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Reasoning with Data Chapter 2 © 2019 McGraw-Hill Education. All rights reserved. Authorized only for instructor use in the classroom. No reproduction or distribution without the prior written consent of McGraw-Hill Education. Learning Objectives Define reasoning. Execute deductive reasoning. Explain an empirically testable conclusion. Execute inductive reasoning. Differentiate between deductive and inductive reasoning. Explain how inductive reasoning can be used to evaluate an assumption. Describe selection bias in inductive reasoning. ‹#› ‹#› © 2019 McGraw-Hill Education. What is Reasoning? Reasoning is the process of forming conclusions, judgments, or inferences from facts or data Reasoning and logic are often used interchangeably Logic is a description of the rules and/or steps behind the reasoning process ‹#› ‹#› © 2019 McGraw-Hill Education. Two Arguments Argument 1: The companies profits are up more than 10% over the past year. An increase in profits of 10% is the result of excellent management. You were the manager over the past year. Therefore, I conclude that you engaged in excellent management last year. Argument 2: Ten of your 300 employees came to me with complaints about your management. They indicated that you treated them unfairly by not giving them a raise they deserved. Therefore, I conclude that all of your employees are disgruntled with your management. ‹#› ‹#› © 2019 McGraw-Hill Education. Understanding Reasoning In presenting the two arguments, the goal is not to make a definitive decision about which you believe (if either) The goal is to think about and distinguish different “lines†of reasoning In distinguishing between the different types of reasoning, you will be able to establish why you believe or question the claims made in the two arguments ‹#› ‹#› © 2019 McGraw-Hill Education. Two Major Types of Reasoning Reasoning Deductive Reasoning Inductive Reasoning Both play an important role in interpreting and drawing conclusions from data analysis ‹#› ‹#› © 2019 McGraw-Hill Education. Deductive Reasoning Deductive Reasoning Goes from the general to the specific Also known as top-down logic Seeks to prove statements of the form “If A, then B†‹#› ‹#› © 2019 McGraw-Hill Education. Deductive Reasoning Such reasoning always implies three underlying components: assumptions (“If Aâ€), methods of proof (“thenâ€), and conclusions (“Bâ€) ‹#› ‹#› © 2019 McGraw-Hill Education. Deductive Reasoning The purest applications of deductive reasoning are in the field of mathematics Two of the most used approaches are direct proofs and transposition Direct proofs Proof that begins with assumptions, explains methods of proof, and states the conclusion(s) Transposition Any time a group of assumptions implies a conclusion, then it is also true that any time the conclusion does not hold, at least one of the assumptions must not hold ‹#› ‹#› © 2019 McGraw-.
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Webinar: Using Behavioral Economics to Identify What Motivates Shopper Behavior
Behavioral economics, the intersection of psychology and economics, is broadly gaining ground in the business world. Shoppers often make irrational decisions based on messaging brands can control.
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The webinar is included as the last slide in this presentation (also at https://youtu.be/35CbJRUz1Cs ). Watch the webinar and reference the slides to learn how marketers can leverage behavioral economics to identify triggers that drive shopper behavior along the path to purchase. You will:
- Learn what behavioral economics is and why it is important.
- Understand how behavioral economics is used in the business world.
- Walk through an actual case study on how brands can use behavioral economics to influence a shopper's path to purchase across audiences and channels.
Visit http://revtrax.com/insights to learn how RevTrax can help you combine customer-level data and behavioral economics methodology to test, optimize, and measure your digital promotions and marketing investments.
250 words, no more than 500· Focus on what you learned that made.docx
250 words, no more than 500
· Focus on what you learned that made an impression, what may have surprised you, and what you found particularly beneficial and why. Specifically:
· What did you find that was really useful, or that challenged your thinking?
· What are you still mulling over?
· Was there anything that you may take back to your classroom?
· Is there anything you would like to have clarified?
Your Weekly Reflection will be graded on the following criteria for a total of 5 points:
· Reflection is written in a clear and concise manner, making meaningful connections to the investigations & objectives of the week.
· Reflection demonstrates the ability to push beyond the scope of the course, connecting to prior learning or experiences, questioning personal preconceptions or assumptions, and/or defining new modes of thinking.
BELOW ARE LESSON COVERED
· This week's investigations introduce and explore one of the most common distributions (one you may be familiar with): the Normal Distribution. In our explorations of the distribution and its associated curve, we will revisit the question of "What is typical?" and look at the likelihood (probability) that certain observations would occur in a given population with a variable that is normally distributed. We will apply our work with Normal Distributions to briefly explore some big concepts of inferential statistics, including the Central Limit Theorem and Hypothesis Testing. There are a lot of new ideas in this week’s work. This week is more exploratory in nature.
Goals:
· Explore the Empirical Rule
· Become familiar with the normal curve as a mathematical model, its applications and limitations
· Calculate z-scores & explain what they mean
· Use technology to calculate normal probabilities
· Determine the statistical significance of an observed difference in two means
· Use technology to perform a hypothesis test comparing means (z-test) and interpret its meaning
· Use technology to perform a hypothesis test comparing means (t-test) (optional)
· Gather data for Comparative Study Final Project.
·
DoW #5: The SAT & The ACT
Two Common Tests for college admission are the SAT (Scholastic Aptitude Test) and the ACT (American College Test). The scores for these tests are scaled so that they follow a normal distribution.
· The SAT reported that its scores were normally distributed with a mean μ=896 and a standard deviation σ=174
· The ACT reported that its scores were normally distributed with a mean μ=20.6 and a standard deviation σ=5.2.
We have two questions to consider for this week’s DoW:
2. A high school student Bobby takes both of these tests. On the SAT, he achieves a score of 1080. On the ACT, he achieves a score of 30. He cannot decide which score is the better one to send with his college applications.
. Question: Which test score is the stronger score to send to his colleges?
· A hypothetical group called SAT Prep claims that students who take their SAT Preparatory course score higher o.
Hypothesis Testing: Central Tendency – Non-Normal (Compare 2+ Factors)
An extension on hypothesis testing, this lesson reviews the Mood’s Median & Kruskal-Wallis tests as central tendency measurements for non-normal distributions.
Data AnalysisResearch Report AssessmentBSB
Data Analysis
Research Report Assessment
BSB123 Data Analysis
BSB123 Data Analysis
Notes on the Assessment
Covers Topics 1 – 10 i.e. descriptive statistics to Multiple Regression
Assignment is based around the international student recruitment industry looking specifically at students interested in postgraduate studies in USA
All 500 observations on spreadsheet are for international students
Variables are all related to factors which affect chance of being admitted and your job is to analyse this so that the company (GES) can advise future students about what to do and what their chances are of being admitted.
Report is split so that in each section you look at different aspects
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Marks reflect (generally) the amount of work you need to do.
BSB123 Data Analysis
BSB123 Data Analysis
BSB123 Data Analysis
BSB123 Data Analysis
What am I looking for?
Can you select the correct technique / analysis to solve the question
Is that technique correctly and FULLY applied with calculations done correctly
E.g. in a hypothesis test, did you:
Correctly identify the test statistic (Z, T, F, χ2)
Did you include accurate hypotheses and decision rule which are consistent with each other
Were the calculations correct
Did you check to see if the assumptions or conditions of the test held
OR for Descriptive Statistics did you:
Consider all aspects of how you describe data and use the appropriate statistics to do that
Choose correct graph(s) for the type of data
Summarise the results to actually describe what you found – not just quote the stats.
Can you interpret the results – not just make a decision or complete a calculation.
Can you express the result in terms of the question and in a way which is understandable to your audience
In other words you will not get full marks unless you can correctly select the right approach to take for the data given, accurately and fully apply that analysis in a way which logically leads to a conclusion, make the conclusion in terms of the problem presented and then communicate that solution concisely and clearly
BSB123 Data Analysis
BSB123 Data Analysis
Examples from THA 4
H0: ≤ 700
H1: > 700
What is wrong with this?
BSB123 Data Analysis
BSB123 Data Analysis
Include title of analysis – t-Test: Two Sample Assuming Unequal Variances
5
Examples from THA 4
BSB123 Data Analysis
BSB123 Data Analysis
Look at t stat – all wrong – copied from somewhere – multiple students all getting it wrong
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Used population terminology not sample
P-value – what is it?
6
Hypothesis Test
State the Hypotheses in terms of the parameter (µ,σ,p)
Identify the correct probability distribution (t, z, F, χ2)
Identify level of significance
State decision rule clearly
Use either test statistic method (i.e. in terms of t or z etc) or in terms of p-value. Don’t need to do both.
Decision rule must be consistent wit ...
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Ashford 2 - Week 1 - Instructor GuidanceWeek OverviewThe f.docx
Ashford 2: - Week 1 - Instructor Guidance
Week Overview:
The following video series: Against All Odds Inside Statistics is helpful if you would like to watch it.
http://www.learner.org/resources/series65.html?pop=yes&pid=3138
For this week, we’ll learn that statistics is the science of collecting, organizing, presenting, analyzing, and interpreting numerical data to assist in making more effective decisions.
In today’s world, numerical information is everywhere. Statistical techniques are used to make decisions that affect our daily lives. The knowledge of statistical methods will help you understand how decisions are made and give you a better understanding of how they affect you. No matter what line of work you select, you will find yourself faced with decisions where an understanding of data analysis is helpful.
The concepts introduced this week include levels of measurement, measurements of center, variations, etc. Normal distribution and calculations are introduced in this week.
Measurements
You should be able to distinguish among the nominal, ordinal, interval, and ratio levels of measurement.
Nominal level - data that is classified into categories and cannot be arranged in any particular order.
EXAMPLES: eye color, gender, religious affiliation.
Ordinal level – data arranged in some order, but the differences between data values cannot be determined or are meaningless.
EXAMPLE: During a taste test of 4 soft drinks, Mellow Yellow was ranked number 1, Sprite number 2, Seven-up number 3, and Orange Crush number 4.
Interval level - similar to the ordinal level, with the additional property that meaningful amounts of differences between data values can be determined. There is no natural zero point.
EXAMPLE: Temperature on the Fahrenheit scale.
Ratio level - the interval level with an inherent zero starting point. Differences and ratios are meaningful for this level of measurement.
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Probability
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There are three ways of assigning probability:
1. Classical Probability
This is based on the assumption that the outcomes of an experiment are equally likely.
2. Empirical Probability
The probability of an event happening is the fraction of the time similar events happened in the past.
Example: On February 1, 2003, the Space Shuttle Columbia exploded. This was the second disaster in 113 space missions for NASA. On the basis of this information, what is the probability that a future mission is successfully completed?
Probability of successful flight ...
Statistics
This is the basic introduction of statistics, you can use it for teaching statistics. This slide will be able to help to understand the basics of statistics to the students with no coding background
Recommended
Webinar: Using Behavioral Economics to Identify What Motivates Shopper Behavior
Behavioral economics, the intersection of psychology and economics, is broadly gaining ground in the business world. Shoppers often make irrational decisions based on messaging brands can control.
Neil Gandhi, VP of Data Science at RevTrax, and Jura Liaukonyte, SM.A., Ph.D, Professor at Cornell University, recently discussed proven behavioral economic methodologies and how they can be applied to digital tests to uncover the most effective shopper motivators. These learnings can be tied to specific audience segments and applied at scale to increase shopper engagement online and in-store, making national, shopper and trade promotions more impactful and cost-effective.
