1) The document analyzes how socioeconomic region affects the development of self-esteem when accounting for age and gender interactions.
2) It finds that teenage females have significantly lower self-esteem than males across all regions, though the difference varies by region.
3) The model selection process identifies the best model as one that includes interactions between age, gender, and socioeconomic region, suggesting region influences self-esteem development.
The analysis of the data has been done using excel statistical sof.docxmattinsonjanel
The analysis of the data has been done using excel statistical software. First, the demand and popularity of each product has been analyzed using pie charts. The extracts from excel shows the distributions of the three product lines across age, sex and education. The three types of bicycles have analyzed in terms of the number of customers using them, sex, and education levels.
The low product line has the highest demand as 80 customers selected, followed by middle product line with 61 customers and finally upper product line. The following extracts shows the demand of the three bicycles on the basis of number of customers, sex, and education.
Analyzing the popularity and demand for three bicycles using sex showed that males have a higher proportion of using bicycles than females. This is show in the following extract and chart.
Also, the level of education determines the use of bicycles. The demand for bicycles varies across the different levels of education. The analysis revealed that non-college high school diploma do not use bicycles. The following pie chart shows the proportion of each education level with respect to the use of bicycles.
Education
Number of Customers
Percentage
Non-High School Diploma
0
0%
High School Diploma
2
1%
Some-College -level work
67
37%
College Degree
97
54%
Graduate Degree at work
14
8%
However, the use of the three products line varied greatly with the age of customers. The following frequency distribution table shows the age group of customers and the frequency of using the three products line.
Bin
Frequency
Cumulative %
Bin
Frequency
Cumulative %
20
10
5.56%
25
62
34.44%
25
62
40.00%
30
45
59.44%
30
45
65.00%
35
32
77.22%
35
32
82.78%
40
16
86.11%
40
16
91.67%
20
10
91.67%
45
8
96.11%
45
8
96.11%
50
7
100.00%
50
7
100.00%
More
0
100.00%
More
0
100.00%
As it can be seen from the histogram, the distribution of age of customers and the frequency on uses of bikes is negatively skewed. That is, at early ages, customers use bicycles more than old ages. At age group 20-25, the demand of bicycles is high and it decreases as age increases. The mean age, median age, mode of an average customer is showed in the following table. The table also shows the average income that most customers receive,
Mean Age
28.98889
Mode Age
25
Median Age
27
Average Income
35672.22
Median Income
34000
More analysis have been done on individual products lines in order to determine the mean age of a customer at a given product line; average salary, average miles/ week, average times/ week among other analysis. The following discussion focuses on each of the three product lines.
a) Lower Product Line.
The following analysis shows the profile of an average customer who chooses to by Low Product Line.
Mean Age
28.6
Sex
Males
55%
Females
45%
Status
Single:
36%
Married.
64%
Mean Salary
30700
Average Miles
88
Average Time/week
3.01 ...
Don't Leave Your Facilities Needs to Chance: From Game Plan to Master PlanSightlines
This presentation explores what facilities leaders can do when rolling the dice doesn't work regarding the management of deferred maintenance. You'll also learn how to:
- Maximize the value of a deferred maintenance assessment
- Integrate deferred maintenance data with a master plan
- Optimize institutional resources to mitigate risk
- Be a partner in program success rather than a follower
The analysis of the data has been done using excel statistical sof.docxmattinsonjanel
The analysis of the data has been done using excel statistical software. First, the demand and popularity of each product has been analyzed using pie charts. The extracts from excel shows the distributions of the three product lines across age, sex and education. The three types of bicycles have analyzed in terms of the number of customers using them, sex, and education levels.
The low product line has the highest demand as 80 customers selected, followed by middle product line with 61 customers and finally upper product line. The following extracts shows the demand of the three bicycles on the basis of number of customers, sex, and education.
Analyzing the popularity and demand for three bicycles using sex showed that males have a higher proportion of using bicycles than females. This is show in the following extract and chart.
Also, the level of education determines the use of bicycles. The demand for bicycles varies across the different levels of education. The analysis revealed that non-college high school diploma do not use bicycles. The following pie chart shows the proportion of each education level with respect to the use of bicycles.
Education
Number of Customers
Percentage
Non-High School Diploma
0
0%
High School Diploma
2
1%
Some-College -level work
67
37%
College Degree
97
54%
Graduate Degree at work
14
8%
However, the use of the three products line varied greatly with the age of customers. The following frequency distribution table shows the age group of customers and the frequency of using the three products line.
