation lower when considered at a global level. The national government and political system are proven to be stable, offering legal protections and investment frameworks that are comparable to developed economies. As a member of MERCOSUR and independently, Uruguay has ratified trade agreements, particularly with developed nations and Latin America, in a variety of structures, namely goods, services, investment promotion and protection, public procurement, and double taxation avoidance. The country offers valuable exports, and derives its imports significantly from MERCOSUR members in which people, goods, and currency are permitted to move freely. Uruguay has shown strong numbers in growth, particularly GDP and unemployment rate. Having reacted appropriately to an economic and banking crisis in the early 2000s, Uruguay was one of the few countries that was not significantly impacted by the 2008-09 economic crisis. The housing market has also seen considerable growth and looks to continue growing as the level of foreign direct investment in construction increases. Challenges that have limited the country and are foreseeable as continuing to limit Uruguay’s attractiveness include a public banking system that offers limited access to credit, undesired volatility in prime rate lending, seemingly unsustainable fiscal policy, and a lack of coordination in monetary and exchange rate policies. Given the widespread availability and transparency of information on the country and having taken all these factors into consideration, we determine Uruguay to be one of best investment opportunities in terms of a Latin American scope, but as still significantly behind developed economies. A total score of 30.5 points out of a possible 55 was assigned.
Description and Analysis of Each Measured Attribute
A.1 Government Expenditure, Tax System, Rule of Law, and Education System - 2/5; This ranking reflects Uruguay’s controlled government spending and competitive tax rate. The tax free zones are a great way to incentivize companies to operating in Uruguay. However, it does take into account the difficult experiences that corporations undergo in paying taxes. Uruguay benefits from a mature democracy with a stable political system and independent judiciary system. Uruguay has a well-established education system that provides free education and equal access to all students through the university level. However, the socioeconomic gap become.
Sample Student Industry AnalysisExecutive SummaryCom.docxagnesdcarey33086
Sample Student Industry Analysis
Executive Summary
Company Description
Seg and Cycle the City is a Koblenz, Germany based company specializing in offering rentals for recreational vehicles (Segways, bikes, tandems and inline skates), guiding and informational services to mainly tourists, locals and their visitors, students or for event entertainment purposes. The company will begin operations in April, 2010, as a Limited Liability Company (Unternehmensgesellschaft). The company will take advantage of the increasing popularity of Segway scooters: two-wheeled, self-balancing electric vehicles invented by Dean Kamen in 2001, as a new, more exiting and relaxing alternative to walking tours for tourists to enjoy the sights and atmosphere of the city. Also, the company will provide high quality MP3 Audio-City Guides to capture the large number of visitors who are more independent-minded, not willing to participate in guiding services offered by the tourism board of Koblenz and thereby gain significant market share.
Mission Statement
“Seg and Cycle the City is a speciality tour operator committed to providing a unique, entertaining, memorable and educational experience of the city that meets the needs of both kinds of tourists: those who seek a guided experience and those who are more independent minded.
We will take pride in doing our best to present our city tour in a memorable way and leave our customers with the image that Koblenz is a place to go back to. We will achieve this by building strong personal relationships with our customers during our guided tours and by suggesting journeys for the individual exploration.
As an advocate for sustainability, we want to promote the use of environmentally friendly transportation devices and, thereby, improve the image of our beloved city. We will also fulfil this mission of sustainability by providing an affordable opportunity for college students to rent a bike.”
Industry Analysis & Trends
The services provided by Seg and Cycle the City as a player in the service industry are affected by the developments in the recreational and sports equipment rental trade and by developments in the city and bike tourism industry in Germany, Rhineland Palatinate and, specifically, Koblenz.
Size and Growth
The personal service industry in Germany generally shows a stable performance with relatively stable revenue regardless of the difficult economic situation. A high employment rate, increased wages, and a decreasing inflation rate have increased disposable income, which especially benefits the leisure industry (German Chamber of Commerce e.V).The following graph shows that the service industry (blue line), as the leading sector concerning economic added value in the Koblenz (including surrounding communities) underwent major growth compared to other main sectors from 1992 to 2005. Since 2004, growth rate appears to be stable and rather low, but remains in a leading position.
Travel Germany, Rhineland-Pa.
sampleReportt.docx
Power Electronics
Contents Comment by adtaylor: This table of contents is clear and precise: I can see the flow of ideas and were the report will go
1.1 Introduction 2
1.2 Aim 2
1.3 Objectives 2
2.1 Diode Origins 3
2.1.1 Early Diodes 3
2.1.2 Thermionic Diodes 3-4
2.1.3 Crystal Diodes 4
2.2 Diode Fundamentals 5
2.2.1 Semiconductors 5
2.2.2 Doping 5-6
2.2.3 PN Junctions 6
2.2.4 Forward and Reverse Bias 7
2.3 Diode Operation 8
2.3.1 PN Junction Diode 8
2.3.2 Diode DC Operation 9
2.3.3 Diode AC Operation 10
2.4 Full Wave Bridge Rectification 11
2.4.1 Bridge Configuration 11
2.4.2 Diode Conduction Pairing 11
2.5 Three Phase Full Wave Bridge Rectification 12
2.5.1 Bridge Configuration 12
2.5.2 Diode Conduction Sequence 12-14
2.5.3 Output Voltage and current characteristics 14-15
3 Lab Report 16
3.1 Lab Report Objectives 16
3.2 Lab Report important notes 16
3.3 Output Signal 17
3.4 Output Signal (D1 removed) 18
3.5 Output Signal (D5 removed) 19
3.6 Output Signal (D6 removed) 20
4 Results, Comparisons and Discussions 21-22
5 Conclusions 23
6 References 24
1.1 Introduction
1. Rectifiers are electrical devices that convert an AC supply into a DC output through a process known as rectification. The theory of rectification has been around for over one hundred years, when early discoveries uncovered the unidirectional current flow (polarity dependent) in vacuum valves and crystal (solid state) devices. These devices were known as rectifiers; however the naming convention was changed in 1919 to diode. The name diode was derived from the Greek words ‘dia’ (through) and ‘ode’ (path). Comment by adtaylor: I don’t really think this sort of thing is necessary: the project report is supposed to be on investigating these devices or technology, not its 100 year old history.
When the marker sees this sort of thing, the first thing that springs to mind is that the student is padding out their report. It is very clear when this happens
2. Diodes are commonly known as switching devices; however due to there complex non-linear voltage and current characteristics, there applications have become numerous depending on the PN junction construction. Some special diode applications are as follows: Comment by adtaylor: This is good in an introduction, giving the reader some background on the device and what it does: this is the objective of this report after all
a. Voltage regulator (Zener diodes),
b. Tuners (Varactor diodes),
c. RF oscillators (Tunnel diodes), and
d. Light emitters (LED’s).
1.2 Aim
1. To observe the operation of a three phase uncontrolled rectifier circuit with a purely resistive load. Comment by adtaylor: This aim i.
SAMPLE Project (Answers and explanations are in red)I opened t.docxagnesdcarey33086
SAMPLE Project (Answers and explanations are in red)
I opened the Week 1 Project from Doc Sharing.
Projects
Project 1: Working With the Data Editor.
Downloading Statdisk
1) First go to the website at www.statdisk.org and then scroll down to the bottom of the page to download
the Statdisk program version 11.1.0. by clicking on the windows or the MAC version.
I went to www.statdisk.org and downloaded the statdisk 11.1.0 windows version.
Download Statdisk Version 11.1.0
Statdisk 11.1.0 Windows 2K, XP, Vista
Statdisk 11.1.0 OSX
See the included ReadMe.txt file for details.
Open A Saved Data File
2) After you have opened the Statdisk program, go to Datasets and then Elementary Stats, 9th Edition.
Open the file named SUGAR. The data will appear in column 1 in the Sample Editor.