The webinar is included as the last slide in this presentation (also at https://youtu.be/35CbJRUz1Cs ). Watch the webinar and reference the slides to learn how marketers can leverage behavioral economics to identify triggers that drive shopper behavior along the path to purchase. You will:
- Learn what behavioral economics is and why it is important.
- Understand how behavioral economics is used in the business world.
- Walk through an actual case study on how brands can use behavioral economics to influence a shopper's path to purchase across audiences and channels.
Visit http://revtrax.com/insights to learn how RevTrax can help you combine customer-level data and behavioral economics methodology to test, optimize, and measure your digital promotions and marketing investments.
250 words, no more than 500· Focus on what you learned that made.docx
250 words, no more than 500
· Focus on what you learned that made an impression, what may have surprised you, and what you found particularly beneficial and why. Specifically:
· What did you find that was really useful, or that challenged your thinking?
· What are you still mulling over?
· Was there anything that you may take back to your classroom?
· Is there anything you would like to have clarified?
Your Weekly Reflection will be graded on the following criteria for a total of 5 points:
· Reflection is written in a clear and concise manner, making meaningful connections to the investigations & objectives of the week.
· Reflection demonstrates the ability to push beyond the scope of the course, connecting to prior learning or experiences, questioning personal preconceptions or assumptions, and/or defining new modes of thinking.
BELOW ARE LESSON COVERED
· This week's investigations introduce and explore one of the most common distributions (one you may be familiar with): the Normal Distribution. In our explorations of the distribution and its associated curve, we will revisit the question of "What is typical?" and look at the likelihood (probability) that certain observations would occur in a given population with a variable that is normally distributed. We will apply our work with Normal Distributions to briefly explore some big concepts of inferential statistics, including the Central Limit Theorem and Hypothesis Testing. There are a lot of new ideas in this week’s work. This week is more exploratory in nature.
Goals:
· Explore the Empirical Rule
· Become familiar with the normal curve as a mathematical model, its applications and limitations
· Calculate z-scores & explain what they mean
· Use technology to calculate normal probabilities
· Determine the statistical significance of an observed difference in two means
· Use technology to perform a hypothesis test comparing means (z-test) and interpret its meaning
· Use technology to perform a hypothesis test comparing means (t-test) (optional)
· Gather data for Comparative Study Final Project.
·
DoW #5: The SAT & The ACT
Two Common Tests for college admission are the SAT (Scholastic Aptitude Test) and the ACT (American College Test). The scores for these tests are scaled so that they follow a normal distribution.
· The SAT reported that its scores were normally distributed with a mean μ=896 and a standard deviation σ=174
· The ACT reported that its scores were normally distributed with a mean μ=20.6 and a standard deviation σ=5.2.
We have two questions to consider for this week’s DoW:
2. A high school student Bobby takes both of these tests. On the SAT, he achieves a score of 1080. On the ACT, he achieves a score of 30. He cannot decide which score is the better one to send with his college applications.
. Question: Which test score is the stronger score to send to his colleges?
· A hypothetical group called SAT Prep claims that students who take their SAT Preparatory course score higher o.
Hypothesis Testing: Central Tendency – Non-Normal (Compare 2+ Factors)
An extension on hypothesis testing, this lesson reviews the Mood’s Median & Kruskal-Wallis tests as central tendency measurements for non-normal distributions.
Data AnalysisResearch Report AssessmentBSB
Data Analysis
Research Report Assessment
BSB123 Data Analysis
BSB123 Data Analysis
Notes on the Assessment
Covers Topics 1 – 10 i.e. descriptive statistics to Multiple Regression
Assignment is based around the international student recruitment industry looking specifically at students interested in postgraduate studies in USA
All 500 observations on spreadsheet are for international students
Variables are all related to factors which affect chance of being admitted and your job is to analyse this so that the company (GES) can advise future students about what to do and what their chances are of being admitted.
Report is split so that in each section you look at different aspects
You will need to do a summary incorporating elements of all of the parts to make recommendations.
Marks reflect (generally) the amount of work you need to do.
BSB123 Data Analysis
BSB123 Data Analysis
BSB123 Data Analysis
BSB123 Data Analysis
What am I looking for?
Can you select the correct technique / analysis to solve the question
Is that technique correctly and FULLY applied with calculations done correctly
E.g. in a hypothesis test, did you:
Correctly identify the test statistic (Z, T, F, χ2)
Did you include accurate hypotheses and decision rule which are consistent with each other
Were the calculations correct
Did you check to see if the assumptions or conditions of the test held
OR for Descriptive Statistics did you:
Consider all aspects of how you describe data and use the appropriate statistics to do that
Choose correct graph(s) for the type of data
Summarise the results to actually describe what you found – not just quote the stats.
Can you interpret the results – not just make a decision or complete a calculation.
Can you express the result in terms of the question and in a way which is understandable to your audience
In other words you will not get full marks unless you can correctly select the right approach to take for the data given, accurately and fully apply that analysis in a way which logically leads to a conclusion, make the conclusion in terms of the problem presented and then communicate that solution concisely and clearly
BSB123 Data Analysis
BSB123 Data Analysis
Examples from THA 4
H0: ≤ 700
H1: > 700
What is wrong with this?
BSB123 Data Analysis
BSB123 Data Analysis
Include title of analysis – t-Test: Two Sample Assuming Unequal Variances
5
Examples from THA 4
BSB123 Data Analysis
BSB123 Data Analysis
Look at t stat – all wrong – copied from somewhere – multiple students all getting it wrong
P and t test – do one
Used population terminology not sample
P-value – what is it?
6
Hypothesis Test
State the Hypotheses in terms of the parameter (µ,σ,p)
Identify the correct probability distribution (t, z, F, χ2)
Identify level of significance
State decision rule clearly
Use either test statistic method (i.e. in terms of t or z etc) or in terms of p-value. Don’t need to do both.
Decision rule must be consistent wit ...
Business Optimization via Causal Inference
Most data scientists are focused on predictive (aka supervised) models, yet real growth depends on the estimation of effect of an action and optimizations of action policies. To this end, I will present causal inference and related packages.
There are three layers of analytics: descriptive (BI), predictive (supervised modeling), and prescriptive. The latter, less-known one, focus on answering the most important business questions. For example, "what was the effect of giving a discount?" or "who to call first?" - In this talk, we will first discuss what frameworks are used to answer these questions, namely causal inference, and reinforcement learning. Then we will deep dive into CI and why is it important. Last but not least we will present some code.
Ashford 2 - Week 1 - Instructor GuidanceWeek OverviewThe f.docx
Ashford 2: - Week 1 - Instructor Guidance
Week Overview:
The following video series: Against All Odds Inside Statistics is helpful if you would like to watch it.
http://www.learner.org/resources/series65.html?pop=yes&pid=3138
For this week, we’ll learn that statistics is the science of collecting, organizing, presenting, analyzing, and interpreting numerical data to assist in making more effective decisions.
In today’s world, numerical information is everywhere. Statistical techniques are used to make decisions that affect our daily lives. The knowledge of statistical methods will help you understand how decisions are made and give you a better understanding of how they affect you. No matter what line of work you select, you will find yourself faced with decisions where an understanding of data analysis is helpful.
The concepts introduced this week include levels of measurement, measurements of center, variations, etc. Normal distribution and calculations are introduced in this week.
Measurements
You should be able to distinguish among the nominal, ordinal, interval, and ratio levels of measurement.
Nominal level - data that is classified into categories and cannot be arranged in any particular order.
EXAMPLES: eye color, gender, religious affiliation.
Ordinal level – data arranged in some order, but the differences between data values cannot be determined or are meaningless.
EXAMPLE: During a taste test of 4 soft drinks, Mellow Yellow was ranked number 1, Sprite number 2, Seven-up number 3, and Orange Crush number 4.
Interval level - similar to the ordinal level, with the additional property that meaningful amounts of differences between data values can be determined. There is no natural zero point.
EXAMPLE: Temperature on the Fahrenheit scale.
Ratio level - the interval level with an inherent zero starting point. Differences and ratios are meaningful for this level of measurement.
EXAMPLES: Monthly income of surgeons, or distance traveled by manufacturer’s representatives per month.
Why do you need to know the level of measurement of a data? This is because the level of measurement of the data dictates the calculations that can be done to summarize and present the data. It also determines the statistical tests that should be performed on the data.
Probability
PROBABILITY is a value between zero and one, inclusive, describing the relative possibility (chance or likelihood) an event will occur.
There are three ways of assigning probability:
1. Classical Probability
This is based on the assumption that the outcomes of an experiment are equally likely.
2. Empirical Probability
The probability of an event happening is the fraction of the time similar events happened in the past.
Example: On February 1, 2003, the Space Shuttle Columbia exploded. This was the second disaster in 113 space missions for NASA. On the basis of this information, what is the probability that a future mission is successfully completed?
Probability of successful flight ...
Statistics
This is the basic introduction of statistics, you can use it for teaching statistics. This slide will be able to help to understand the basics of statistics to the students with no coding background
The Roles of Data and Predictive Analytics in BusinessChapter .docx
The Roles of Data and Predictive Analytics in Business
Chapter 1
© 2019 McGraw-Hill Education. All rights reserved. Authorized only for instructor use in the classroom. No reproduction or distribution without the prior written consent of McGraw-Hill Education.
Learning Objectives
Explain how predictive analytics can help in business strategy formulation.
Distinguish structured from unstructured data.
Differentiate units of observation.
Outline a data-generating process.
Describe the primary ways that data analysis is used to aid business performance.
Discriminate between lead and lag information.
Discriminate between active and passive prediction.
Recognize questions pertaining to business strategy that may utilize (active) predictive analytics.
‹#›
© 2019 McGraw-Hill Education.
Defining Data & Data Uses in Business
Data
A collection of information
Database
Organized collection of data that firms use for analysis
Business analytics
The use of data analysis to aid in business decision making
Predictive analytics
The use of data analysis to designed to form predictions about future, unknown, events or outcomes
‹#›
© 2019 McGraw-Hill Education.
Business Strategy
Plan of action designed by a business practitioner to achieve a business objective
Business objectives include profit maximization, enhanced employee satisfaction, etc.
Examples of action include pricing decisions, advertising campaigns, and methods of employee compensation
‹#›
© 2019 McGraw-Hill Education.
Predictive Analytics for Business Strategy
With no data, strong theoretical model is often not enough to predict effective business strategies
Sound theoretical arguments coupled with data becomes a strong tool to predict effective business strategies
Predictive analytics is an ideal complement to create a successful business strategy
‹#›
© 2019 McGraw-Hill Education.
Data Features
Structured Data
Data with well-defined units of observation which can be classified and structured in the form of a spreadsheet.
An example:
‹#›
© 2019 McGraw-Hill Education.
Data Features
Unstructured Data
Any data that cannot be classified and structured.
An example:
‹#›
© 2019 McGraw-Hill Education.
The Unit of Observation
The entity for which information has been collected
Crucial component of structured data
Tells us the way the in which the information in a dataset varies
Answers the questions: What, Where, Who, When?
Four main groupings: cross-sectional data, pooled cross-sectional data, time-series data, and panel data
‹#›
© 2019 McGraw-Hill Education.
Data Types
Cross-Sectional Data
Data that provide a snapshot of information at one fixed point in time.
An example:
‹#›
© 2019 McGraw-Hill Education.