Bin
Frequency
Cumulative %
Bin
Frequency
Cumulative %
20
10
5.56%
25
62
34.44%
25
62
40.00%
30
45
59.44%
30
45
65.00%
35
32
77.22%
35
32
82.78%
40
16
86.11%
40
16
91.67%
20
10
91.67%
45
8
96.11%
45
8
96.11%
50
7
100.00%
50
7
100.00%
More
0
100.00%
More
0
100.00%
As it can be seen from the histogram, the distribution of age of customers and the frequency on uses of bikes is negatively skewed. That is, at early ages, customers use bicycles more than old ages. At age group 20-25, the demand of bicycles is high and it decreases as age increases. The mean age, median age, mode of an average customer is showed in the following table. The table also shows the average income that most customers receive,
Mean Age
28.98889
Mode Age
25
Median Age
27
Average Income
35672.22
Median Income
34000
More analysis have been done on individual products lines in order to determine the mean age of a customer at a given product line; average salary, average miles/ week, average times/ week among other analysis. The following discussion focuses on each of the three product lines.
a) Lower Product Line.
The following analysis shows the profile of an average customer who chooses to by Low Product Line.
Mean Age
28.6
Sex
Males
55%
Females
45%
Status
Single:
36%
Married.
64%
Mean Salary
30700
Average Miles
88
Average Time/week
3.01 ...
Don't Leave Your Facilities Needs to Chance: From Game Plan to Master PlanSightlines
This presentation explores what facilities leaders can do when rolling the dice doesn't work regarding the management of deferred maintenance. You'll also learn how to:
- Maximize the value of a deferred maintenance assessment
- Integrate deferred maintenance data with a master plan
- Optimize institutional resources to mitigate risk
- Be a partner in program success rather than a follower
MS1023 Business Statistics with Computer Applications Homework.docxrosemarybdodson23141
MS1023 Business Statistics with Computer Applications Homework #1
Maho Sonmez [email protected] 1
1. Sue Taylor, Director of Global Industrial
Sales, is concerned by a deteriorating sales
trend. Specifically, the number of industrial
customers is stable at 1,500, but they are
purchasing less each year. She orders her
staff to search for causes of the downward
trend by surveying all 1,500 industrial
customers. For this study, the set of 1,500
industrial customers is ______________.
a) a parameter
b) a sample
c) the population
d) a statistic
e) the frame
2. Sue Taylor, Director of Global Industrial
Sales, is concerned by a deteriorating sales
trend. Specifically, the number of industrial
customers is stable at 1,500, but they are
purchasing less each year. She orders her
staff to search for causes of the downward
trend by selecting a focus group of 40
industrial customers. For this study, the set
of 40 industrial customers is ________.
a) a parameter
b) a sample
c) the population
d) a statistic
e) the frame
3. Sue Taylor, Director of Global Industrial
Sales, is concerned by a deteriorating sales
trend. Specifically, the number of customers
is stable at 1,500, but they are purchasing
less each year. She orders her staff to
search for causes of the downward trend by
surveying all 1,500 industrial customers.
Sue is ordering a __________.
a) statistic from the industrial customers
b) census of the industrial customers
c) sample of the industrial customers
d) sorting of the industrial customers
e) parameter of the industrial customers
4. Sue Taylor, Director of Global Industrial
Sales, is concerned by a deteriorating sales
trend. Specifically, the number of customers
is stable at 1,500, but they are purchasing
less each year.
She orders her staff to search for causes of
the downward trend by surveying all 1,500
industrial customers. One question on the
survey asked the customers: “Which of the
following best describes your primary
business: a. manufacturing, b. wholesaler,
c. retail, d. service.” The measurement
level for this question is
_________________.
a) interval level
b) ordinal level
c) nominal level
d) ratio level
e) relative level
5. Which scale of measurement has these
two properties: linear distance is meaningful
and the location of origin (or zero point) is
arbitrary?
a) Interval level
b) Ordinal level
c) Nominal level
d) Ratio level
e) Minimal level
6. Which scale of measurement has these
two properties: linear distance is
meaningful and the location of origin (or
zero point) is absolute (or natural)?
a) Interval level
b) Ordinal level
c) Nominal level
d) Ratio level
e) Relative level
7. Which of the following operations is
meaningful for processing nominal data?
a) Addition
b) Multiplication
c) Ranking
d) Counting
e) Division
MS1023 Business Statistics with Computer Applications Homework #1
Maho Sonmez [email pr.
Introduction to Statistics Part A - Outputs 1. A sa.docxmariuse18nolet
Introduction to Statistics
Part A - Outputs:
1. A sample of university employees had been assembled. An output
concerning their education is presented below. Study the output and
answer the following questions:
a. What is the mean, median and the standard deviation?
b. Firstly, what is the interquartile range, and secondly, how many education years has
the most educated employee?
c. what is the number of education years that 36% of the sample has that number of
education years or below it? Answer the same question for 83%.
d. Firstly, how many employees has 12 education years or less? Secondly, how many
employees have missing values in the variable education years?