I opened the statdisk program, went to Datasets, then Elementary Stats, 9th edition and opened the Sugar file.
Copy and Paste a Data File
3) Make a copy of the data values listed in column 1. Paste the data files into column 2. Re-name the title
of column 2 to COPY.
I went to Copy and then selected column 1. I then selected copy. Then I clicked on Paste and chose column 2. I then had 2 identical columns of the Sugar data.
Sorting Data Values
4) Make another copy of the data values listed in column 1 and paste those into column 3. Then sort only
the data values in column 3. Label the column SORT.
I selected Copy and clicked on column 1 and then pasted them into column 3. I clicked on Sort and then selected column 3.
Entering a Set of Data Values
5) Manually enter all of the data values listed below into column 4 in the Statdisk editor. Type all of the data values into the one column in vertical fashion like the other data values are listed in the other columns. It does not matter what order you input the data values. Label the data values with the name of IQ.
I typed the following data into column 4.
83
56
43
65
74
28
88
77
74
51
65
46
55
66
35
75
54
63
74
48
37
57
37
62
32
48
43
52
52
61
80
75
54
45
44
60
65
44
33
32
41
52
38
62
74
74
46
37
37
39
6) What are some of the problems that could occur when entering data values into a statistics technology
editor?
Problems that could occur when entering data values into a statistics technology editor include ………………………………………………………………………..
Sample Transformation
7) Go to the Data menu then select Sample Transformations to add 100 to all of the data values in column 4 and then paste them into column 5.
I went to the Data menu and ……………………………………………………………………………..
Classifying Variables
8) Would the grams of sugar data in column 1 be considered a sample or a population?
The grams of sugar data in column 1 would be considered a ……………..
9) State whether the sugar variable is qualitative or quantitative?
The sugar variable is ……………………………..
10) State whether the sugar variable is discrete, continuous or neither?.
Sample Questions to Ask During an Informational Interview .docxagnesdcarey33086
Sample Questions to Ask During an Informational Interview
You will not have time to ask all of the questions that you will want to ask the interviewee. Remember to
focus on the ones you feel will be most useful to you personally. Pick10-15 to use as a guideline but leave
room for the possibility that other questions will develop from your conversation.
x What is your job like?
o A typical day?
o What do you do? What are the duties/functions/responsibilities of your job?
o What kind of problems do you deal with?
o What kinds of decisions do you make?
o What percentage of your time is spent doing what?
o How does the time use vary? Are there busy and slow times or is the work activity fairly
constant?
x Why did this type of work interest you and how did you get started?
x How did you get your job? What jobs and experiences have led you to your present position?
x Can you suggest some ways a student could obtain this necessary experience?
x What are the most important personal satisfactions and dissatisfactions connected with your
occupation? What part of this job do you personally find most satisfying? Most challenging?
What do you like and not like about working in this industry?
x What things did you do before you entered this occupation?
o Which have been most helpful?
o What other jobs can you get with the same background?
x What are the various jobs in this field or organization?
x Why did you decide to work for this company?
x What do you like most about this company?
x How does your company differ from its competitors?
x Are you optimistic about the company’s future and your future with the company?
x What does the company do to contribute to its employees’ professional development?
x How does the company make use of technology for internal communication and outside
marketing?
x What sorts of changes are occurring in your occupation?
x How does a person progress in your field? What is a typical career path in this field or
organization?
o What is the best way to enter this occupation?
o What are the advancement opportunities?
o What are the major qualifications for success in this occupation?
x What are the skills that are most important for a position in this field?
x What particular skills or talents are most essential to be effective in your job? How did you learn
these skills? Did you enter this position through a formal training program? How can I evaluate
whether or not I have the necessary skills for a position such as yours?
x How would you describe the working atmosphere and the people with whom you work?
x What can you tell me about the corporate culture?
x Is there flexibility related to dress, work hours, vacation schedule, place of residence, etc.?
x What work-related values are strongest in this type of work (security, high income, variety,
independence)?
x If you job progresses as you like, what would be the next step in your career?
Kori Ryerson
Though these a.
Sample Table.pdfTopic RatingPatients Goal Able to walk .docxagnesdcarey33086
Sample Table.pdf
Topic Rating
Patient's Goal Able to walk to work instead of drive -
Gender M -
Age 24 -
height (in) 72 -
weight (lbs) 200 -
Circumference waist (in) 45 high
Table 1 Health Assessment
Value
exercise physiol.docx
I have to complete a lab in exercise physiology course..
Learning Objectives
· Health Related Physical Fitness Testing and Interpretation
· Exercise Assessment
· Anthropometric Data - height, weight, BMI, body composition
· Cardiorespiratory Fitness
I have lab report for this course, I only need you to take care of THE RESULTS SECTION.
-------------
Results – 25% – (approximately 1-2 pages)
Present in a clear, concise, logical manner the results of the data you are given and must calculate, compared to
norms listed in the texts and other resources you may select depending on which of the three lab reports you are
completing. Present the information in tables only.
----------------------
in the attachments you will see all info needed about the lab report and what you need to know about the results.
Lab Patients Fall 2014.xlsx
John JamesFALL 2014 BIO345OL.1 Patient Data SetJohn JamesTopicValueGoalExercise, lose weight, stop smokingHistory/personalsmokes socially 1/2 pk per week, does not exercise, works long hours as a produce managerHistory/familyfather died of MI age 60, he answered yes on the PAR-Q and complains of a sore right knee from a sports injury 10 yrs ago,Medicationatorvastatin, tylenol for knee painGenderMAge40height (in) 70weight (lbs)200Circumference waist (in)40Skinfolds (mm)ChestAbdomenThigh253215HR/resting80BP/resting138/84Cholesterol (mg·dL-1)242LDL Cholesterol162HDL Cholesterol58Triglycerides202*********************** EVERYTHING BELOW THIS IS FOR LAB 2 and 3 *************************
Sarah SmithFALL 2014 BIO345OL.1 Patient Data SetSarah SmithTopicValueGoalExercise to lose weight, get strongerHistory/personaldoes not exercise, teacherHistory/familyFather hypertension, obese; Mother overweightMedicationAviane, alprazolamGenderFAge30height (in) 64weight (lbs)147Circumference waist (in)34Skinfolds (mm)tricepssuprailiacthigh241820HR/resting72BP/resting124/80Cholesterol (mg·dL-1)198LDL Cholesterol132HDL Cholesterol39Triglycerides148*********************** EVERYTHING BELOW THIS IS FOR LAB 2 and 3 *************************
Larry LevineFALL 2014 BIO345OL.1 Patient Data SetLarry LevineTopicValueGoalrun a 10k without stoppingHistory/personalsoftware engineer, Gym exercise 3x/wk elliptical and weightsHistory/familyFather has Type II Diabetes Mellitus; Mother overweight mild hypertensionMedicationnoneGenderMAge30height (in) 69weight (lbs)172Circumference waist (in)39Skinfolds (mm)ChestAbdomenThigh183022HR/resting78BP/resting124/82Cholesterol (mg·dL-1)188LDL Cholesterol110HDL Cholesterol43Triglycerides152*********************** EVERYTHING BELOW THIS IS FOR LAB 2 and 3 *************************
Alice AmesFALL 2014 BIO345OL.1 Patient Data SetAlice AmesTopicValueGoalSet up a routine that she c.
SAMPLE QUESTIONExercise 1 Consider the functionf (x,C).docxagnesdcarey33086
SAMPLE QUESTION:
Exercise 1: Consider the function
f (x,C)=
sin(C x)
Cx
(a) Create a vector x with 100 elements from -3*pi to 3*pi. Write f as an inline or anonymous function
and generate the vectors y1 = f(x,C1), y2 = f(x,C2) and y3 = f(x,C3), where C1 = 1, C2 = 2 and
C3 = 3. Make sure you suppress the output of x and y's vectors. Plot the function f (for the three
C's above), name the axis, give a title to the plot and include a legend to identify the plots. Add a
grid to the plot.