Data Typ.
Hypothesis TestingThe Right HypothesisIn business, or an.docx
Hypothesis Testing
The Right Hypothesis
In business, or any other discipline, once the question has been asked there must be a statement as to what will or will not occur through testing, measurement, and investigation. This process is known as formulating the right hypothesis. Broadly defined a hypothesis is a statement that the conditions under which something is being measured or evaluated holds true or does not hold true. Further, a business hypothesis is an assumption that is to be tested through market research, data mining, experimental designs, quantitative, and qualitative research endeavors. A hypothesis gives the businessperson a path to follow and specific things to look for along the road.
If the research and statistical data analysis supports and proves the hypothesis that becomes a project well done. If, however, the research data proved a modified version of the hypothesis then re-evaluation for continuation must take place. However, if the research data disproves the hypothesis then the project is usually abandoned.
Hypotheses come in two forms: the null hypothesis and the alternate hypothesis. As a student of applied business statistics you can pick up any number of business statistics textbooks and find a number of opinions on which type of hypothesis should be used in the business world. For the most part, however, and the safest, the better hypothesis to formulate on the basis of the research question asked is what is called the null hypothesis. A null hypothesis states that the research measurement data gathered will not support a difference, relationship, or effect between or amongst those variables being investigated. To the seasoned research investigator having to accept a statement that no differences, relationships, and/or effects will occur based on a statistical data analysis is because when nothing takes place or no differences, effects, or relationship are found there is no possible reason that can be given as to why. This is where most business managers get into trouble when attempting to offer an explanation as to why something has not happened. Attempting to provide an answer to why something has not taken place is akin to discussing how many angels can be placed on the head of a pin—everyone’s answer is plausible and possible. As such business managers need to accept that which has happened and not that which has not happened.
Many business people will skirt the null hypothesis issue by attempting to set analternative hypothesis that states differences, effects and relationships will occur between and amongst that which is being investigated if certain conditions apply.Unfortunately, however, this reverse position is as bad. The research investigator might well be safe if the data analysis detects differences, effect or relationships, but what if it does not? In that case the business manager is back to square one in attempting to explain what has not happened. Although the hypothesis situation may seem c.
Coefficient alpha& exampleCoefficient alpha checks the internal .docx
Coefficient alpha& example
Coefficient alpha checks the internal consistency of measurements with dichotomous choices. It is equivalent to performing the split half methodology on all combinations of questions and is applicable when each question is either right or wrong. A correct question scores 1 and an incorrect question scores 0. The test statistic (Coefficient alpha) is:
where
k = number of questions
pj = number of people in the sample who answered question j correctly
qj = number of people in the sample who didn’t answer question j correctly
σ2 = variance of the total scores of all the people taking the test = VAR.S(R1) where R1 = array containing the total scores of all the people taking the test.
Values range from 0 to 1. A high value indicates reliability, while too high a value (in excess of .90) indicates a homogeneous test.
Example 1: A questionnaire with 11 questions is administered to 12 students. The results are listed in the upper portion of Figure 1. Determine the reliability of the questionnaire using Coefficient alpha
Summary :
The value Coefficient alpha (α)= 0.768 shows that the test has a good reliability.
note:
- scales with (α) between 0.80 - 0.95 are considered to have very good reliability.
- scales with (α) between 0.70 - 0.80 are considered to have good reliability.
- scales with (α) between 0.60 - 0.70 indicates fair reliability.
- if the (α) is below 0.60, the scales has poor reliability.
Figure 1Q1Q2Q3Q4Q5Q6Q7Q8Q9Q10Q11TOTAL1111111111111121111111101093101111111008411101101100751111100010066011011110006711110010000581111100000059010110000104101001010000031111100000000312100100000002TOTAL10999865543169p= percentage of students who asnwsweredthat question correctely 0.8333333333
Hanan: or (# of people aswered correctely / total people
= 10/12=0.8333330.750.750.750.66666666670.50.41666666670.41666666670.33333333330.250.0833333333q= are the percentage of students who answered that question incorrectly. q= 1-p0.1666666667
Hanan:
or (# of people aswered incorrectely / total people
=2/12= 0.1666670.250.250.250.33333333330.50.58333333330.58333333330.66666666670.750.9166666667pq = p*q0.13888888890.18750.18750.18750.22222222220.250.24305555560.24305555560.22222222220.18750.07638888892.1458333333k
Hanan:
number of questions11∑pq2.1458333333var ( ơ2)
Hanan:
see VAR worksheet for more explenation.7.1136363636 coefficient aplha (α)
Hanan:
=k/(k-I)*(1-∑pq/var)0.7681842386The value Coefficient alpha (α)= 0.768 shows that the test has a good reliability.
VARXX =(69/12)X-X(X-X)2115.2527.562593.2510.562582.255.062571.251.562560.250.0625̅̅̅60.250.06255-0.750.56255-0.750.56254-1.753.06253-2.757.56253-2.757.56252-3.7514.0625695.75078.25VAR= ơ2
Hanan:
= ∑(x-̅x)2/(n-1)
= 78.25/(12-1)
= 78.25/11
7.1136363636
3
©2013 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
Theory Building
Business Research Met.
!JWI 531 Financial Management II Week Four Lec.docx
!
JWI 531
Financial Management II
Week Four | Lecture Two
!
!
Please note that this basic version of the lecture is provided as a convenience for the student, and may be
missing interactive materials throughout. Students are still responsible for reviewing the missing
materials - including audio, video, and interactive widgets - that are found in the full lecture.
- Page
-1
ADDITIONAL VALUATION
TECHNIQUES: SENSITIVITY ANALYSIS
AND DECISION TREES
!
In the digital age, businesses are deluged with data. Sophisticated
tools are abundant. Until recently, however, the financial world’s
wizardry seemed invincible. Recent events have significantly
changed that perception.
But a few complex techniques still remain unblemished. Sensitivity
analysis and decision trees, in particular, can help you manage
uncertainty about the future. And businesses today have learned to
live with a high degree of uncertainty.
The assets companies own will eventually reveal their full,
productive capacity. The key word is eventually. You won’t know just
how valuable an entity or a project is until that time comes.
Since you know you’re going to be wrong at some point, what can
you do about it? Not much, except to minimize the damage and
incorporate uncertainty into your decision-making processes.
- Page
-2
HOW TO BE GOOD AT BEING WRONG
The greatest value of sensitivity analysis is that it quickly shows you
just how wrong your valuation estimate can be and still be OK.
When you’re investing precious resources into a project or a
business, you’ll definitely want to know what will happen should
things turn out worse or better than expected.
Simply stated, sensitivity analysis studies multiple scenarios. You
create a range of excessively negative and positive situations
(including the most likely scenario in between) and adjust a limited
number of key variables like discount and growth rates. You then
compare all these scenarios. The purpose is to reveal how sensitive a
model is to fluctuations in one direction or another. Because
valuation is an imperfect science, financial decision-makers
desperately want to know the margin of error they have if
something goes wrong.
The most basic approach in the sensitivity-analysis tool kit is simple
data entry—substituting different figures into your formulas and
models and seeing what you get. When doing your analysis of
discounted cash flow, net present value, or internal rate of return,
the easiest way to incorporate sensitivity analysis is to make a table
with long-term growth figures as column values and various
discount rates as row values. (You can select other relevant inputs,
- Page
-3
but whichever you choose, make sure you’re focusing on those that
have the most influence over the outcome.) Changing these
variables can show you how a small movement can vastly alter the
expected intrinsic value of an investment.
L ...
Graphic Representation Grading GuideCOMTM541 Version 22.docx
Graphic Representation Grading Guide
COM/TM541 Version 2
2
Individual Assignment: Graphic Representation
Purpose of Assignment
The purpose of this assignment reinforces the concept of how two different or opposing conclusions can be drawn from similar sets of data. Since data is often the basis for business decision making, you must think critically about how charts are created and where information presented originates. By telling two different stories in the creation of two charts or graphs, you will be more capable of analyzing and questioning graphics that you may encounter in your career. Another goal of the assignment is to have you locate raw data and convert it into a professional chart or graph using best design practices by paying attention to the use of colors, labels, sizing, and fonts.
Resources Required
· Misleading Statistics
· Census data
· Internet usage data
· U. S. Energy stats
Grading Guide
Content
Met
Partially Met
Not Met
Comments:
The student chooses a data source.
The student creates a graph or table that illustrates the story behind the data.
The student creates another graph or table of a contradicting story using the same data set.
The student compares the results of the graphs and includes the source of the data.
The student compares the results of the graphs and includes the story the data is telling the reader.
The student compares the results of the graphs and includes the reasons behind the graphing techniques used.
The student compares the results of the graphs and includes the contradicting story that was being told.
The student compares the results of the graphs and includes ethical implications of altering the aesthetics of the graphic.
The student compares the results of the graphs and includes how technology is used to create graphs and clarify business communication.
The summary is no more than 525 words in length.
Total Available
Total Earned
8.5
#/8.5
Writing Guidelines
Met
Partially Met
Not Met
Comments:
The paper—including tables and graphs, headings, title page, and reference page—is consistent with APA formatting guidelines and meets course-level requirements.
Intellectual property is recognized with in-text citations and a reference page.
Paragraph and sentence transitions are present, logical, and maintain the flow throughout the paper.
Sentences are complete, clear, and concise.
Rules of grammar and usage are followed including spelling and punctuation.
Total Available
Total Earned
3.5
#/3.5
Assignment Total
#
12
#/12
Additional comments:
Copyright © 2016 by University of Phoenix. All rights reserved.
Misleading Statistics
COM/TM541 Version 2
3Misleading Statistics
When talking about misleading statistics, two references are most often cited:
1) The quote, “There are three kinds of lies: lies, damned lies, and statistics” (attributed to Mark Twain, Benjamin Disraeli, or someone else)
2) The book, How t ...
InstructionDue Date 6 pm on October 28 (Wed)Part IProbability a.docx
InstructionDue Date: 6 pm on October 28 (Wed)
Part IProbability and Sampling Distributions1.Thinking about probability statements. Probability is measure of how likely an event is to occur. Match one of probabilities that follow with each statement of likelihood given (The probability is usually a more exact measure of likelihood than is the verbal statement.)Answer0 0.01 0.3 0.6 0.99 1(a) This event is impossible. It can never occur.(b) This event is certain. It will occur on every trial.(c) This event is very unlikely, but it will occur once in a while in a long sequence of trials.(d) This event will occur more often that not.2. Spill or Spell? Spell-checking software catches "nonword errors" that result in a string of letters that is not a word, as when "the" is typed as "the." When undergraduates are asked to write a 250-word essay (without spell-checking), the number X of nonword errors has the following distribution:Value of X01234Probability0.10.20.30.30.1(a) Check that this distribution satisfies the two requirements for a legitimate assignment of probabilities to individual outcomes.(b) Write the event "at least one nonword error" in term of X (for example, P(X >3)). What is the probability of this event?(c) Describe the event X ≤ 2 in words. What is its probability? 3. Discrete or continuous? For each exercise listed below, decide whether the random variable described is discrete or continuous and explains the sample space.(a) Choose a student in your class at random. Ask how much time that student spent studying during the past 24 hours.(b) In a test of a new package design, you drop a carton of a dozen eggs from a height of 1 foot and count the number of broken eggs.(c) A nutrition researcher feeds a new diet to a young male white rat. The response variable is the weight (in grams) that the rat gains in 8 weeks.4. Tossing Coins(a) The distribution of the count X of heads in a single coin toss will be as follows. Find the mean number of heads and the variance for a single coin toss.Number of Heads (Xi)01mean:Probability (Pi)0.50.5variance:(b) The distribution of the count X of heads in four tosses of a balanced coin was as follows but some missing probabilities. Fill in the blanks and then find the mean number of heads and the variance for the distribution with assumption that the tosses are independent of each other.Number of Heads (Xi)01234mean:Probability (Pi)0.06250.0625variance:(c) Show that the two results of the means (i.e. single toss and four tosses) are related by the addition rule for means. (d) Show that the two results of the variances (i.e. single toss and four tosses) are related by the addition rule for variances (note: It was assumed that the tosses are independent of each other). 5. Generating a sampling distribution. Let's illustrate the idea of a sampling distribution in the case of a very small sample from a very small .
