2. Using our sample data, construct a 95 confidence interval for th.pdf

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A 95% confidence interval for the mean salary difference between genders in the sample data is calculated as $4.77 to $25.35. A two-sample t-test indicates the means are not equal, as the p-value is 0.005. Using a two-sample t-test is better than two one-sample tests when comparing samples, as it directly tests if a difference exists between the sample means rather than just providing individual confidence intervals.

Education

2. Using our sample data, construct a 95% confidence interval for the mean salary difference
between the genders in the population. How does this compare to the findings in week 2,
question 2?
Difference
St Err.
T value
Low
to
High
Can the means be equal? Yes or No. Why?
Why is using a two sample tool (t-test, confidence interval) a better choice than using 2 one-
sample techniques when comparing two samples?
ID
Salary
Compa
Midpoint
Age
Performance Rating
Service
Gender
Raise
Degree
Gender1
Gr
1
66.1
1.159
57
34
85
8
0
5.7
0
M

E
2
25.9
0.834
31
52
80
7
0
3.9
0
M
B
3
35.2
1.135
31
30
75
5
1
3.6
1
F
B
4
55.3
0.971
57
42
100
16
0
5.5
1
M

E
5
49.6
1.033
48
36
90
16
0
5.7
1
M
D
6
78.3
1.168
67
36
70
12
0
4.5
1
M
F
7
42.3
1.058
40
32
100
8
1
5.7
1
F

C
8
22.8
0.990
23
32
90
9
1
5.8
1
F
A
9
78
1.164
67
49
100
10
0
4
1
M
F
10
23.3
1.014
23
30
80
7
1
4.7
1
F

A
11
23.6
1.025
23
41
100
19
1
4.8
1
F
A
12
60.8
1.067
57
52
95
22
0
4.5
0
M
E
13
40.6
1.014
40
30
100
2
1
4.7
0
F

C
14
21.7
0.943
23
32
90
12
1
6
1
F
A
15
21.8
0.949
23
32
80
8
1
4.9
1
F
A
16
37.4
0.934
40
44
90
4
0
5.7
0
M

C
17
57
1.000
57
27
55
3
1
3
1
F
E
18
33.5
1.081
31
31
80
11
1
5.6
0
F
B
19
23
1.000
23
32
85
1
0
4.6
1
M

A
20
36
1.162
31
44
70
16
1
4.8
0
F
B
21
76
1.135
67
43
95
13
0
6.3
1
M
F
22
43.7
0.911
48
48
65
6
1
3.8
1
F

D
23
25.3
1.098
23
36
65
6
1
3.3
0
F
A
24
48.9
1.019
48
30
75
9
1
3.8
0
F
D
25
25.8
1.122
23
41
70
4
0
4
0
M

A
26
23.3
1.013
23
22
95
2
1
6.2
0
F
A
27
42.3
1.057
40
35
80
7
0
3.9
1
M
C
28
75.2
1.122
67
44
95
9
1
4.4
0
F

F
29
80.9
1.208
67
52
95
5
0
5.4
0
M
F
30
49
1.020
48
45
90
18
0
4.3
0
M
D
31
24.2
1.054
23
29
60
4
1
3.9
1
F

A
32
27.5
0.886
31
25
95
4
0
5.6
0
M
B
33
63.6
1.115
57
35
90
9
0
5.5
1
M
E
34
28.6
0.922
31
26
80
2
0
4.9
1
M

B
35
22.4
0.976
23
23
90
4
1
5.3
0
F
A
36
23.6
1.026
23
27
75
3
1
4.3
0
F
A
37
24.3
1.057
23
22
95
2
1
6.2
0
F

A
38
63
1.105
57
45
95
11
0
4.5
0
M
E
39
34.8
1.123
31
27
90
6
1
5.5
0
F
B
40
24.3
1.057
23
24
90
2
0
6.3
0
M

A
41
42.8
1.071
40
25
80
5
0
4.3
0
M
C
42
23
0.998
23
32
100
8
1
5.7
1
F
A
43
75.4
1.125
67
42
95
20
1
5.5
0
F

F
44
60.7
1.065
57
45
90
16
0
5.2
1
M
E
45
57.9
1.206
48
36
95
8
1
5.2
1
F
D
46
62.2
1.091
57
39
75
20
0
3.9
1
M

E
47
62.2
1.091
57
37
95
5
0
5.5
1
M
E
48
70.1
1.230
57
34
90
11
1
5.3
1
F
E
49
61.7
1.083
57
41
95
21
0
6.6
0
M

E
50
61.4
1.077
57
38
80
12
0
4.6
0
M
E
Difference
St Err.
T value
Low
to
High
Solution
Two-sample T for Salary
Gender N Mean StDev SE Mean
0 25 52.3 18.6 3.7
1 25 37.2 17.5 3.5
Difference = mu (0) - mu (1)
Estimate for difference: 15.06
95% CI for difference: (4.77, 25.35)
T-Test of difference = 0 (vs not =): T-Value = 2.94 P-Value = 0.005 DF = 48
Difference = 15.06
standard error of difference between means = 5.107
T value = 2.94
Low = 4.77

High =25.35
No, The means are not equal . ( since P-value is very low i.e. 0005)
We use two sample t-test to the average salary differences between the two genders. This gives
whether there is truly difference exists between the genders . If we use one sample individual
test, then this test gives the individual averages confidence interval , from which we can't
interpret the difference of their averages.