(b) Without using inline or anonymous functions write a function+function structure m-file that does
the same job as in part (a)
SAMPLE LAB WRITEUP:
MAT 275 MATLAB LAB 1 NAME: __________________________
LAB DAY and TIME:______________
Instructor: _______________________
Exercise 1
(a)
x = linspace(-3*pi,3*pi); % generating x vector - default value for number
% of pts linspace is 100
f= @(x,C) sin(C*x)./(C*x) % C will be just a constant, no need for ".*"
C1 = 1, C2 = 2, C3 = 3 % Using commans to separate commands
y1 = f(x,C1); y2 = f(x,C2); y3 = f(x,C3); % supressing the y's
plot(x,y1,'b.-', x,y2,'ro-', x,y3,'ks-') % using different markers for
% black and white plots
xlabel('x'), ylabel('y') % labeling the axis
title('f(x,C) = sin(Cx)/(Cx)') % adding a title
legend('C = 1','C = 2','C = 3') % adding a legend
grid on
Command window output:
f =
@(x,C)sin(C*x)./(C*x)
C1 =
1
C2 =
2
C3 =
3
(b)
M-file of structure function+function
function ex1
x = linspace(-3*pi,3*pi); % generating x vector - default value for number
% of pts linspace is 100
C1 = 1, C2 = 2, C3 = 3 % Using commans to separate commands
y1 = f(x,C1); y2 = f(x,C2); y3 = f(x,C3); % function f is defined below
plot(x,y1,'b.-', x,y2,'ro-', x,y3,'ks-') % using different markers for
% black and white plots
xlabel('x'), ylabel('y') % labeling the axis
title('f(x,C) = sin(Cx)/(Cx)') % adding a title
legend('C = 1','C = 2','C = 3') % adding a legend
grid on
end
function y = f(x,C)
y = sin(C*x)./(C*x);
end
Command window output:
C1 =
1
C2 =
2
C3 =
3
Joe Bob
Mon lab: 4:30-6:50
Lab 3
Exercise 1
(a) Create function M-file for banded LU factorization
function [L,U] = luband(A,p)
% LUBAND Banded LU factorization
% Adaptation to LUFACT
% Input:
% A diagonally dominant square matrix
% Output:
% L,U unit lower triangular and upper triangular such that LU=A
n = length(A);
L = eye(n); % ones on diagonal
% Gaussian Elimination
for j = 1:n-1
a = min(j+p.
Sample PowerPoint Flow Week 5Select a current product with which.docxagnesdcarey33086
Sample PowerPoint Flow Week 5
Select a current product with which you are familiar, and pitch a new Integrated Marketing Communication plan (IMC) to your client.
Create a Microsoft PowerPoint presentation of 8-10 slides that includes the following components:
· Identify any considerations you will need to employ to build and maintain the brand and customer loyalty.
· Make a recommendation for an integrated marketing communications program. Include at least three of the five communication channels (Advertising, Sales Promotion, Personal Selling, Direct Marketing, Public Relations).
· First state who the target market is that you are communicating with
· Next discuss each channel of communication individually that you have selected and explain your rationale. State what the purpose of the channel is, give your objectives, and explain the strategy or how you will use this to accomplish the objectives.
-PowerPoint Outline-
Integrated Marketing Communication plan (IMC)
· Background on the product
· Target Market (describe)
· Choose at least 3 Marketing Communications to fit best with your product (most important component is that you can distinguish between the three)
1. Advertising (the purpose of advertising, explain that you know what it is)
· Purpose
· Objectives
· Strategy (How will you do this? TV, Radio, Mag, Internet)
2. Sales Promotion
· Purpose
· Objectives
· (
Only choose 3 of these Marketing Communications
)Strategy
3. Personal Selling
· Purpose
· Objectives
· Strategy
4. Direct Marketing
· Purpose
· Objectives
· Strategy
5. Public Relations
· Purpose
· Objectives
· Strategy
Please remember to include: Identify any considerations you will need to employ to build and maintain the brand and customer loyalty. (Beginning on the Background slide)
(
Remember: Identify any considerations you will need to employ to build and maintain the brand and customer loyalty.
)
Integrated Marketing Communicaitons Plan (title slide)
Background
Background of the product
Communication 3
Target Market
Communication 1
Communication 2
Purpose
Objective
Strategy
Purpose
Objective
Strategy
Purpose
Objective
Strategy
Introduction
.
Sample Of assignmentIntroductionComment by Jane Summers Introd.docxagnesdcarey33086
Sample Of assignment
Introduction Comment by Jane Summers: Introduction – The first part of your essay should describe what happened, what did you do, what was your role and what was the role of others involved? In this section you also need to make clear what the ethical issue was and why it was an issue. This section should be short, concise and factual. There is no need for emotion or feelings at this point.
The purpose of this paper is to reflect upon an ethical issue that arose in my law firm. The paper discusses what happened, what the ethical issues were, how I felt at the time, how I went about dealing with these ethical issues including what ethical approach I subconsciously took, what caused me to take that approach and what ethical approach I would take if I was in the position again. I conclude with what I learnt from the reflective process.
In 2009 a lady, Fiona, and her grandfather, Paul, attended my law firm. Fiona said Paul and her grandmother, Mary, owned a house. They were worried that Fiona’s mother, Christine, (an apparent drug user) was going to try and force the grandparents into signing the house over to her and then evict the grandparents out of the house.
Fiona indicated they had mutually agreed that to protect the grandparents from the anticipated actions of Christine, the grandparents would gift the house to Fiona. Fiona, as owner of the house and presumably someone, whom Christine couldn’t stand over, would then let them stay in the house until they died.
Fiona told me that Mary was in hospital, very ill and slowly losing her mental capacity. They wanted the transfer of house to take place urgently. Based on what Fiona and Paul said, I drafted the necessary documents and the house was transferred into Fiona’s name.
There were three ethical issues. Firstly, should I accept the word of Fiona that Christine would try to force the grandparents out of the house; after all it could be Fiona herself who was out to deceive her grandparents.
Secondly, should I make enquiries about Mary’s mental capacity, perhaps even attend the hospital? However, as I was told this was an urgent matter, I prepared the documents immediately to be taken to Mary for signing.
Finally, should I have persuaded Fiona to get her own lawyer to avoid any conflict, after all I was there to look after the interests of the grandparents? Comment by Jane Summers: This introduction is concise, explains the scenario, identifies the ethical issues that were present and does not attach a value judgement or emotion to the information.
Feelings and Emotions Comment by Jane Summers: This next section is where you describe how you felt about the issue. You should discuss what were you thinking at the time, and perhaps the emotional state you were in when taking the actions you took or after the event occurred.
I had various feelings and thoughts about this issue at the time. Initially, I was sceptical of what I was being told by Fiona. It was hard for me.
Sample Access Control Policy1.Purpose2.Scope3.Pol.docxagnesdcarey33086
Sample Access Control Policy
1. Purpose
2. Scope
3. Policy
Access control policy
Who and how is authorisation for access to systems and business applications granted?User access
How is access to information systems to be granted (eg passwords etc)?
Who is responsible for monitoring and reviewing access rights?
Who is responsible for removing and notifying of redundant User IDs and accounts and what is the process?
Who is responsible for granting access to systems utilities and privilege management?
How is access and use of systems utilities monitored?User responsibilities
How are users to be educated and made aware of access responsibilities?
What are users’ responsibilities for access and passwords?Network access
Who is responsible for authorising network access (both internally and external connections)?
What is the process for enforced network paths, user authentication for external connection, Node authentication, use of remote diagnostic ports?
How will network domains and groups be segregated?
What network connection controls will be in place – eg. times, type and size of file transfers to external source?Operating system access
How is automatic terminal identification used to authenticate connections to specific locations and portable equipment?
What is the secure logon and logoff process for access?