A General Manger of Harley-Davidson has to decide on the size of a.docx
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 .
Mb0040 statistics for management
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
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Module Five Normal Distributions & Hypothesis TestingTop of F.docx
Module Five: Normal Distributions & Hypothesis Testing
Top of Form
Bottom of Form
·
Introduction & Goals
This week's investigations introduce and explore one of the most common distributions (one you may be familiar with): the Normal Distribution. In our explorations of the distribution and its associated curve, we will revisit the question of "What is typical?" and look at the likelihood (probability) that certain observations would occur in a given population with a variable that is normally distributed. We will apply our work with Normal Distributions to briefly explore some big concepts of inferential statistics, including the Central Limit Theorem and Hypothesis Testing. There are a lot of new ideas in this week’s work. This week is more exploratory in nature.
Goals:
· Explore the Empirical Rule
· Become familiar with the normal curve as a mathematical model, its applications and limitations
· Calculate z-scores & explain what they mean
· Use technology to calculate normal probabilities
· Determine the statistical significance of an observed difference in two means
· Use technology to perform a hypothesis test comparing means (z-test) and interpret its meaning
· Use technology to perform a hypothesis test comparing means (t-test) (optional)
· Gather data for Comparative Study Final Project.
·
DoW #5: The SAT & The ACT
Two Common Tests for college admission are the SAT (Scholastic Aptitude Test) and the ACT (American College Test). The scores for these tests are scaled so that they follow a normal distribution.
· The SAT reported that its scores were normally distributed with a mean μ=896 and a standard deviation σ=174
· The ACT reported that its scores were normally distributed with a mean μ=20.6 and a standard deviation σ=5.2.
We have two questions to consider for this week’s DoW:
2. A high school student Bobby takes both of these tests. On the SAT, he achieves a score of 1080. On the ACT, he achieves a score of 30. He cannot decide which score is the better one to send with his college applications.
. Question: Which test score is the stronger score to send to his colleges?
· A hypothetical group called SAT Prep claims that students who take their SAT Preparatory course score higher on the SAT than the general population. To support their claim, they site a study in which a random sample of 50 SAT Prep students had a mean SAT score of 1000. They claim that since this mean is higher than the known mean of 896 for all SAT scores, their program must improve SAT scores.
. Question: Is this difference in the mean scores statistically significant? Does SAT Prep truly improve SAT Scores?
.
Investigation 1: What is Normal?
One reason for gathering data is to see which observations are most likely. For instance, when we looked at the raisin data in DoW #3, we were looking to see what the most likely number of raisins was for each brand of raisins. We cannot ever be certain of the exact number of raisins in a box (because it varies) ...
Descriptive and Inferential Statistics
The two major areas of statistics are: descriptive statistics and inferential statistics. In this presentation, the difference between the two are shown including examples.
reply to a postI really enjoyed watching the How Racist Are You- video.docx
reply to a post
I really enjoyed watching the “How Racist Are You?” video. I thought it was such a great social experiment on how racism is portrayed and how many people simply don’t understand what it’s like to be discriminated against. Racism is defined as the prejudice, discrimination, or antagonism by an individual, community, or institution against a person or people on the basis of their membership in a particular ethnic group. The idea of racism, dates back to many ages ago, and unfortunately is a still a big problem today.
While watching the video, I found myself to be in disbelief at times when the blue-eyed group attempted to share their “similar” experience. It just showed how some of those people have never been prejudiced a day in their life. I think people like to argue that they aren’t racist because they simply don’t have a problem with one’s skin color, but fail to see the bigger picture. You really cannot argue one’s personal experience. If you fail to see that person’s reality and aren’t facing what they face on a daily, you are a part of the bigger problem. Our society is in denial when it comes to racism and people tend to blame the victims on the receiving end.
We all have different experiences in our lives, but I think we need to do better in simply understanding each other. I think the social experiment did a great job in attempting to put things into perspective for the others. There are many people who simply just disregard you because you haven’t lived the life THEY have lived and they don’t seem to realize that there is far more going on in this world. I don’t have many personal experiences, but I have a spouse who has been targeted before and has shared many stories with me on his experiences. I think we just need to do better as a society and there is still a lot more work that needs to be done. Unfortunately, the problem is still at hand.
.
reply to this discussionOver the years the grand nursing theories have.docx
reply to this discussion
Over the years the grand nursing theories have provided practical guidance to nurses and have helped to create the basis for making certain clinical decisions to help the patient and, in turn, provide quality care. One of the prominent figures in developing these theories was Virginia Henderson. She pioneered the nursing need theory, which centered around increasing patient independence to improve the quality of their care post-hospitalization. It consisted of 14 vital components—many of which are rooted in Maslow's Hierarchy of needs—like eating, drinking, and breathing to an adequate standard. Her foremost accomplishment was coming up with the definition of nursing as "assistance in aiding individuals to develop independence in connection to execution of activities contributing to health or its recoveryâ€; she wanted to demonstrate that with these 14 needs the individual can recover. However, some of the needs mentioned by Henderson only apply to individuals who are fully functional and not to those who require assistance, thus contradicting their main goal of being independent. However, the theory is still applied in specific circumstances in order to achieve better performance in nursing care. Thus, knowing and applying the grand theories will enable the development of models and frameworks that will help provide high-quality care that is patient-centered and prevent nursing from being regarded from a more generic perspective (Smith, 2019).
In the field of nursing,  the best practices and knowledge foundations are always evolving. Changes in research strategies, professional procedures, or nursing care may be required as new studies and experience deepen our understanding. When evaluating and using any information, procedure, substance, or experiment provided, practitioners and researchers should always rely on their own experience and knowledge. They must take into account both their own safety and the safety of others, particularly those of whom they have a duty of care, when employing such information or procedures. On the other hand, mid-range nursing theories are more focused and have more direct links to nursing practice than grand nursing theories, which have broader points of view. These nursing theories offer clear ideas and arguments that address nursing specialties.
Despite the fact that time has passed and that, in many cases, the grand nursing theories are not applicable, they currently help to establish a better practice and establish a better patient-nurse relationship.
.
reply to classmate post- I need it for TODAYmy test results are neutra.docx
reply to classmate post. I need it for TODAY
my test results are neutral
classmates:
Biases can be dangerous and deadly, especially when the people who have them hold positions of power. The results of my Implicit Association Test (IAT) were neutral. I scored an IAT D-score of 0 as most of my responses were similar in the compatible and incompatible trials. I believe black and white people are equal and similar except for the color of their skin. I am not biased and do not prefer a specific group of people based on the surface value of their skin. I am not surprised by my IAT results because I know I do not favor any group of people simply because their skin has a particular color or shade. I agree with the results of my IAT test as they accurately reflect my perception of racism. I feel that the implicit bias people have based on skin color is not a collective trait. Some people are still neutral and unfazed by skin color. I believe skin color is just a physical attribute that has been mistaken to mean more than it actually does.
Based on the Diversity advocate VernÄ Myers' TED talk, most people have an implicit bias of some sort (Myers, 2014). When these biases are expressed explicitly, people are likely to be surprised by the error of their ways. Myers admits to believing that men make better drivers than women. She only recognizes this implicit bias when she expresses her thoughts loudly (Myers, 2014). This goes to show that implicit bias often goes undetected even by the people who have the bias. This is because the preference is subconscious and is seldom recognized by people unless they conduct tests like the Implicit Association Tests or express their bias explicitly.
.
Regatta UniversityIT Project Charter ForStudent Degree ProgressVersion.docx
Regatta University
IT Project Charter
For
Student Degree Progress
Version 1.0
Table of Contents
Document History 3 Executive Summary 4 Project Purpose/Justification 4 Project Description 4 Preliminary Scope Statement 4 Out of Scope 4 Project Objectives and Success Criteria 4 Constraints 4 Assumptions 4 Project Deliverables 5 Project Resource Estimate 5 Summary Milestone Schedule 5 Summary Budget 5 Project Approval Requirements 6 Project Organization 6 Authorization 6 Project Team 6 Directors of Assigned Resources 6 Project Sponsor 6
Document History
Document Revision/Release Status
Executive Summary
Students leverage a web portal to access university-related information and resources. Content within it can be targeted to all users or to those within a pre-specified role (e.g., students at a particular campus) or it can be user-specific.
As an academic institution, one piece of information critical to students is their current progress in their degree program or programs.
The purpose of the student degree progress project is to create an area to display user-specific academic progress.
Project Purpose/Justification
University leadership requested this project to promote student awareness and provide students easy access to their degree progress.
Project Description
Preliminary Scope Statement
The purpose of this project is to create an area that displays user-specific academic progress within the appropriate roles, leveraging data through a back-end system. This project will include the necessary modifications to the back end to relay this information. More specifically, this project will include the following:
· Creating the graphical elements in a web-friendly format
· Creating the necessary web view to accommodate the graphical elements
· Creating the necessary changes to the back end to prepare data supporting the student information
· Creating the necessary web service to securely expose the student’s information to the graphical elements and web view
Out of Scope
Due to business-specific processes, some students, particularly those who have been pursuing a degree for over four years, may not be able to see their academic progress using the deliverables of this project. Changes to the back end to expand this functionality are out of scope, as are any changes intended to enhance functionality to provide additional data beyond the academic progress already captured in another system.
Project Objectives and Success Criteria
· The project will provide the functionality described within the preliminary scope statement.
· The project will be delivered without incurring any additional expenses.
· The project will be delivered free from any significant defects or without causing any down time.
· The project will be delivered no later than two weeks from the target time frame defined within the milestone schedule below.
Constraints
· Additional funding is not available.
· The information available to students is derived from data withi.
Reflection post500 wordsAPA 7th edition3 references within 5 yearsDisc.docx
Reflection post
500 words
APA 7th edition
3 references within 5 years
Discuss the application of Middle Range Theories and Nursing Practice and Reflect on topics: theory of pain, theory of unpleasant symptoms, theory of Self-Efficacy,
Theory of Chronic Sorrow
, spirituality in nursing practice, theory of attachment.
.
Reflect on your own journey of cultural competency- including the conc.docx
Reflect on your own journey of cultural competency, including the concepts and experiences addressed in this course.
Address the following four objectives and express your personal level of achievement of each objective:
Explore your own cultural identity and heritage and confront biases and stereotypes.
Develop an awareness and understanding of the complexities of the modern healthcare delivery system - its philosophy and problems, biases, and stereotypes.