Statistics
Highest Year of School Completed
1510
7
12.88
12.00
12
2.984
8.904
20
0
20
19455
9.00
12.00
12.00
12.00
15.00
16.00
Valid
Missing
N
Mean
Median
Mode
St d. Dev iation
Variance
Range
Minimum
Maxim um
Sum
10
25
36
50
75
83
Percentiles
2. Researcher which studies the field of eating disorders checked the
correlation between subjects body image ranking and the degree to
which they restrict food intake. Study the output and answer the
following questions:
Highest Year of School Completed
2 .1 .1 .1
5 .3 .3 .5
5 .3 .3 .8
6 .4 .4 1.2
12 .8 .8 2.0
25 1.6 1.7 3.6
68 4.5 4.5 8.1
56 3.7 3.7 11.9
73 4.8 4.8 16.7
85 5.6 5.6 22.3
461 30.4 30.5 52.8
130 8.6 8.6 61.5
175 11.5 11.6 73.0
73 4.8 4.8 77.9
194 12.8 12.8 90.7
43 2.8 2.8 93.6
45 3.0 3.0 96.6
22 1.5 1.5 98.0
30 2.0 2.0 100.0
1510 99.5 100.0
7 .5
1517 100.0
0
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Total
Valid
NAMissing
Total
Frequency Percent Valid Percent
Cumulat iv e
Percent
Correlati ons
1 .383**
.000
217 217
.383** 1
.000
217 217
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Body perception
Restriction of f ood
intake (f asting)
Body
perception
Restriction of
f ood intake
(f asting)
Correlation is signif icant at the 0.01 lev el (2-t ailed).**.
1. What can be inferred from the table?
a. There is a significant negative correlation between the variables.
b. There is a moderately negative correlation between the variables.
c. There is a moderately positive correlation between the variables.
d. There is a weak positive correlation between the variables.
2. Report the correlation.
3. The following output present a regression done in order to predict
the number of SMS messages a person sends to his\hers partner by the
length of the relationship (in years). Study the output and answer the
following questions:
Regression
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 -.661(a) .436 .435 123.540
a Predictors: (Constant), Duration of relationship (years)
ANOVA(b)
.
Presentation by Fulbright Professor Noreen McDonald (sabbatical at the Institute for Transport Studies), October 2015.
www.its.leeds.ac.uk/about/events/seminar-series
Creating Better Customer Experiences Online (with Top Tasks) presented by Ger...Patrick Van Renterghem
Creating Better Customer Experiences Online (with Top Tasks) presented by @GerryMcGovern on Dec. 4th, 2013 @itworks. Interesting for Web (Internet, Intranet, portals) designers, content managers, communication officers, marketing departments, ...
Presentation given at the OECD Gender Budgeting Experts Meeting, Vienna, Austria. 18-19 June 2018
For more information see http://www.oecd.org/gov/budgeting/gender-budgeting-experts-meeting-2018.htm
In this report you will find preliminary data of some of the top highlight questions of the survey. This does not represent all the data, but a simple preview. The survey will remain open until the official end date of the survey in April 2016 as we prepare our data for key events and publications in May 2016 and onwards.
MS1023 Business Statistics with Computer Applications Homework.docxrosemarybdodson23141
MS1023 Business Statistics with Computer Applications Homework #1
Maho Sonmez [email protected] 1
1. Sue Taylor, Director of Global Industrial
Sales, is concerned by a deteriorating sales
trend. Specifically, the number of industrial
customers is stable at 1,500, but they are
purchasing less each year. She orders her
staff to search for causes of the downward
trend by surveying all 1,500 industrial
customers. For this study, the set of 1,500
industrial customers is ______________.
a) a parameter
b) a sample
c) the population
d) a statistic
e) the frame
2. Sue Taylor, Director of Global Industrial
Sales, is concerned by a deteriorating sales
trend. Specifically, the number of industrial
customers is stable at 1,500, but they are
purchasing less each year. She orders her
staff to search for causes of the downward
trend by selecting a focus group of 40
industrial customers. For this study, the set
of 40 industrial customers is ________.
a) a parameter
b) a sample
c) the population
d) a statistic
e) the frame
3. Sue Taylor, Director of Global Industrial
Sales, is concerned by a deteriorating sales
trend. Specifically, the number of customers
is stable at 1,500, but they are purchasing
less each year. She orders her staff to
search for causes of the downward trend by
surveying all 1,500 industrial customers.
Sue is ordering a __________.
a) statistic from the industrial customers
b) census of the industrial customers
c) sample of the industrial customers
d) sorting of the industrial customers
e) parameter of the industrial customers
4. Sue Taylor, Director of Global Industrial
Sales, is concerned by a deteriorating sales
trend. Specifically, the number of customers
is stable at 1,500, but they are purchasing
less each year.