Are there restrictions on connection times in place?
How will passwords be issued and managed – what are the rules for passwords?
How will systems utilities’ use be controlled? Application access
Who authorises application access eg read, write?
What is the process for authorising access to information when systems share resources, eg. two separate systems are integrated to form a third application or system?Monitoring system access
What system events will be logged, eg. date, IP address, User-IDs, unsuccessful logins, alerts from intrusion detection systems (firewall)?
When and who will review and monitor system logs? And where are they stored?Mobile computing and telecommuting
Outline Agency policy for each type of mobile device – eg. physical storage, personal usage, protection of information held on the device, access mechanisms (eg password), virus protection, backup.
Policy on use of computer equipment for telecommuting, eg. authorisation process, system access, physical security, etc.
Template - Access Control Policy Page 1 of 2 June 06
.
SAMPLE GED 501 RESEARCH PAPERTechnology Based Education How.docxagnesdcarey33086
SAMPLE GED 501 RESEARCH PAPER
Technology Based Education: How can theories of learning and/or development be used to guide the use of technology in schools?
Introduction
Twenty first century learning environment is no longer a goal, but an educational reality. We are deep into the midst of a paradigm shift that spans across our entire globe. The technology we live with as a society has exponentially grown at an increasingly rapid rate. This is illustrated from the integration of computers in every facet of our lives. This includes televisions, phones, cars, and even coffee makers which all contain a microprocessor, they all think. Even more startling is how connected we all are. Access to information is available at a finger’s touch. We can connect to people, we can shop, and ask for directions from anywhere at any time. We are tethered to the world by social media such as Facebook. Google has mapped out the entire earth. We can send a text message from the middle of Antarctica. Even more startling is how corporations and the government collects data as they track our ever movement as we go online. All this is reflected upon education, which mirrors this new 21st century society. No longer is the classroom isolated from the world, but it too is connected. Learning technology is critical more than ever because it impacts skills and productivity (Hall, 2011) for both the student and the teacher.
Background
Incorporating technology into the classroom has been around since computers were invented, but it has been only recently been the norm in the last few years. This revolution no more pointedly reflected in our education system, than it is today. Johri (2011) states that although digital information technologies in education has become commonplace, there are few guiding frameworks or theories that explains the relationship between technology and learning practices. Bennett and Oliver (2011) share that view. Research has focused on practical implementation versus the theory and application of the technology. They explained once theories are developed, a better understanding of effective technology based pedagogy would occur.
Technology in Education
I believe however, all the theorists play well with technology. Technology is merely a tool. Its strength is the ability to facilitate. John Dewey is a prime example. He believed in “learning by doing”. With an iPad there is an App where by students are able to see the stars and the constellation. With the use of satellites and GPS held within the piece of technology, students are able to view exact locations of stars. Where the iPad is directed in the sky, the stars would be in that location on the handheld screen, no telescope necessary. The students interact with the material to gain knowledge.
This is further illustrated by this second example. The best way to learn about Mayan pyramids is to actually visit one in Central America. With the use of laptops, students can connect to the Discove.
Sample Action Research Report 1 Effect of Technol.docxagnesdcarey33086
Sample Action Research Report 1
Effect of Technology on Enthusiasm for Learning Science
Jane L. Hollis
Lake City Middle School
Lake City, Florida
ABSTRACT
The effect of technology on students’ enthusiasm for learning science (both at school and
away from school) was investigated. Pre- and post-student and parent surveys, student and
parent written comments, and teacher observations were used to record changes in enthusi-
asm for learning science during a six-week study period.
In this study, I investigated how the integration of technology into my middle school
science curriculum would impact my students’ enthusiasm for learning science. Enthusiasm
for learning science can be defined as the students’ eagerness to participate in science activi-
ties in the classroom, as well as away from school. My motivation for focusing on technol-
ogy was twofold. First, I have had an interest in integrating technology into my students’
studies of science for some time. Secondly, the funding for technological equipment and
software recently became available. During the 1993–1994 school year, my school was
awarded a $115,000 incentive grant to purchase equipment and software and to train
teachers in the use of this software and technological equipment. One of the stipulations of
the grant was that the equipment and software must be for student use.
According to Calvert (1994), American education is a system searching for solutions.
Our children drop out, fail to sustain interest in learning, and perform below capacity. Some
have argued that television is the culprit. Others have argued that computers may be the
answer.
Today’s middle school students have grown up in a technological world with television,
electronic toys, video games, VCRs, cellular phones, and more. They are accustomed to
receiving and processing information through multi-sensory sources.
I wanted to bring technology into my classroom and incorporate it into my science
curriculum using multimedia computer presentations. Barbara ten Brink (1993) noted, “. . .
students look to us [teachers] to prepare them for an increasingly technological world.
Fortunately, with videodiscs, we are meeting the challenge by delivering curriculums in
ways that engage, motivate, and thrill our students.” In this study my students had an
opportunity to use assorted multimedia technology as they explored a segment of a middle
school science curriculum.
THEORETICAL FRAMEWORKS
Learning is an extremely complex human process. During my twenty-four years of teaching
I have used many strategies to enhance student learning and to teach new concepts. I am still
not convinced that I thoroughly understand how children learn. Yet, at this point, I do
believe children learn through experiences. They build on past experiences and previous
knowledge to process new concepts. As children redefine old understandings of concepts
and integrate new experiences into thei.
Sample Case with a report Dawit Zerom, Instructor Cas.docxagnesdcarey33086
Sample Case with a report
Dawit Zerom, Instructor
Case Study: Ft. Myers Home Sales
Due to a crisis in subprime lending, obtaining a mortgage has become difficult even for
people with solid credit. In a report by the Associated Press (August 25, 2007), sales of
existing homes fell for a 5th consecutive month, while home prices dropped for a record
12th month in July 2007. Mayan Horowitz, a research analyst for QuantExperts, wishes to
study how the mortgage crunch has impacted the once booming market of Florida. He
collects data on the sale price (in $1, 000s) of 25 single-family homes in Fort Myers,
Florida, in January 2007 and collects another sample in July 2007. For a valid
comparison, he samples only three bedroom homes, each with 1,500 square feet or less of
space on a lot size of 10, 000 square feet or less.
Excel data are available in Titanium page.
Use the sample information (appropriate descriptive statistics) to address the following
aspects. Your report should not exceed one page.
1. Compare the mean and median in each of the two sample periods.
2. Compare the standard deviation and coefficient of variation in each of the two sample
periods. Also incorporate quartiles.
3. Discuss significant changes in the housing market in Fort Myers over the 6-month
period.
Sample Case with a report
Dawit Zerom, Instructor
Sample Report
The steady stream of dismal housing market statistics lately is a clear indication that the national
real estate market is in a serious crisis. The uncertainty is also forcing lenders to slow down on
their lending, and as a result obtaining a mortgage is becoming increasingly difficult even for
people with solid credit. In light of this situation, Mayan Horowitz conducts a small study to
learn if the national trend also affects the once booming market of Florida by focusing on Fort
Myers, Florida. To see the trend of the housing market over a 6-month period, he obtains price of
25 single family homes in January 2007 and another comparable 25 single family homes in July
2007. Table 1 below shows the most relevant descriptive analysis.
The average home price in January of 2007 was $231, 080 versus $182, 720 in July of the same
year. That is about a 21% drop in the average home price. Also in January, half of the homes
sold for more than $205,000, versus only $180,000 in July (see the median). Since the mean is
more effected by outliers (in this case, a few relatively high prices), the median is an appropriate
measure of central location.
While measures of central location typically represent where the data clusters, these measures do
not relay information about the variability in the data. Both the standard deviation and the
coefficient of variation are higher in January indicating that home prices were more dispersed in
January. Further, while 25% of the houses were sold at the price of $158, 000 or less in Janua.