Develop a keen awareness of the socialization process that brings the provider into this complex system.
Develop the ability to “hear†things that transcend language, and to foster an understanding of the patient and his or her cultural heritage, and the resilience found within the culture that supports family and community structures.
Identify areas in which you see the need for further development and improvement. Create a professional development plan to address these areas. Include web-based and/or community resources and opportunities for service that can assist you in working toward achievement of the above objectives.
Using a simple table with two columns label your columns Personal Cultural Need in Column 1 and Achieving the Need in Column 2.
.
Reflect on how practicing your initiative skill in this course has mad.docx
Reflect on how practicing your initiative skill in this course has made you a stronger communicator.
Then, provide specific examples for how you have taken initiative in one of the areas below and explain how it has improved your communication in that area.
Collaborating with others.
Addressing interpersonal conflict.
Presenting an articulate argument.
5-7 sentences
.
More Related Content
Similar to Reasoning with DataChapter 2 2019 McGraw-Hill Education- All rights re.docx
The Roles of Data and Predictive Analytics in BusinessChapter .docx
The Roles of Data and Predictive Analytics in Business
Chapter 1
© 2019 McGraw-Hill Education. All rights reserved. Authorized only for instructor use in the classroom. No reproduction or distribution without the prior written consent of McGraw-Hill Education.
Learning Objectives
Explain how predictive analytics can help in business strategy formulation.
Distinguish structured from unstructured data.
Differentiate units of observation.
Outline a data-generating process.
Describe the primary ways that data analysis is used to aid business performance.
Discriminate between lead and lag information.
Discriminate between active and passive prediction.
Recognize questions pertaining to business strategy that may utilize (active) predictive analytics.
‹#›
© 2019 McGraw-Hill Education.
Defining Data & Data Uses in Business
Data
A collection of information
Database
Organized collection of data that firms use for analysis
Business analytics
The use of data analysis to aid in business decision making
Predictive analytics
The use of data analysis to designed to form predictions about future, unknown, events or outcomes
‹#›
© 2019 McGraw-Hill Education.
Business Strategy
Plan of action designed by a business practitioner to achieve a business objective
Business objectives include profit maximization, enhanced employee satisfaction, etc.
Examples of action include pricing decisions, advertising campaigns, and methods of employee compensation
‹#›
© 2019 McGraw-Hill Education.
Predictive Analytics for Business Strategy
With no data, strong theoretical model is often not enough to predict effective business strategies
Sound theoretical arguments coupled with data becomes a strong tool to predict effective business strategies
Predictive analytics is an ideal complement to create a successful business strategy
‹#›
© 2019 McGraw-Hill Education.
Data Features
Structured Data
Data with well-defined units of observation which can be classified and structured in the form of a spreadsheet.
An example:
‹#›
© 2019 McGraw-Hill Education.
Data Features
Unstructured Data
Any data that cannot be classified and structured.
An example:
‹#›
© 2019 McGraw-Hill Education.
The Unit of Observation
The entity for which information has been collected
Crucial component of structured data
Tells us the way the in which the information in a dataset varies
Answers the questions: What, Where, Who, When?
Four main groupings: cross-sectional data, pooled cross-sectional data, time-series data, and panel data
‹#›
© 2019 McGraw-Hill Education.
Data Types
Cross-Sectional Data
Data that provide a snapshot of information at one fixed point in time.
An example:
‹#›
© 2019 McGraw-Hill Education.
Data Typ.
Hypothesis TestingThe Right HypothesisIn business, or an.docx
Hypothesis Testing
The Right Hypothesis
In business, or any other discipline, once the question has been asked there must be a statement as to what will or will not occur through testing, measurement, and investigation. This process is known as formulating the right hypothesis. Broadly defined a hypothesis is a statement that the conditions under which something is being measured or evaluated holds true or does not hold true. Further, a business hypothesis is an assumption that is to be tested through market research, data mining, experimental designs, quantitative, and qualitative research endeavors. A hypothesis gives the businessperson a path to follow and specific things to look for along the road.
If the research and statistical data analysis supports and proves the hypothesis that becomes a project well done. If, however, the research data proved a modified version of the hypothesis then re-evaluation for continuation must take place. However, if the research data disproves the hypothesis then the project is usually abandoned.
Hypotheses come in two forms: the null hypothesis and the alternate hypothesis. As a student of applied business statistics you can pick up any number of business statistics textbooks and find a number of opinions on which type of hypothesis should be used in the business world. For the most part, however, and the safest, the better hypothesis to formulate on the basis of the research question asked is what is called the null hypothesis. A null hypothesis states that the research measurement data gathered will not support a difference, relationship, or effect between or amongst those variables being investigated. To the seasoned research investigator having to accept a statement that no differences, relationships, and/or effects will occur based on a statistical data analysis is because when nothing takes place or no differences, effects, or relationship are found there is no possible reason that can be given as to why. This is where most business managers get into trouble when attempting to offer an explanation as to why something has not happened. Attempting to provide an answer to why something has not taken place is akin to discussing how many angels can be placed on the head of a pin—everyone’s answer is plausible and possible. As such business managers need to accept that which has happened and not that which has not happened.
Many business people will skirt the null hypothesis issue by attempting to set analternative hypothesis that states differences, effects and relationships will occur between and amongst that which is being investigated if certain conditions apply.Unfortunately, however, this reverse position is as bad. The research investigator might well be safe if the data analysis detects differences, effect or relationships, but what if it does not? In that case the business manager is back to square one in attempting to explain what has not happened. Although the hypothesis situation may seem c.
Coefficient alpha& exampleCoefficient alpha checks the internal .docx
Coefficient alpha& example
Coefficient alpha checks the internal consistency of measurements with dichotomous choices. It is equivalent to performing the split half methodology on all combinations of questions and is applicable when each question is either right or wrong. A correct question scores 1 and an incorrect question scores 0. The test statistic (Coefficient alpha) is:
where
k = number of questions
pj = number of people in the sample who answered question j correctly
qj = number of people in the sample who didn’t answer question j correctly
σ2 = variance of the total scores of all the people taking the test = VAR.S(R1) where R1 = array containing the total scores of all the people taking the test.
Values range from 0 to 1. A high value indicates reliability, while too high a value (in excess of .90) indicates a homogeneous test.
Example 1: A questionnaire with 11 questions is administered to 12 students. The results are listed in the upper portion of Figure 1. Determine the reliability of the questionnaire using Coefficient alpha
Summary :
The value Coefficient alpha (α)= 0.768 shows that the test has a good reliability.
note:
- scales with (α) between 0.80 - 0.95 are considered to have very good reliability.
- scales with (α) between 0.70 - 0.80 are considered to have good reliability.
- scales with (α) between 0.60 - 0.70 indicates fair reliability.
- if the (α) is below 0.60, the scales has poor reliability.
Figure 1Q1Q2Q3Q4Q5Q6Q7Q8Q9Q10Q11TOTAL1111111111111121111111101093101111111008411101101100751111100010066011011110006711110010000581111100000059010110000104101001010000031111100000000312100100000002TOTAL10999865543169p= percentage of students who asnwsweredthat question correctely 0.8333333333
Hanan: or (# of people aswered correctely / total people
= 10/12=0.8333330.750.750.750.66666666670.50.41666666670.41666666670.33333333330.250.0833333333q= are the percentage of students who answered that question incorrectly. q= 1-p0.1666666667
Hanan:
or (# of people aswered incorrectely / total people
=2/12= 0.1666670.250.250.250.33333333330.50.58333333330.58333333330.66666666670.750.9166666667pq = p*q0.13888888890.18750.18750.18750.22222222220.250.24305555560.24305555560.22222222220.18750.07638888892.1458333333k
Hanan:
number of questions11∑pq2.1458333333var ( ơ2)
Hanan:
see VAR worksheet for more explenation.7.1136363636 coefficient aplha (α)
Hanan:
=k/(k-I)*(1-∑pq/var)0.7681842386The value Coefficient alpha (α)= 0.768 shows that the test has a good reliability.
VARXX =(69/12)X-X(X-X)2115.2527.562593.2510.562582.255.062571.251.562560.250.0625̅̅̅60.250.06255-0.750.56255-0.750.56254-1.753.06253-2.757.56253-2.757.56252-3.7514.0625695.75078.25VAR= ơ2
Hanan:
= ∑(x-̅x)2/(n-1)
= 78.25/(12-1)
= 78.25/11
7.1136363636
3
©2013 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
Theory Building
Business Research Met.
!JWI 531 Financial Management II Week Four Lec.docx
!
JWI 531
Financial Management II
Week Four | Lecture Two
!
!
Please note that this basic version of the lecture is provided as a convenience for the student, and may be
missing interactive materials throughout. Students are still responsible for reviewing the missing
materials - including audio, video, and interactive widgets - that are found in the full lecture.
- Page
-1
ADDITIONAL VALUATION
TECHNIQUES: SENSITIVITY ANALYSIS
AND DECISION TREES
!
In the digital age, businesses are deluged with data. Sophisticated
tools are abundant. Until recently, however, the financial world’s
wizardry seemed invincible. Recent events have significantly
changed that perception.
But a few complex techniques still remain unblemished. Sensitivity
analysis and decision trees, in particular, can help you manage
uncertainty about the future. And businesses today have learned to
live with a high degree of uncertainty.
The assets companies own will eventually reveal their full,
productive capacity. The key word is eventually. You won’t know just
how valuable an entity or a project is until that time comes.
Since you know you’re going to be wrong at some point, what can
you do about it? Not much, except to minimize the damage and
incorporate uncertainty into your decision-making processes.
- Page
-2
HOW TO BE GOOD AT BEING WRONG
The greatest value of sensitivity analysis is that it quickly shows you
just how wrong your valuation estimate can be and still be OK.
When you’re investing precious resources into a project or a
business, you’ll definitely want to know what will happen should
things turn out worse or better than expected.
Simply stated, sensitivity analysis studies multiple scenarios. You
create a range of excessively negative and positive situations
(including the most likely scenario in between) and adjust a limited
number of key variables like discount and growth rates. You then
compare all these scenarios. The purpose is to reveal how sensitive a
model is to fluctuations in one direction or another. Because
valuation is an imperfect science, financial decision-makers
desperately want to know the margin of error they have if
something goes wrong.
The most basic approach in the sensitivity-analysis tool kit is simple
data entry—substituting different figures into your formulas and
models and seeing what you get. When doing your analysis of
discounted cash flow, net present value, or internal rate of return,
the easiest way to incorporate sensitivity analysis is to make a table
with long-term growth figures as column values and various
discount rates as row values. (You can select other relevant inputs,
- Page
-3
but whichever you choose, make sure you’re focusing on those that
have the most influence over the outcome.) Changing these
variables can show you how a small movement can vastly alter the
expected intrinsic value of an investment.
L ...
Graphic Representation Grading GuideCOMTM541 Version 22.docx
Graphic Representation Grading Guide
COM/TM541 Version 2
2
Individual Assignment: Graphic Representation
Purpose of Assignment
The purpose of this assignment reinforces the concept of how two different or opposing conclusions can be drawn from similar sets of data. Since data is often the basis for business decision making, you must think critically about how charts are created and where information presented originates. By telling two different stories in the creation of two charts or graphs, you will be more capable of analyzing and questioning graphics that you may encounter in your career. Another goal of the assignment is to have you locate raw data and convert it into a professional chart or graph using best design practices by paying attention to the use of colors, labels, sizing, and fonts.