She orders her staff to search for causes of
the downward trend by surveying all 1,500
industrial customers. One question on the
survey asked the customers: “Which of the
following best describes your primary
business: a. manufacturing, b. wholesaler,
c. retail, d. service.” The measurement
level for this question is
_________________.
a) interval level
b) ordinal level
c) nominal level
d) ratio level
e) relative level
5. Which scale of measurement has these
two properties: linear distance is meaningful
and the location of origin (or zero point) is
arbitrary?
a) Interval level
b) Ordinal level
c) Nominal level
d) Ratio level
e) Minimal level
6. Which scale of measurement has these
two properties: linear distance is
meaningful and the location of origin (or
zero point) is absolute (or natural)?
a) Interval level
b) Ordinal level
c) Nominal level
d) Ratio level
e) Relative level
7. Which of the following operations is
meaningful for processing nominal data?
a) Addition
b) Multiplication
c) Ranking
d) Counting
e) Division
MS1023 Business Statistics with Computer Applications Homework #1
Maho Sonmez [email pr.
Introduction to Statistics Part A - Outputs 1. A sa.docxmariuse18nolet
Introduction to Statistics
Part A - Outputs:
1. A sample of university employees had been assembled. An output
concerning their education is presented below. Study the output and
answer the following questions:
a. What is the mean, median and the standard deviation?
b. Firstly, what is the interquartile range, and secondly, how many education years has
the most educated employee?
c. what is the number of education years that 36% of the sample has that number of
education years or below it? Answer the same question for 83%.
d. Firstly, how many employees has 12 education years or less? Secondly, how many
employees have missing values in the variable education years?
Statistics
Highest Year of School Completed
1510
7
12.88
12.00
12
2.984
8.904
20
0
20
19455
9.00
12.00
12.00
12.00
15.00
16.00
Valid
Missing
N
Mean
Median
Mode
St d. Dev iation
Variance
Range
Minimum
Maxim um
Sum
10
25
36
50
75
83
Percentiles
2. Researcher which studies the field of eating disorders checked the
correlation between subjects body image ranking and the degree to
which they restrict food intake. Study the output and answer the
following questions:
Highest Year of School Completed
2 .1 .1 .1
5 .3 .3 .5
5 .3 .3 .8
6 .4 .4 1.2
12 .8 .8 2.0
25 1.6 1.7 3.6
68 4.5 4.5 8.1
56 3.7 3.7 11.9
73 4.8 4.8 16.7
85 5.6 5.6 22.3
461 30.4 30.5 52.8
130 8.6 8.6 61.5
175 11.5 11.6 73.0
73 4.8 4.8 77.9
194 12.8 12.8 90.7
43 2.8 2.8 93.6
45 3.0 3.0 96.6
22 1.5 1.5 98.0
30 2.0 2.0 100.0
1510 99.5 100.0
7 .5
1517 100.0
0
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Total
Valid
NAMissing
Total
Frequency Percent Valid Percent
Cumulat iv e
Percent
Correlati ons
1 .383**
.000
217 217
.383** 1
.000
217 217
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Body perception
Restriction of f ood
intake (f asting)
Body
perception
Restriction of
f ood intake
(f asting)
Correlation is signif icant at the 0.01 lev el (2-t ailed).**.
1. What can be inferred from the table?
a. There is a significant negative correlation between the variables.
b. There is a moderately negative correlation between the variables.
c. There is a moderately positive correlation between the variables.
d. There is a weak positive correlation between the variables.
2. Report the correlation.
3. The following output present a regression done in order to predict
the number of SMS messages a person sends to his\hers partner by the
length of the relationship (in years). Study the output and answer the
following questions:
Regression
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 -.661(a) .436 .435 123.540
a Predictors: (Constant), Duration of relationship (years)
ANOVA(b)
.
Presentation by Fulbright Professor Noreen McDonald (sabbatical at the Institute for Transport Studies), October 2015.
www.its.leeds.ac.uk/about/events/seminar-series
Creating Better Customer Experiences Online (with Top Tasks) presented by Ger...Patrick Van Renterghem
Creating Better Customer Experiences Online (with Top Tasks) presented by @GerryMcGovern on Dec. 4th, 2013 @itworks. Interesting for Web (Internet, Intranet, portals) designers, content managers, communication officers, marketing departments, ...
Presentation given at the OECD Gender Budgeting Experts Meeting, Vienna, Austria. 18-19 June 2018
For more information see http://www.oecd.org/gov/budgeting/gender-budgeting-experts-meeting-2018.htm
In this report you will find preliminary data of some of the top highlight questions of the survey. This does not represent all the data, but a simple preview. The survey will remain open until the official end date of the survey in April 2016 as we prepare our data for key events and publications in May 2016 and onwards.