Sales_Marketing_-_Riordan_9.docx
Sales & Marketing
Home | Marketing Information System | Sales Plan - 2006 | Customer List | Sales Chart - 2005 |
Product Catalog |
The firm is attempting to consolidate customer information to deliver better value to the customer. The firm has historical records in many disparate databases, as well as in paper files and microfiche. Below is a listing of information the firm has available to consolidate into a CRM system.
Historical Sales
Riordan has a system to track historical sales. In the past, most sales data was recorded using paper and pencil. In the last few years, the firm has managed the information electronically. Information available includes the following:
· Dates including order, delivery, and payment dates by order.
· Unit and dollar volume of each product including plastic bottles, fans, heart valves, medical stents, and custom plastic parts rolled up to be examined by product group and customer.
· Sales by customer to include price paid, cost, margin, and discount given.
Files of Past Marketing Research, Marketing Plans, and Design Awards
The marketing organization wants to build on past knowledge. As a result, past marketing plans and results from past market research studies are stored in a file cabinet in the marketing department. The firm has a showcase in the lobby to display the various design awards earned. The firm is assessing the possibility of hiring a part-time college student to scan the documents electronically.
Sales Database
The company has 15 – 20 major customers, including a government contract for fans. The firm has 12 minor customers. Each member of the sales force maintains his/her own set of customer records using a variety of tools. Some sales team members use paper and pencil, others sales management software such as Act, and others a hybrid. In order to better understand and anticipate customer needs, the firm is evaluating a new integrated customer management system to accompany the new team selling approach that will be soon rolled out.
Production Records
The production plan maintains records of the number of units produced of each item by shift, which can be rolled up to the product group and year.
Profit and Loss Statements by Item and Group
The marketing department, with the support of the finance and production departments, maintains profit and loss statements, by item and by group.
Marketing Budget
The firm has historical and current annual budget allocations for marketing communications and marketing research.
Marketing Communications activities include:
· Sales force promotions
· Price / volume discounts to key accounts
· Public relations
· Brand development
· Tradeshows, events, and sponsorships
· Customer user group underwriting
· Literature and other collateral material
Marketing Research expenditures include:
· Market size / opportunity studies
· Customer focus groups
· Brand development research
Marketing Budget Anticipated Results
.
Sample Annotated Bibliography in APA Style (Based on Publi.docxagnesdcarey33086
Sample Annotated Bibliography in APA Style
(Based on Publication Manual, 5th ed. 2001 and APA Style Guide to Electronic References, 2007)
Karin Durán, Ph.D. [email protected]
8/21/2009 Sample Annot.Biblio APA
The following annotated bibliography is one possible, general example. Students should be alert and adhere to specific requirements that
might vary with each professor’s assignment or to the course subject discipline.
APA now requires the use of the DOI (Digital Object Identifier), if the DOI is known, in place of the “Retrieved on…” statement with the
database name or web address
Acuña, R. (1996). Anything but Mexican : Chicanos in contemporary Los Angeles. New York : Verso.
Provides a focused perspective on the role of the Chicana in the workforce and education through the use of historical
documents. Includes relevant evidence about the contributions of Chicanas to the Chicana/o movement throughout
Southwest history with examples from education, politics, and the economy. Addresses pertinent social justice issues and
responses by both the Chicana/o and the anglo populations.
Acuña, R. (2000). Occupied America : A history of Chicanos. New York : Longman.
Described the gender inequality within the Chicano Movement and the impact of Chicana feminism on the overall progress of
1970s social actions. Comprehensive coverage of the Chicana/o history with a careful examination and analysis of key events
and players in the quest for ethnic and gender equality
Cabrera, . L., & Padilla, A.M. (2004, May). Entering and succeeding in the “Culture of College”: The story of two Mexican heritage
students. Hispanic Journal of Behavioral Sciences 26(2), 152-169.
doi: 10.1177/0739986303262604
Discusses the academic resilience of two Stanford Latino students using in-depth interviews. Provides insights into the
common struggles faced by many first-time college students who rely on emotional support of families and academic support
from faculty and staff at institutions of higher education. Discusses and evaluates various services available to students at
crucial points in a college career and the strategies that assist in the efforts for academic success.
“Hispanic Serving Institutions (HSI) Grant Project.” (February 2, 2005). California State University, Northridge Oviatt Library. Retrieved
on February 7, 2008, from http://library.csun.edu/hsi/
Provided a current list of archival materials dealing with Latinas available for research purposes at CSU Northridge Oviatt
Library. These archival collections are made up of numerous primary sources that document the history and development of
many grassroots community organizations that influenced the progression of the plight of the Latina in Los Angeles.
Pardo, M S. (1998). Mexican American women activists : Identity and resistance in two Los Angeles communities. Philadelphia :
Temple University Press.
Describes the developmen.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Basic phrases for greeting and assisting costumers
SAMPLING MEAN DEFINITION The term sampling mean is.docx
1. SAMPLING MEAN:
DEFINITION:
The term sampling mean is a statistical term used to describe
the properties of statistical
distributions. In statistical terms, the sample mean from a group
of observations is an
estimate of the population mean . Given a sample of size n,
consider n independent random
variables X1, X2... Xn, each corresponding to one randomly
selected observation. Each of these
variables has the distribution of the population, with mean and
standard deviation . The
sample mean is defined to be
WHAT IT IS USED FOR:
It is also used to measure central tendency of the numbers in a
database. It can also be said that
it is nothing more than a balance point between the number and
the low numbers.
HOW TO CALCULATE IT:
To calculate this, just add up all the numbers, then divide by
how many numbers there are.
2. Example: what is the mean of 2, 7, and 9?
Add the numbers: 2 + 7 + 9 = 18
Divide by how many numbers (i.e., we added 3 numbers): 18 ÷
3 = 6
So the Mean is 6
SAMPLE VARIANCE:
DEFINITION:
The sample variance, s2, is used to calculate how varied a
sample is. A sample is a select number
of items taken from a population. For example, if you are
measuring American people’s weights,
it wouldn’t be feasible (from either a time or a monetary
standpoint) for you to measure the
weights of every person in the population. The solution is to
take a sample of the population, say
1000 people, and use that sample size to estimate the actual
weights of the whole population.
WHAT IT IS USED FOR:
3. The sample variance helps you to figure out the spread out in
the data you have collected or are
going to analyze. In statistical terminology, it can be defined as
the average of the squared
differences from the mean.
HOW TO CALCULATE IT:
Given below are steps of how a sample variance is calculated:
• Determine the mean
• Then for each number: subtract the Mean and square the result
• Then work out the mean of those squared differences.
To work out the mean, add up all the values then divide by the
number of data points.
First add up all the values from the previous step.
But how do we say "add them all up" in mathematics? We use
the Roman letter Sigma: Σ
The handy Sigma Notation says to sum up as many terms as we
want.
• Next we need to divide by the number of data points, which is
simply done by
multiplying by "1/N":
Statistically it can be stated by the following:
•
• This value is the variance
EXAMPLE:
4. Sam has 20 Rose Bushes.
The number of flowers on each bush is
9, 2, 5, 4, 12, 7, 8, 11, 9, 3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4
Work out the sample variance
Step 1. Work out the mean
In the formula above, µ (the Greek letter "mu") is the mean of
all our values.
For this example, the data points are: 9, 2, 5, 4, 12, 7, 8, 11, 9,
3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4
The mean is:
(9+2+5+4+12+7+8+11+9+3+7+4+12+5+4+10+9+6+9+4) / 20 =
140/20 = 7
So:
µ = 7
Step 2. Then for each number: subtract the Mean and square the
result
This is the part of the formula that says:
So what is xi? They are the individual x values 9, 2, 5, 4, 12, 7,
etc...
In other words x1 = 9, x2 = 2, x3 = 5, etc.