Resources Required
· Misleading Statistics
· Census data
· Internet usage data
· U. S. Energy stats
Grading Guide
Content
Met
Partially Met
Not Met
Comments:
The student chooses a data source.
The student creates a graph or table that illustrates the story behind the data.
The student creates another graph or table of a contradicting story using the same data set.
The student compares the results of the graphs and includes the source of the data.
The student compares the results of the graphs and includes the story the data is telling the reader.
The student compares the results of the graphs and includes the reasons behind the graphing techniques used.
The student compares the results of the graphs and includes the contradicting story that was being told.
The student compares the results of the graphs and includes ethical implications of altering the aesthetics of the graphic.
The student compares the results of the graphs and includes how technology is used to create graphs and clarify business communication.
The summary is no more than 525 words in length.
Total Available
Total Earned
8.5
#/8.5
Writing Guidelines
Met
Partially Met
Not Met
Comments:
The paper—including tables and graphs, headings, title page, and reference page—is consistent with APA formatting guidelines and meets course-level requirements.
Intellectual property is recognized with in-text citations and a reference page.
Paragraph and sentence transitions are present, logical, and maintain the flow throughout the paper.
Sentences are complete, clear, and concise.
Rules of grammar and usage are followed including spelling and punctuation.
Total Available
Total Earned
3.5
#/3.5
Assignment Total
#
12
#/12
Additional comments:
Copyright © 2016 by University of Phoenix. All rights reserved.
Misleading Statistics
COM/TM541 Version 2
3Misleading Statistics
When talking about misleading statistics, two references are most often cited:
1) The quote, “There are three kinds of lies: lies, damned lies, and statistics” (attributed to Mark Twain, Benjamin Disraeli, or someone else)
2) The book, How t ...
InstructionDue Date 6 pm on October 28 (Wed)Part IProbability a.docx
InstructionDue Date: 6 pm on October 28 (Wed)
Part IProbability and Sampling Distributions1.Thinking about probability statements. Probability is measure of how likely an event is to occur. Match one of probabilities that follow with each statement of likelihood given (The probability is usually a more exact measure of likelihood than is the verbal statement.)Answer0 0.01 0.3 0.6 0.99 1(a) This event is impossible. It can never occur.(b) This event is certain. It will occur on every trial.(c) This event is very unlikely, but it will occur once in a while in a long sequence of trials.(d) This event will occur more often that not.2. Spill or Spell? Spell-checking software catches "nonword errors" that result in a string of letters that is not a word, as when "the" is typed as "the." When undergraduates are asked to write a 250-word essay (without spell-checking), the number X of nonword errors has the following distribution:Value of X01234Probability0.10.20.30.30.1(a) Check that this distribution satisfies the two requirements for a legitimate assignment of probabilities to individual outcomes.(b) Write the event "at least one nonword error" in term of X (for example, P(X >3)). What is the probability of this event?(c) Describe the event X ≤ 2 in words. What is its probability? 3. Discrete or continuous? For each exercise listed below, decide whether the random variable described is discrete or continuous and explains the sample space.(a) Choose a student in your class at random. Ask how much time that student spent studying during the past 24 hours.(b) In a test of a new package design, you drop a carton of a dozen eggs from a height of 1 foot and count the number of broken eggs.(c) A nutrition researcher feeds a new diet to a young male white rat. The response variable is the weight (in grams) that the rat gains in 8 weeks.4. Tossing Coins(a) The distribution of the count X of heads in a single coin toss will be as follows. Find the mean number of heads and the variance for a single coin toss.Number of Heads (Xi)01mean:Probability (Pi)0.50.5variance:(b) The distribution of the count X of heads in four tosses of a balanced coin was as follows but some missing probabilities. Fill in the blanks and then find the mean number of heads and the variance for the distribution with assumption that the tosses are independent of each other.Number of Heads (Xi)01234mean:Probability (Pi)0.06250.0625variance:(c) Show that the two results of the means (i.e. single toss and four tosses) are related by the addition rule for means. (d) Show that the two results of the variances (i.e. single toss and four tosses) are related by the addition rule for variances (note: It was assumed that the tosses are independent of each other). 5. Generating a sampling distribution. Let's illustrate the idea of a sampling distribution in the case of a very small sample from a very small .
A General Manger of Harley-Davidson has to decide on the size of a.docx
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 .
Mb0040 statistics for management
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
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Module Five Normal Distributions & Hypothesis TestingTop of F.docx
Module Five: Normal Distributions & Hypothesis Testing
Top of Form
Bottom of Form
·
Introduction & Goals
This week's investigations introduce and explore one of the most common distributions (one you may be familiar with): the Normal Distribution. In our explorations of the distribution and its associated curve, we will revisit the question of "What is typical?" and look at the likelihood (probability) that certain observations would occur in a given population with a variable that is normally distributed. We will apply our work with Normal Distributions to briefly explore some big concepts of inferential statistics, including the Central Limit Theorem and Hypothesis Testing. There are a lot of new ideas in this week’s work. This week is more exploratory in nature.
Goals:
· Explore the Empirical Rule
· Become familiar with the normal curve as a mathematical model, its applications and limitations
· Calculate z-scores & explain what they mean
· Use technology to calculate normal probabilities
· Determine the statistical significance of an observed difference in two means
· Use technology to perform a hypothesis test comparing means (z-test) and interpret its meaning
· Use technology to perform a hypothesis test comparing means (t-test) (optional)
· Gather data for Comparative Study Final Project.
·
DoW #5: The SAT & The ACT
Two Common Tests for college admission are the SAT (Scholastic Aptitude Test) and the ACT (American College Test). The scores for these tests are scaled so that they follow a normal distribution.
· The SAT reported that its scores were normally distributed with a mean μ=896 and a standard deviation σ=174
· The ACT reported that its scores were normally distributed with a mean μ=20.6 and a standard deviation σ=5.2.
We have two questions to consider for this week’s DoW:
2. A high school student Bobby takes both of these tests. On the SAT, he achieves a score of 1080. On the ACT, he achieves a score of 30. He cannot decide which score is the better one to send with his college applications.
. Question: Which test score is the stronger score to send to his colleges?
· A hypothetical group called SAT Prep claims that students who take their SAT Preparatory course score higher on the SAT than the general population. To support their claim, they site a study in which a random sample of 50 SAT Prep students had a mean SAT score of 1000. They claim that since this mean is higher than the known mean of 896 for all SAT scores, their program must improve SAT scores.
. Question: Is this difference in the mean scores statistically significant? Does SAT Prep truly improve SAT Scores?
.
Investigation 1: What is Normal?
One reason for gathering data is to see which observations are most likely. For instance, when we looked at the raisin data in DoW #3, we were looking to see what the most likely number of raisins was for each brand of raisins. We cannot ever be certain of the exact number of raisins in a box (because it varies) ...
Descriptive and Inferential Statistics
The two major areas of statistics are: descriptive statistics and inferential statistics. In this presentation, the difference between the two are shown including examples.
Similar to Reasoning with DataChapter 2 2019 McGraw-Hill Education- All rights re.docx (15)
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!JWI 531 Financial Management II Week Four Lec.docx
!JWI 531 Financial Management II Week Four Lec.docx
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Graphic Representation Grading GuideCOMTM541 Version 22.docx
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InstructionDue Date 6 pm on October 28 (Wed)Part IProbability a.docx
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A General Manger of Harley-Davidson has to decide on the size of a.docx
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CASE STUDY:
An Older Immigrant Couple: Mr. and Mrs. Arahan Mr. and Mrs. Arahan, an older couple in their seventies, have been living with their oldest daughter, her husband of 15 years, and their two children, ages 12 and 14. They all live in a middle-income neighborhood in a suburb of a metropolitan city. Mr. and Mrs. Arahan are both college educated and worked full-time while they were in their native country. In addition, Mr. Arahan, the only offspring of wealthy parents, inherited a substantial amount of money and real estate. Their daughter came to the United States as a registered nurse and met her husband, a drug company representative. The older couple moved to the United States when their daughter became a U.S. citizen and petitioned them as immigrants. Since the couple was facing retirement, they welcomed the opportunity to come to the United States.
The Arahans found life in the United States different from that in their home country, but their adjustment was not as difficult because both were healthy and spoke English fluently. Most of their time was spent taking care of their two grandchildren and the house. As the grandchildren grew older, the older couple found that they had more spare time. The daughter and her husband advanced in their careers and spent a great deal more time at their jobs. There were few family dinners during the week. On weekends, the daughter, her husband, and their children socialized with their own friends. The couple began to feel isolated and longed for a more active life.
Mr. and Mrs. Arahan began to think that perhaps they should return to the home country, where they still had relatives and friends. However, political and economic issues would have made it difficult for them to live there. Besides, they had become accustomed to the way of life in the United States with all the modern conveniences and abundance of goods that were difficult to obtain in their country. However, they also became concerned that they might not be able to tolerate the winter months and that minor health problems might worsen as they aged. They wondered who would take care of them if they became very frail and where they would live, knowing that their daughter had only saved money for their grandchildren’s college education. They expressed their sentiments to their daughter, who became very concerned about how her parents were feeling.
This older couple had been attending church on a regular basis, but had never been active in other church-related activities. The church bulletin announced the establishment of parish nursing with two retired registered nurses as volunteers. The couple attended the first opening of the parish clinic. Here, they met one of the registered nurses, who had a s.
read the following NPR article -The Anti-Pollution Documentary That's.docx
read the following NPR article "
The Anti-Pollution Documentary That's Taken China By Storm
Links to an external site.
" (and/or watch Chai Jing's documentary - Under the Dome), and then watch the CNN documentary "
Freedom Project: Cocoa-nomics
Links to an external site.
";
Then, respond to the following two questions -
To what extent has globalization contributed to global ethical issues highlighted in the NPR article and the CNN documentary?
How can businesses make positive change?
no specific amount of words just answer the questions.
.
Question 2- Libya recently announced that it is claiming a 200-mile zo.docx
Question 2:
Libya recently announced that it is claiming a 200-mile zone off its coast as its territorial waters (and airspace). To stake its claim, Libya placed armed warships across the 200 mile line and warned the world that this line constitutes a “line of death†to anyone who might cross it. Customary International Law establishes that States may only claim 12 miles off their coast as territorial waters. Immediately, the Security Council adopts a resolution condemning Libya’s actions as “violating international law and constituting a threat to the peace.†The Resolution, however, stated nothing about using force against Libya. The United States, pursuant to Congressional authorization, sends armed warships and three fighter jets to patrol the 200 mile zone. The U.S. warships and Fighter Jets never came closer than 30 miles to the Libyan coast. After a Libyan ship fired at one of the fighter jets, the U.S. warships and fighter jets returned fire, destroying two Libyan ships. Did the U.S.’s actions violate the U.N. Charter? Even if the U.S. did not violate the Charter, should the United States care whether its actions violated the U.N. Charter? (At least 300 words).
.
Question- Topic 5 DQ 1A doctoral learner has decided to do.docx
Question- Topic 5 DQ 1
A doctoral learner has decided to do a case study for his/her proposed dissertation research study topic because it is believed to be the best approach to address the research questions. The researcher's choices of data sources for this particular study are to conduct interviews, to conduct observations, and to conduct focus groups. Will these data sources generate the breadth and depth of the data necessary for this design? Why or why not? What challenges might the researcher encounter in collecting data from these sources? Explain.
Student 1 - Heather R.
Conducting interviews, observations, and focus groups as data collection methods meets the requirement of using three or more data sources to answer the research questions of a descriptive case study. Both interviews and focus groups generate rich descriptive data that can yield in-depth analysis during triangulation, a technique used for checking and comparing the collected data (Forrest- Lawrence, 2019). When interviews and observations are used as data sources in a qualitative case study design, they add depth and breadth to the study and help get the most complete answer to the research question.
Researchers may have trouble with interviews and focus groups if they don't have the skills to ask open-ended questions and dig deeper, or if they don't know enough about the participants' cultures to understand what they're doing when they conduct observations. According to Schoch (2020), this requires the implementation of protocols that are important for ensuring uniformity between interviews or for ensuring that interviewers know what they are looking for when conducting observations.
References
Forrest-Lawrence, P. (2019). Case study research. In (Ed.), Handbook of research methods in health social sciences (pp. 317–331). Springer Singapore.
Schoch, K. (2020). Case study research. Research design and methods: An applied guide for the scholar-practitioner , 245-258.
Student 2 - JuanG
A researcher’s mission in a case study design is to pursue solid data collection illustrating his/her proposed study topic phenomenon.
Moving into the data collection for case study Nilmanat (2021) suggests a researcher conducts certain surveys, observation, and focus groups, and where each type of source allows a researcher to build data giving a distinction between the weakness and strengths of a case study. Enhancing a case study can be done by generating a foundation to portray adequate description of methodology by assessing specific criteria within the frameworks from a perspective of existing problems and current practices of related parties (Alma, 2021). Equally important to case study data collection is challenges. Challenges regarding specific criteria list by Nilmanat (2021) concludes a case study data collection as being time consuming since the data could be expansive and a need to develop volumes of data, as well as inconsistencies between the methodology, design, and th.
QUESTION 1There are several tools that are commonly used by hackers to.docx
QUESTION 1
There are several tools that are commonly used by hackers to gain illegal access to computers and network to include backdoor, botnet, denial of service attack, phishing and spoofing, robot, zombie worm, Trojan, viruses, and worms. What is a denial of service attack (DoS)? What impact would a denial of service attack have on a major critical infrastructure such as mass transit?
Your response must be at least 200 words in length.
QUESTION 2
There are several differences between the terms
cyber crime
and
cyber terrorism
. Distinguish between these terms. Explain why it is important for investigators and intelligence agencies to have a clear distinction between each of these terms. Further, outline and explain three examples of cybercrime and three examples of cyber terrorism.
Your response must be at least 200 words in length
.
Question 1Initial Post- -The Story of an Hour-- by Kate Chopin- is set.docx
Question 1
Initial Post:
"The Story of an Hour," by Kate Chopin, is set in very limited confines. The events occur primarily in a room, on a staircase, and at a front door. How does this limitation help to express the themes of the story?
Question 2
Initial Post:
In James Joyce’s story “Eveline†the main character ultimately decides not to leave, but before she makes that decision, we’re told, “All the seas of the world tumbled about her heart. He was drawing her into them: he would drown her.†She wasn’t going to be literally drowned by Frank, but what is the significance of this water imagery? How does it help to suggest Eveline’s reasons for not leaving?
Parameters
You must make a substantive and thoughtful initial post of 250 words or more to
one of the questions
, incorporating MLA citation and a Works Cited.
Readings
Meyer, Michael and D. Quentin Miller.
The Compact Bedford Introduction to Literature: Reading, Thinking, and Writing
, 12th ed., Macmillan Learning, 2020.
Introduction: Reading Imaginative Literature
- The Nature of Literature
- The Value of Literature
- The Changing Literary Canon
Chapter 1: Reading Fiction
- Reading Fiction Responsively
Chapter 8: Style, Tone, and Irony
- Style
- Tone
- Irony
Chapter 42: Critical Strategies for Reading
(all sections)
Chapter 43: Writing About Fiction
(all sections)
Selected literary works
- "The Story of an Hour" by Kate Chopin
- “Eveline†by James Joyce
.
QSO 321 Project Preassessment Evaluation Summary Based on an evalu.docx
QSO 321 Project Preassessment Evaluation Summary
Based on an evaluation of the organization’s current state and recent initiatives, the areas listed below have been identified as being in need of improvement based on the triple-bottom-line framework and B Corp Certification requirements.
Governance ï‚· A more formal structure or stakeholder group is needed to review social and environmental
performance regularly and accurately. Workers
ï‚· Employee pay is low, both for hourly and salaried employees.
ï‚· Attrition rates are high, resulting in a significant loss of company resources.
ï‚· Employees lack the autonomy to shut down unsafe practices and processes.
ï‚· Required trainings are frequent, but professional development for growth is very limited in both opportunities and funding.
Community ï‚· The organization needs to regularly screen and evaluate suppliers for social and environmental
impact, not just when an initial partnership is begun.
ï‚· The majority of materials used to grow, manufacture, ship, and sell the tea are not locally sourced.
Environment ï‚· Although some supplying farms do have some sustainable practices in place, not all facilities and
practices are designed to restore or preserve the environment.
ï‚· Most energy sources, especially energy sources for domestic locations, are not renewable.
ï‚· The organization has not conducted any kind of environmental assessment since 2010, and that assessment was only conducted to identify areas of opportunity, not for certification.
ï‚· Cost and time are prioritized in shipping, resulting in the inefficient use of transportation and a high carbon footprint.
Customers  Quality control is conducted randomly, but isn’t an ingrained part of the workflow process.
ï‚· There are no product guarantees available to customers.
ï‚· Customer feedback is primarily collected through online reviews of purchased products or sales locations, rather than customer surveys.
The following areas have been identified as being strengths of the organization based on the TBL framework and B Corp Certification requirements:
Governance  The organization’s mission, vision, code of ethics, and culture statement strongly support
sustainable, ethical practices.
ï‚· The organization has identified key goals and metrics to monitor and evaluate sustainable business practices.
Workers ï‚· Employees are very content with benefits.
ï‚· Job-related training is thorough and frequent.
ï‚· There are established employee feedback programs with high rates of engagement.
Community ï‚· The organization is very diverse in its workforce, and employees report feeling safe and
respected at work.
ï‚· There are clear, measurable goals to continue growing diversity and inclusion throughout the organization.
ï‚· The organization has set key requirements for its outsourced workforce, although regular evaluations are lacking.
ï‚· The organization provides incentives and sets goals for suppliers regarding socially responsible .
QSO 321 Module Three Assignment TemplateComplete this template by repl.docx
QSO 321 Module Three Assignment Template
Complete this template by replacing the bracketed text with the relevant information. Use the row in italics as an example.
Internal Stakeholders
External Stakeholders
image1.png
Process for Tea Workflow Production Picking, Processing, and Packaging TPicking, Processing, and Packaging Teaea
FARM Start
Workers pick tea leaves
Leaves are put into 50 kg bags
Bags are moved to a
processing area
PROCESSING Bags of tea are
reweighed
Leaves are spread across pans and
troughs
Leaves are withered on pans
Leaves are cooled
Leaves are removed from pans and
reweighed
Leaves are moved onto special trays
and rolled
Leaves go through a first
drying
Leaves are cooled
Leaves are put into new trays for a second drying
Leaves are removed from trays and reweighed; poor-quality
leaves are thrown away
Leaves are visually inspected
Poor-quality leaves are thrown away
Leaves are moved to an
oxidizing area
Leaves are oxidized
Leaves get a final drying to
complete the oxidation process
Leaves are cooled
Leaves are sent to packaging and
shipping
PACKAGING AND SHIPPING
Leaves are weighed
Leaves are sorted; small, misshaped, or discolored tea leaves
are discarded
Leaves are weighed
Leaves get a second sorting
Leaves are put into paper tea
bags and sealed
Tea bags are wrapped in
paper packets
Tea bags are sealed
Wrapped tea bags are sorted into cardboard
packs of 20
Cardboard packs are sealed and
wrapped in plastic
Boxes are sorted into shipping
boxes
Shipping boxes are wrapped and sealed, ready to be shipped
End
Process Workflow for Tea Production
FARM
Start
PROCESSING
PACKAGING AND SHIPPING
End
QSO 321 Process Workflow for Tea Production
The flowchart covers the steps involved in picking, processing, and packaging and shipping tea. The outline below detasils these steps at each stage of the process.
Farm
Workers pick usable tea leaves from the field
Picked tea leaves are weighed and grouped into 50 kg bags
Bags of weighed tea are moved to a processing area
Processing
Bags of tea are reweighed at the processing facility
Tea leaves are spread across pans and troughs
Tea leaves are withered on pans
Tea leaves are cooled
Tea leaved are removed from pans and reweighed
Tea leaves are moved onto special trays and put through a rolling machine
Tea leaves go through their first drying
Tea leaves are cooled
Tea leaves are removed from special rolling trays and put into new trays to go through a second drying machine
Tea leaves are removed from trays and reweighed; poor-quality leaves are thrown away
Tea leaves are visually inspected
Poor-quality leaves are thrown away
Tea leaves are moved to an oxidizing area
Tea leaves are oxidized
Tea leaves go through their final drying to complete the oxidation process
Tea leaves are cooled
Tea leaves are sent to packaging and shipping
Packaging and Shipping
Tea leaves are weighed
Tea leaves go through the first sorting machine; small, misshaped, or discolored tea leaves are discarded
Tea leaves are .
QSO 321 Module Two Assignment TemplateComplete this template by replac.docx
QSO 321 Module Two Assignment Template
Complete this template by replacing the bracketed text with the relevant information.
References
[Insert text here.]
image1.png
Behavior Assessment – Assignment #1
image1.png
image2.png
image3.png
image4.png
image5.png
.
PurposeThe purpose of this assignment is to provide the student an opp.docx
Purpose
The purpose of this assignment is to provide the student an opportunity to explore the roles and competencies of the advanced practice nurse (APN).
Activity Learning Outcomes
Through this assignment, the student will demonstrate the ability to:
1. Examine roles and competencies of advanced practice nurses essential to performing as leaders and advocates of holistic, safe, and quality care. (CO1)
2. Explore the process of scholarship engagement to improve health and healthcare outcomes in various settings. (CO4)
Requirements
1. The Roles in Advanced Nursing Practice paper is worth 150 points and will be graded on the quality of the content, use of citations, use of Standard English grammar, sentence structure, and overall organization based on the required components as summarized in the directions and grading criteria/rubric.
2. Submit the paper as a Microsoft Word Document. You are encouraged to use the APA Academic Writer and Grammarly tools when creating your assignment.
3. The length of the paper should be 3-6 pages, excluding title page and reference page(s).
4. Support ideas with a minimum of 2 scholarly resources. Scholarly resources do not include your textbook. You may need to use more than 2 scholarly resources to fully support your ideas.
5. You may use first person voice when describing your rationale for choosing the CNP role and your plans for clinical practice.
6. Current APA format is required with both a title page and reference page(s). Use the following as Level 1 headings to denote the sections of your paper (Level 1 headings use upper- and lower-case letters and are bold and centered):
·
· Roles in Advanced Practice Nursing (This is the paper introduction. In APA format, a restatement of the paper title, centered and bolded serves as the heading of the introduction section)
· Four APN Roles
· Rationale for Choosing CNP Role
· Plans for Clinical Practice
· Role Transition
· Conclusion
Directions
1. Introduction :Â Provide an overview of what will be covered in the paper. Introduction should include general statements on advanced practice nursing roles, general statements on the role transition from RN to APN, and identification of the purpose of the paper.