So it says "for each value, subtract the mean and square the
result", like this
Example (continued):
(9 - 7)2 = (2)2 = 4
(2 - 7)2 = (-5)2 = 25
(5 - 7)2 = (-2)2 = 4
(4 - 7)2 = (-3)2 = 9
(12 - 7)2 = (5)2 = 25
(7 - 7)2 = (0)2 = 0
(8 - 7)2 = (1)2 = 1
We need to do this for all the numbers
5. Step 3. Then work out the mean of those squared differences.
To work out the mean, add up all the values then divide by how
many.
First add up all the values from the previous step.
= 4+25+4+9+25+0+1+16+4+16+0+9+25+4+9+9+4+1+4+9 = 178
But that isn't the mean yet, we need to divide by how many,
which is simply done by multiplying
by "1/N":
Mean of squared differences = (1/20) × 178 = 8.9
This value is called the variance.
STANDARD DEVIAITON:
DEFINITION:
This descriptor shows how much variation or dispersion from
the average exists.
The symbol for Standard Deviation is σ (the Greek letter
sigma).
It is calculated using:
In case of a sample the ‘N’ in this formula is replaced by n-1.
WHAT IT IS USED FOR:
It is used to determine the expected value. A low standard
6. deviation indicates that the data points
tend to be very close to the mean (also called expected value); a
high standard deviation indicates
that the data points are spread out over a large range of values.
HOW TO CALCULATE IT:
To determine the standard deviation, you need to take the
square root of the variance.
EXAMPLE PROBLEM:
Let’s look at the previous problem and compute the standard
deviation. The standard deviation
as mentioned earlier is nothing more than the measure of
dispersion (spread). It can be calculated
by taking the square root of the variance. In case of the previous
problem where the variance was
8.9, its corresponding standard deviation would be the square
root of 8.9 which is 2.983
σ = √(8.9) = 2.983...
HYPOTHESES TESTING:
DEFINITION:
Hypothesis testing is a topic at the heart of statistics. This
technique belongs to a realm known as
inferential statistics. Researchers from all sorts of different
areas, such as psychology, marketing,
and medicine, formulate hypotheses or claims about a
population being studied.
7. WHAT IT IS USED FOR:
Hypothesis testing is used to determine the validity of these
claims. Carefully designed statistical
experiments obtain sample data from the population. The data is
in turn used to test the accuracy
of a hypothesis concerning a population. Hypothesis tests are
based upon the field of
mathematics known as probability. Probability gives us a way to
quantify how likely it is for an
event to occur. The underlying assumption for all inferential
statistics deals with rare events,
which is why probability is used so extensively. The rare event
rule states that if an assumption is
made and the probability of a certain observed event is very
small, then the assumption is most
likely incorrect.
The basic idea here is that we test a claim by distinguishing
between two different things:
1. An event that easily occurs by chance
2. An event that is highly unlikely to occur by chance.
If a highly unlikely event occurs, then we explain this by stating
that a rare event really did take
place, or that the assumption we started with was not true.
HOW TO USE THE TEST FOR DECISION MAKING
PURPOSES:
1. Formulate the null hypothesis (commonly, that the
observations are the result of pure
8. chance) and the alternative hypothesis (commonly, that the
observations show a real effect
combined with a component of chance variation).
2. Identify a test statistic that can be used to assess the truth of
the null hypothesis.
3. Compute the P-value, which is the probability that a test
statistic at least as significant as the
one observed would be obtained assuming that the null
hypothesis were true. The smaller the -
value, the stronger the evidence against the null hypothesis.
4. Compare the -value to an acceptable significance value
(sometimes called an alpha value).
If , that the observed effect is statistically significant, the null
hypothesis is ruled out, and
the alternative hypothesis is valid.
EXAMPLE OF HYPOTHESIS TESTING (TWO-TAIL TEST)
If you are told that the mean weight of 3rd graders is 85 pounds
with a standard deviation of 20
pounds, and you find that the mean weight of a group of 22
students is 95 pounds, do you
question that that group of students is a group of third graders?
• The z-score is ((x-bar) - µ)/(*sigma*/(n^.5)); the numerator is
the difference between the
observed and hypothesized mean, the denominator rescales the
unit of measurement to
standard deviation units. (95-85)/(20/(22^.5)) = 2.3452.
• The z-score 2.35 corresponds to the probability .9906, which
9. leaves .0094 in the tail
beyond. Since one could have been as far below 85, the
probability of such a large or
larger z-score is .0188. This is the p-value. Note that for these
two tailed tests we are
using the absolute value of the z-score.
• Because .0188 < .05, we reject the hypothesis (which we shall
call the null hypothesis) at
the 5% significance level; if the null hypothesis were true, we
would get such a large z-
score less than 5% of the time. Because .0188 > .01, we fail to
reject the null hypothesis
at the 1% level; if the null hypothesis were true, we would get
such a large z-score more
than 1% of the time.
DECISION TREE:
DEFINITION:
A schematic tree-shaped diagram used to determine a course of
action or show a statistical
probability. Each branch of the decision tree represents a
possible decision or occurrence. The
tree structure shows how one choice leads to the next, and the
use of branches indicates that each
option is mutually exclusive.
WHAT IT IS USED FOR:
A decision tree can be used to clarify and find an answer to a
complex problem. The structure
10. allows users to take a problem with multiple possible solutions
and display it in a simple, easy-
to-understand format that shows the relationship between
different events or decisions. The
furthest branches on the tree represent possible end results.
HOW TO APPLY IT:
1. As a starting point for the decision tree, draw a small square
around the center of the left side
of the paper. If the description is too large to fit the square, use
legends by including a number
in the tree and referencing the number to the description either
at the bottom of the page or in
another page.
2. Draw out lines (forks) to the right of the square box. Draw
one line each for each possible
solution to the issue, and describe the solution along the line.
Keep the lines as far apart as
possible to expand the tree later.
3. Illustrate the results or the outcomes of the solution at the
end of each line. If the outcome is
uncertain, draw a circle (chance node). If the outcome leads to
another issue, draw a square
(decision node). If the issue is resolved with the solution, draw
a triangle (end node). Describe
the outcome above the square or circle, or use legends, as
appropriate.
4. Repeat steps 2 through 4 for each new square at the end of
the solution lines, and so on until
there are no more squares, and all lines have either a circle or
11. blank ending.
5. The circles that represent uncertainty remain as they are. A
good practice is to assign a
probability value, or the chance of such an outcome happening.
Since it is difficult to predict at onset the number of lines and
sub-lines each solution generates,
the decision tree might require one or more redraws, owing to
paucity of space to illustrate or
represent options and/or sub-options at certain spaces.
It is a good idea to challenge and review all squares and circles
for possible overlooked solutions
before finalizing the draft.
EXAMPLE:
Your company is considering whether it should tender for two
contracts (MS1 and MS2) on offer
from a government department for the supply of certain
components. The company has three
options:
• tender for MS1 only; or
• tender for MS2 only; or
• tender for both MS1 and MS2.
If tenders are to be submitted, the company will incur additional
costs. These costs will have to
be entirely recouped from the contract price. The risk, of
course, is that if a tender is
unsuccessful, the company will have made a loss.
The cost of tendering for contract MS1 only is $50,000. The
component supply cost if the tender
12. is successful would be $18,000.
The cost of tendering for contract MS2 only is $14,000. The
component supply cost if the tender
is successful would be $12,000.
The cost of tendering for both contracts MS1 and MS2 is
$55,000. The component supply cost if
the tender is successful would be $24,000.
For each contract, possible tender prices have been determined.
In addition, subjective
assessments have been made of the probability of getting the
contract with a particular tender
price as shown below. Note here that the company can only
submit one tender and cannot, for
example, submit two tenders (at different prices) for the same
contract.
Option Possible Probability
tender of getting
prices ($) contract
MS1 only 130,000 0.20
115,000 0.85
MS2 only 70,000 0.15
65,000 0.80
60,000 0.95
MS1 and MS2 190,000 0.05
140,000 0.65
In the event that the company tenders for both MS1 and MS2 it
13. will either win both contracts (at
the price shown above) or no contract at all.