2. Four APN Roles : Describe the role, educational preparation, and work environment for the four APN roles (CNP, CNS, CRNA & CNM). Provide support from at least one scholarly source.
3. Rationale for Choosing CNP Role :Â Describe your rationale for choosing the CNP advanced practice role versus one of the other roles.
4. Plans for Clinical Practice : Discuss your plans for clinical practice after graduation. Explain how your understanding of NP practice has changed after researching the four ANP roles.
5. Role Transition :Â Discuss your transition from the RN role to the NP role. Describe two factors that may impact your transition. Discuss two strategies you will use to support a successful transition from the RN to yo.
Psychotic disorders change ones sense of reality and cause abnormal th.docx
Psychotic disorders change one’s sense of reality and cause abnormal thinking and perception. Patients presenting with psychotic disorders may suffer from delusions or hallucinations or may display negative symptoms such as lack of emotion or withdraw from social situations or relationships. Symptoms of medication-induced movement disorders can be mild or lethal and can include, for example, tremors, dystonic reactions, or serotonin syndrome.
For this Assignment, you will complete a focused SOAP note for a patient in a case study who has either a schizophrenia spectrum, other psychotic, or medication-induced movement disorder.
RESOURCES
.
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- 1. Reasoning with Data Chapter 2 © 2019 McGraw-Hill Education. All rights reserved. Authorized only for instructor use in the classroom. No reproduction or distribution without the prior written consent of McGraw-Hill Education. Learning Objectives Define reasoning. Execute deductive reasoning. Explain an empirically testable conclusion. Execute inductive reasoning. Differentiate between deductive and inductive reasoning. Explain how inductive reasoning can be used to evaluate an assumption. Describe selection bias in inductive reasoning. ‹#› ‹#› © 2019 McGraw-Hill Education. What is Reasoning? Reasoning is the process of forming conclusions, judgments, or inferences from facts or data Reasoning and logic are often used interchangeably Logic is a description of the rules and/or steps behind the reasoning process ‹#› ‹#› © 2019 McGraw-Hill Education. Two Arguments
- 2. Argument 1: The companies profits are up more than 10% over the past year. An increase in profits of 10% is the result of excellent management. You were the manager over the past year. Therefore, I conclude that you engaged in excellent management last year. Argument 2: Ten of your 300 employees came to me with complaints about your management. They indicated that you treated them unfairly by not giving them a raise they deserved. Therefore, I conclude that all of your employees are disgruntled with your management. ‹#› ‹#› © 2019 McGraw-Hill Education. Understanding Reasoning In presenting the two arguments, the goal is not to make a definitive decision about which you believe (if either) The goal is to think about and distinguish different “lines― of reasoning In distinguishing between the different types of reasoning, you will be able to establish why you believe or question the claims made in the two arguments ‹#› ‹#› © 2019 McGraw-Hill Education. Two Major Types of Reasoning Reasoning Deductive Reasoning Inductive Reasoning Both play an important role in interpreting and drawing conclusions from data analysis ‹#› ‹#›
- 3. © 2019 McGraw-Hill Education. Deductive Reasoning Deductive Reasoning Goes from the general to the specific Also known as top-down logic Seeks to prove statements of the form “If A, then B― ‹#› ‹#› © 2019 McGraw-Hill Education. Deductive Reasoning Such reasoning always implies three underlying components: assumptions (“If A―), methods of proof (“thenâ€), and conclusions (“Bâ€) ‹#› ‹#› © 2019 McGraw-Hill Education. Deductive Reasoning The purest applications of deductive reasoning are in the field of mathematics Two of the most used approaches are direct proofs and transposition Direct proofs Proof that begins with assumptions, explains methods of proof, and states the conclusion(s) Transposition Any time a group of assumptions implies a conclusion, then it is also true that any time the conclusion does not hold, at least one of the assumptions must not hold ‹#›
- 4. ‹#› © 2019 McGraw-Hill Education. Direct Proof Let’s prove the following statement by direct proof: If X and Y are odd numbers, then their sum (X + Y) is an even number An Example: If X = 5(odd) and Y = 9(odd), then their sum X + Y = 14 is an even number Failing to find a contradiction is not the same a proving a statement is generally true ‹#› ‹#› © 2019 McGraw-Hill Education. Direct Proof: A Mathematical Approach If X and Y are odd numbers, then their sum (X + Y) is an even number X and Y are odd numbers If X is an odd number, then X can be written as X=2K+1, where K is an integer. (Example: X=13 X=(2 × 6)+1) If Y is an odd number, then Y can be written as Y =2M+1, where M is an integer, (Example: Y=23 Y=(2 × 11)+1) X+Y=(2K+1)+(2M+1)=2K+2M+2=2(K+M+1) K+M+1 is an integer so X+Y is 2 times an integer Any number that is 2 times an integer is divisible by 2 This means X+Y is even ‹#› ‹#›
- 5. © 2019 McGraw-Hill Education. Direct Proof: Common Sense Approach “If McDonald’s offers breakfast all day, their revenues will increase.― McDonald’s stores offer breakfast all day. The addition of breakfast during lunch/dinner hours implies more choices. Customers already choosing McDonald’s during lunch/dinner hours can continue buying the same meals at McDonald’s. Customers not choosing McDonald’s during lunch/dinner hours may start eating at McDonald’s. Retaining current customers and adding new ones, McDonald’s revenues will increase overall. ‹#› ‹#› © 2019 McGraw-Hill Education. Transposition While direct proofs are sufficient to prove a point logically, an alternative approach, transposition, may be more effective Transposition Is the equivalence between the statements “If A, then B― and “If not B, then A― Any time a group of assumptions implies a conclusion, then it is also true that any time the conclusion does not hold, then at least one of the assumptions must not hold ‹#› ‹#› © 2019 McGraw-Hill Education. Transposition “If A, then B†AND “If not B, then not Aâ€
- 6. ASSUMPTIONS (A) CONCLUSIONS (B) ‹#› ‹#› © 2019 McGraw-Hill Education. Transposition: A Mathematical Approach Prove the statement: If X2 is even, then X is even Suppose X is not an even number; it is instead an odd number If X is an odd number, then X= (2K +1), where K is an integer X2 = (2K+1)2 = 4K2 + 4K+1. 4K2 + 4K = 4(K2 +K) and so is divisible by 2 4K2 + 4K is an even number X2 = 4(K2 +K)+1 is an even number plus 1, meaning it is an odd number ‹#› ‹#› © 2019 McGraw-Hill Education. Transposition The statement was: If X2 is even, then X is even Using transposition, the opposite of the conclusion is used to proof the opposite of the assumption: If X is odd, then X2 is even would be incorrect Transposition can also be used without using mathematics to prove statements like “If A, then B―.
- 7. Transposition can be particularly effective if an assumption seems indisputably obvious. ‹#› ‹#› © 2019 McGraw-Hill Education. Transposition: An Example Proof the statement: “If McDonald’s stores offer breakfast all day, revenue will increase― McDonald’s stores revenues will not increase This means total revenues from current and new customers will not increase This means either there will be no new customers or revenues from current customers will decrease This means there could not have been an expansion in the menu McDonald’s stores do not offer breakfast all day ‹#› ‹#› © 2019 McGraw-Hill Education. Direct Proof and Transposition Direct Proof State assumptions Explain methods of proof (mathematics, common sense, etc.) State conclusions Transposition Assume the opposite of the conclusion Explain methods of proof (mathematics, common sense, etc.)
- 8. State assumption(s) that is (are) violated (not A) ‹#› ‹#› © 2019 McGraw-Hill Education. Deductive Reasoning Used commonly in the application of law If there is disagreement with a conclusion there are two possible sources: The method of proof, OR The assumption There are two ways of resolving disputes about assumptions Show robustness- the persistent accuracy of a conclusion despite variation in the associated assumption(s) within the context of a deductive argument Assess consistency with a collected dataset ‹#› ‹#› © 2019 McGraw-Hill Education. Empirically Testable Conclusions An empirically testable conclusion is a conclusion whose validity can be meaningfully tested using observable data. Example: A banana company’s management staffs are divided into two groups about their product’s placement in a major grocery store chain. Group 1 believes that change in current location will increase its sales. Group 2 believes that current location is good enough. ‹#›
- 9. ‹#› © 2019 McGraw-Hill Education. Empirically Testable Conclusions Company has the sales data in the current location. Company chooses to move its product to a new location and collects sales data. Now the company can meaningfully test the validity of the management’s competing conclusions. Making the actual decision about the validity of an empirically testable conclusion based on observable data is an application of inductive reasoning ‹#› ‹#› © 2019 McGraw-Hill Education. Inductive Reasoning Inductive reasoning Reasoning that goes from the specific to the general; bottom-up logic Population The entire set of potential observations about which we want to learn Data sample A subset of population that is collected and observed ‹#› ‹#› © 2019 McGraw-Hill Education. Inductive Reasoning Business regularly collect data samples to draw conclusions about the population after applying inductive reasoning.
- 10. The conclusion from inductive reasoning requires degree of support (also called inductive probability). Degree of support is also called the strength of the inductive argument. Example: if we are 50% confident about the conclusion, then the degree of support is 50%. ‹#› ‹#› © 2019 McGraw-Hill Education. Degrees of Support Two Types of Degrees of Support Both play an important role in interpreting and drawing conclusions from data analysis SUBJECTIVE DEGREE OF SUPPORT (IT IS BASED ON OPINION AND LACKING STATISTICAL FOUNDATION) OBJECTIVE DEGREE OF SUPPORT (IT HAS A STATISTICAL FOUNDATION AND THUS MORE CREDIBLE THAN SUBJECTIVE DEGREE OF SUPPORT) ‹#› ‹#› © 2019 McGraw-Hill Education. Evaluating Assumptions Through deductive reasoning, an empirically testable conclusion is made Collect a data sample Test the conclusion by comparing the observed outcomes in the data samples to their corresponding probabilities Use inductive reasoning to decide whether the conclusion passes or fails If it fails, transposition implies we must reject
- 11. If it passes, then we must not reject ‹#› ‹#› © 2019 McGraw-Hill Education. Inductive Reasoning for Evaluating Assumptions ‹#› ‹#› © 2019 McGraw-Hill Education. Selection Bias in Inductive Reasoning Improper use of inductive reasoning may lead to inaccurate, or biased conclusions Data-generating process is typically the source of the bias Survey questions constructed in a leading way Confirmation bias is the tendency to confirm a claim Predictable patterns are discovered Predictable-world bias is the tendency to find order when none exists, and occurs when people “read too much― into perceived patterns from random data ‹#› ‹#› © 2019 McGraw-Hill Education. Selection Bias Selection bias The act of drawing conclusions about a population using a selected data sample, without accounting for the means of selection There are two common types:
- 12. Collector selection bias occurs when the collector selects the members of the data sample in a systematic way Availability bias occurs when the collector of the data sample selects the members of the data sample according to what is most readily available Member selection bias occurs when potential members of the data sample self-select into, or out of, the sample ‹#› ‹#› © 2019 McGraw-Hill Education. image1.png image2.png image3.JPG