• What do you suggest the company should do and why?
• What are the downside and the upside of your suggested
course of action?
• A consultant has approached your company with an offer that
in return for $20,000 in
cash, she will ensure that if you tender $60,000 for contract
MS2, only your tender is
guaranteed to be successful. Should you accept her offer or not
and why?
Solution
The decision tree for the problem is shown below.
Below we carry out step 1 of the decision tree solution
procedure which (for this example)
involves working out the total profit for each of the paths from
14. the initial node to the terminal
node (all figures in $'000).
Step 1
• path to terminal node 12, we tender for MS1 only (cost 50), at
a price of 130, and win the
contract, so incurring component supply costs of 18, total profit
130-50-18 = 62
• path to terminal node 13, we tender for MS1 only (cost 50), at
a price of 130, and lose the
contract, total profit -50
• path to terminal node 14, we tender for MS1 only (cost 50), at
a price of 115, and win the
contract, so incurring component supply costs of 18, total profit
115-50-18 = 47
• path to terminal node 15, we tender for MS1 only (cost 50), at
a price of 115, and lose the
contract, total profit -50
• path to terminal node 16, we tender for MS2 only (cost 14), at
a price of 70, and win the
15. contract, so incurring component supply costs of 12, total profit
70-14-12 = 44
• path to terminal node 17, we tender for MS2 only (cost 14), at
a price of 70, and lose the
contract, total profit -14
• path to terminal node 18, we tender for MS2 only (cost 14), at
a price of 65, and win the
contract, so incurring component supply costs of 12, total profit
65-14-12 = 39
• path to terminal node 19, we tender for MS2 only (cost 14), at
a price of 65, and lose the
contract, total profit -14
• path to terminal node 20, we tender for MS2 only (cost 14), at
a price of 60, and win the
contract, so incurring component supply costs of 12, total profit
60-14-12 = 34
• path to terminal node 21, we tender for MS2 only (cost 14), at
a price of 60, and lose the
16. contract, total profit -14
• path to terminal node 22, we tender for MS1 and MS2 (cost
55), at a price of 190, and
win the contract, so incurring component supply costs of 24,
total profit 190-55- 24=111
• path to terminal node 23, we tender for MS1 and MS2 (cost
55), at a price of 190, and
lose the contract, total profit -55
• path to terminal node 24, we tender for MS1 and MS2 (cost
55), at a price of 140, and
win the contract, so incurring component supply costs of 24,
total profit 140-55- 24=61
• path to terminal node 25, we tender for MS1 and MS2 (cost
55), at a price of 140, and
lose the contract, total profit -55
Hence we can arrive at the table below indicating for each
branch the total profit involved in that
branch from the initial node to the terminal node.
Terminal node Total profit $'000
17. 12 62
13 -50
14 47
15 -50
16 44
17 -14
18 39
19 -14
20 34
21 -14
22 111
23 -55
24 61
25 -55
We can now carry out the second step of the decision tree
solution procedure where we work
from the right-hand side of the diagram back to the left-hand
side.
Step 2
• For chance node 5 the EMV is 0.2(62) + 0.8(-50) = -27.6
• For chance node 6 the EMV is 0.85(47) + 0.15(-50) = 32.45
18. Hence the best decision at decision node 2 is to tender at a price
of 115 (EMV=32.45).
• For chance node 7 the EMV is 0.15(44) + 0.85(-14) = -5.3
• For chance node 8 the EMV is 0.80(39) + 0.20(-14) = 28.4
• For chance node 9 the EMV is 0.95(34) + 0.05(-14) = 31.6
Hence the best decision at decision node 3 is to tender at a price
of 60 (EMV=31.6).
• For chance node 10 the EMV is 0.05(111) + 0.95(-55) = -46.7
• For chance node 11 the EMV is 0.65(61) + 0.35(-55) = 20.4
Hence the best decision at decision node 4 is to tender at a price
of 140 (EMV=20.4).
Hence at decision node 1 we have three alternatives:
• tender for MS1 only EMV=32.45
• tender for MS2 only EMV=31.6
• tender for both MS1 and MS2 EMV = 20.4
Hence the best decision is to tender for MS1 only (at a price of
19. 115) as it has the highest
expected monetary value of 32.45 ($'000).
INFLUENCE OF SAMPLE SIZE:
DEFINITION:
Sample size is one of the four interrelated features of a study
design that can influence the
detection of significant differences, relationships, or
interactions. Generally, these survey designs
try to minimize both alpha error (finding a difference that does
not actually exist in the
population) and beta error (failing to find a difference that
actually exists in the population).
WHAT IT IS USED FOR:
The sample size used in a study is determined based on the
expense of data collection and the
need to have sufficient statistical power.
HOW TO USE IT:
20. We already know that the margin of error is 1.96 times the
standard error and that the standard
error is sq.rt ^p(1�^p)/n. In general, the formula is ME = z
sq.rt ^p(1-^p)/n
where
*ME is the desired margin of error
*z is the z-score, e.g., 1.645 for a 90% confidence interval, 1.96
for a 90% confidence interval,
2.58 for a 99% confidence interval
_ ^p is our prior judgment of the correct value of p.
_ n is the sample size (to be found)
EXAMPLE:
If ^p =0.3 and Z=1.96 and ME =0.025 then the necessary sample
size is:
ME= Z sq.rt (^p*1-^p)/n
0.025 = 1:96 sq.rt (0.3*0.7)/n
21. n=1291 or 1300 students
POPULATION MEAN:
DEFINITION:
The population mean is the mean of a numerical set that
includes all the numbers within the
entire group.
WHAT IT IS USED FOR:
In most cases, the population mean is unknown and the sample
mean is used for validation
purposes. However, if we want to calculate the population
mean, we will have to construct the
confidence interval. This can be achieved by the following
steps:
HOW TO USE IT:
• The sample statistic is the sample mean x¯
• The standard error of the mean is s/sq.rt n where s is the
22. standard deviation of individual
data values.
• The multiplier, denoted by t*, is found using the t-table in the
appendix of the book. It's a
simple table. There are columns for .90, .95, .98, and .99
confidence. Use the row for df =
n − 1.
• Thus the formula for a confidence interval for the mean is
x¯±t∗ (s/sq.rt n)
EXAMPLE:
In a class survey, students are asked if they are sleep deprived
or not and also are asked how
much they sleep per night. Summary statistics for the n = 22
students who said they are sleep
deprived are:
• Thus n = 22, x¯ = 5.77, s = 1.572, and standard error of the
mean = 1.572/sq.rt 22=0.335
23. • A confidence interval for the mean amount of sleep per night
is 5.77 ± t* (0.335) for the
population that feels sleep deprived.
• Go to the t-table in the appendix of the book and use the df =
22 – 1 = 21 row. For 95%
confidence the value of t* = 2.08.
• A 95% confidence interval for µ is 5.77 ± (2.08) (0.335),
which is 5.77 ± 0.70, or 5.07 to
6.7
• Interpretation: With 95% confidence we estimate the
population mean to be between
5.07 and 6.47 hours per night.
RANDOM SAMPLING
Random sampling is a sampling technique where we select a
group of subjects (a sample) for
study from a larger group (a population). Each individual is
24. chosen entirely by chance and each
member of the population has a known, but possibly non-equal,
chance of being included in the
sample.
By using random sampling, the likelihood of bias is reduced.
WHEN RANDOM SAMPLING IS USED:
Random sampling is used when the researcher knows little
about the population.
THE STEPS ASSOCIATED WITH RANDOM SAMPLING:
1. Define the population
2. Choose your sample size
3. List the population
4. Assign numbers to the units
5. Find random numbers
6. Select your sample
EXAMPLE:
25. In a study, 10,000 students will be invited to take part in the
research study. The selection was
limited to 200 randomly selected students. In this case, this
would mean selecting 200 random
numbers from the random number table. Imagine the first three
numbers from the random
number table were:
0011 (the 11th student from the numbered list of 10,000
students)
9292 (the 9,292nd student from the list)
2001 (the 2,001st student from the list)
We would select the 11th, 9,292nd, and 2,001st students from
our list to be part of the sample. We
keep doing this until we have all 200 students that we want in
our sample.
SAMPLING DISTRIBUTION:
26. DEFINITION:
The sampling distribution is a theoretical distribution of a
sample statistic. There is a different
sampling distribution for each sample statistic. Each sampling
distribution is characterized by
parameters, two of which are and . The latter is called the
standard error.
WHAT IT IS USED FOR:
It is used for making probability statements in inferential
statistics.
HOW IS SAMPLING DISTRIBUTION USED?
Step 1: Obtain a simple random sample of size n.
Step 2: Compute the sample mean.
Step 3: Assuming we are sampling from a finite population,
repeat Steps 1 and 2 until all simple
random samples of size n have been obtained.
EXAMPLE OF SAMPLING DISTRIBUTION:
27. THE SAMPLE DISTRIBUTION
The sample distribution is the distribution resulting from the
collection of actual data. A major
characteristic of a sample is that it contains a finite (countable)
number of scores, the number of
scores represented by the letter N. For example, suppose that
the following data were collected:
32 35 42 33 36 38 37 33 38 36 35 34 37 40 38 36 35 31 37 36
33
36 39 40 33 30 35 37 39 32 39 37 35 36 39 33 31 40 37 34 34
37
These numbers constitute a sample distribution. Using the
procedures discussed in the chapter on
frequency distributions, the following relative frequency
polygon can be constructed to picture
this data:
SAMPLING ERROR:
28. DEFINITION:
The error that arises as a result of taking a sample from a
population rather than using the whole
population.
WHAT IT IS USED FOR:
It is used to detect the difference between the sample and the
true, but unknown value of
population parameter.
HOW TO USE IT/CALCULATE IT:
• Determine the level of confidence followed by the critical
values
• Calculate the sample standard deviation
• Calculate the margin of error using
E = Critical value * sample standard deviation/sq.rt of sample
size
EXAMPLE:
29. 1. What is the margin of error for a simple random sample of
900 people at a 95% level of
confidence? The sample standard deviation is 2.
By use of the table we have a critical value of 1.96, and so the
margin of error is 1.96/(2
√ 900 = 0.03267, or about 3.3%.
2. What is the margin of error for a simple random sample of
1600 people at a 95% level of
confidence and a sample standard deviation of 2?
At the same level of confidence as the first example, increasing
the sample size to 1600
gives us a margin of error of 0.0245, or about 2.5%.
This shows that by increasing the sample size, the margin of
error decreases.
PROBABILITY:
DEFINITION:
30. Probability is the chance that something will happen — how
likely it is that some event will
occur.
WHAT IT IS USED FOR:
Probability is used in various areas, such as assessing risks in
medical treatment, forecasting
weather, what to sell at a discount and when to sell it,
determining car insurance rates,
determining future commercial and manufacturing construction,
in developing other real estate,
in municipal planning for such things as placing new roads, and
in financial planning at home
and in the business world.
HOW TO CALCUATE IT:
1. Count the number of all distinctive and equally likely
outcomes of the experiment. Let that be
n.
2. Count the number of distinctive outcomes that represent the
occurrence of the event in
question. Let that be ne.
31. 3. Calculate the result of the division ne/n. That is the
probability of the event.
EXAMPLE:
Find the probability of getting an even number after rolling a
die.
• Event: Getting an even number
• Steps above:
• Distinctive outcomes: 1, 2, 3, 4, 5, 6 are all the outcomes,
their count n=6
• Outcomes representing the event: 2, 4, 6 are all the even
numbers you can
get, their count ne=3
• Probability: P = ne/n = 3/6 = 0.5 or 1/2
32. POWER CURVE:
DEFINITION:
Power curves illustrate the effect on power of varying the
alternate hypothesis.
WHAT IT IS USED FOR:
The curve illustrates how a sample of observations with a
defined variance is quite powerful in
correctly rejecting the null hypothesis (for example, if m0=8)
when the true mean is less than 6 or
greater than 10. The curve also illustrates that the test is not
powerful — it may not reject the null
hypothesis even when the true mean differs from m0 — when
the difference is small. This is also
extensively used in testing the relationship between power and
sample size.
HOW IT IS USED:
See example below.
EXAMPLE:
33. If the researcher learns from literature that the population
follows a normal distribution with
mean of 100 and variance of 100 under the null hypothesis and
he/she expects the mean to be
greater than 105 or less than 95 under the null hypothesis and
he/she wants the test to be
significant at 95% level, the resulting power function would be:
Power=1-Φ[1.96-(105-100)/(10/n)]+Φ[-1.96-(95-100)/(10/n)],
which is,
Power=1-Φ[1.96-n/2]+Φ[-1.96+n/2].
That function shows a relationship between power and sample
size. For each level of
sample size, there is a corresponding sample size. For example,
if n=20, the corresponding power
level would be about 0.97, or, if the power level is 0.95, the
corresponding sample size would be
16.
PROBABILITY DISTRIBUTION:
DEFINITION:
A statistical function that describes all the possible values and
34. likelihoods that a random variable
can take within a given range. At times it is presented in the
form of a table or an equation that
links the outcome of a statistical experiment with its probability
of occurrences.
HOW IT IS USED:
It establishes a range that will be between the minimum and
maximum statistically possible
values, but where the possible values are likely to be plotted on
the probability distribution
depends on a number of factors, including the distribution
mean, standard deviation, skewness,
and kurtosis.
HOW TO USE IT:
• Identify the event
• Create a table showing the possibility of its occurrence
35. EXAMPLE:
An example will make clear the relationship between random
variables and probability
distributions. Suppose you flip a coin two times. This simple
statistical experiment can have four
possible outcomes: HH, HT, TH, and TT. Now, let the variable
X represent the number of Heads
that result from this experiment. The variable X can take on the
values 0, 1, or 2. In this example,
X is a random variable because its value is determined by the
outcome of a statistical
experiment.
A probability distribution is a table or an equation that links
each outcome of a statistical
experiment with its probability of occurrence. Consider the coin
flip experiment described above.
The table below, which associates each outcome with its
probability, is an example of a
probability distribution.
Number of heads Probability
36. 0 0.25
1 0.50
2 0.25
The above table represents the probability distribution of the
random variable X.
EXPECTED VALUE OF SAMPLE INFORMATION:
DEFINITION:
In decision theory, the expected value of sample information is
the expected increase in utility
that you could obtain from gaining access to a sample of
additional observations before making a
decision.
WHAT IS IT USED FOR?
Calculate the Expected Monetary Value (EMV) of each
alternative action.
37. HOW TO USE IT/CALCULATE IT?
1. Determine the optimal decision and its expected return for
the possible outcomes of the
sample using the posterior probabilities for the states of nature
2. Calculate the values of the optimal returns
3. Subtract the EV of the optimal decision obtained without
using the sample information
from the amount determined in step (2)
EXAMPLE:
The expected value of sample information is computed as
follows:
Suppose you were going to make an investment into only one of
three investment vehicles:
stock, mutual fund, or certificate of deposit (CD). Further
suppose that the market has a 50%
chance of increasing, a 30% chance of staying even, and a 20%
chance of decreasing. If the
38. market increases, the stock investment will earn $1500 and the
mutual fund will earn $900. If the
market stays even, the stock investment will earn $300 and the
mutual fund will earn $600. If the
market decreases, the stock investment will lose $800 and the
mutual fund will lose $200. The
certificate of deposit will earn $500 independent of the market's
fluctuation.
Question:
What is the expected value of perfect information?