This document describes performing a regression analysis using compa as the dependent variable and the same independent variables as a previous analysis. It provides the regression hypotheses and asks to interpret the findings, including the F statistic value, its p-value and whether it is significant. It also asks for the coefficient p-values, whether they are significant, and which variables are significant. It concludes by asking for the regression equation using only significant variables and whether gender is a significant factor in determining compa.

week 1 ScoreWeek 1.Measurement and Description - chapters 1 and 2.docx

melbruce90096

week 1 Score:Week 1.Measurement and Description - chapters 1 and 2<1 point>1Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?<1 point>2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: overall sample, Females, and Males.You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =stdev functions. (the range must be found using the difference between the =max and =min functions with Fx) functions.Note: Place data to the right, if you use Descriptive statistics, place that to the right as well.SalaryCompaAgePerf. Rat.ServiceOverallMeanStandard DeviationRangeFemaleMeanStandard DeviationRangeMaleMeanStandard DeviationRange<1 point>3What is the probability for a:Probabilitya. Randomly selected person being a male in grade E?b. Randomly selected male being in grade E? Note part b is the same as given a male, what is probabilty of being in grade E?c. Why are the results different?<1 point>4For each group (overall, females, and males) find:OverallFemaleMalea.The value that cuts off the top 1/3 salary in each group.Hint: can use these Fx functionsb.The z score for each value:Excel's standize functionc.The normal curve probability of exceeding this score:1-normsdist functiond.What is the empirical probability of being at or exceeding this salary value?e.The value that cuts off the top 1/3 compa in each group.f.The z score for each value:g.The normal curve probability of exceeding this score:h.What is the empirical probability of being at or exceeding this compa value?i.How do you interpret the relationship between the data sets? What do they mean about our equal pay for equal work question?<2 points>5. What conclusions can you make about the issue of male and female pay equality? Are all of the results consistent? What is the difference between the sal and compa measures of pay?Conclusions from looking at salary results:Conclusions from looking at compa results:Do both salary measures show the same results?Can we make any conclusions about equal pay for equal work yet? dataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1Gr164.41.13573485805.70METhe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 227.40.884315280703.90MBNote: to simplfy the a.

Inferantial statistic presentation about two sample t test

salmankhan9322010

DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx

theodorelove43763

DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.157.71.012573485805.70METhe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 227.80.897315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.3341.096313075513.61FB459.21.03857421001605.51METhe column labels in the table mean:549.51.0314836901605.71MDID – Employee sample number Salary – Salary in thousands 675.71.1306736701204.51MFAge – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)741.71.0434032100815.71FCService – Years of service (rounded)Gender – 0 = male, 1 = female 823.41.018233290915.81FAMidpoint – salary grade midpoint Raise – percent of last raise980.81.206674910010041MFGrade – job/pay gradeDegree (0= BS\BA 1 = MS)1023.61.027233080714.71FAGender1 (Male or Female)Compa - salary divided by midpoint1123.61.02423411001914.81FA1266.91.1745752952204.50ME1341.61.0414030100214.70FC1421.50.93623329012161FA1524.41.059233280814.91FA16390.975404490405.70MC1768.81.2075727553131FE1834.91.1263131801115.60FB1923.21.008233285104.61MA20361.1603144701614.80FB2175.31.1246743951306.31MF2256.71.182484865613.81FD2322.60.984233665613.30FA2451.51.072483075913.80FD2525.51.1092341704040MA2622.90.994232295216.20FA2743.51.088403580703.91MC2874.41.111674495914.40FF2973.51.097675295505.40MF3045.70.9524845901804.30MD3123.71.031232960413.91FA3226.90.867312595405.60MB3355.10.967573590905.51ME34280.904312680204.91MB3521.90.953232390415.30FA3623.71.032232775314.30FA3723.21.010232295216.20FA3857.61.0105745951104.50ME3934.31.108312790615.50FB4024.41.062232490206.30MA4140.51.012402580504.30MC4223.31.0122332100815.71FA4377.21.1526742952015.50FF4456.90.9995745901605.21ME4557.71.202483695815.21FD4665.41.1485739752003.91ME4756.80.997573795505.51ME4859.71.0485734901115.31FE4962.41.0955741952106.60ME5056.50.9925738801204.60ME Week 1Week 1.Measurement and Description - chapters 1 and 2The goal this week is to gain an understanding of our data set - what kind of data we are looking at, some descriptive measurse, and a look at how the data is distributed (shape).1Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, .

MARKETING MANAGEMENT PHILOSOPHIESCHAPTER 1 - ASSIGNMENTQuest.docx

infantsuk

MARKETING MANAGEMENT PHILOSOPHIES CHAPTER 1 - ASSIGNMENT Question 1. Considering the differences of the philosophies, in some cases slight differences, select a company (product or service) and describe the current philosophy they pose for the customer. Include in your comments the level of customer value delivered by the company’s actions. In other words, measure the company’s interaction with their customers against the Market Concept Philosophy. Does the company operate under the Market Concept Philosophy or do they lean more toward one of the other Philosophies. Be specific with your examples. DataSee comments at the right of the data set.IDSalaryCompaMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1Grade8231.000233290915.80FAThe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10220.956233080714.70FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.11231.00023411001914.80FA14241.04323329012160FAThe column labels in the table mean:15241.043233280814.90FAID – Employee sample number Salary – Salary in thousands 23231.000233665613.31FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)26241.043232295216.21FAService – Years of service (rounded)Gender: 0 = male, 1 = female 31241.043232960413.90FAMidpoint – salary grade midpoint Raise – percent of last raise35241.043232390415.31FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)36231.000232775314.31FAGender1 (Male or Female)Compa - salary divided by midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.870315280703.90MB32280.903312595405.60MB34280.903312680204.91MB16471.175404490405.70MC27401.000403580703.91MC41431.075402580504.30MC5470.9794836901605.71MD30491.0204845901804.30MD1581.017573485805.70ME4661.15757421001605.51ME12601.0525752952204.50ME33641.122573590905.51ME38560.9825745951104.50ME44601.0525745901605.21ME46651.1405739752003.91ME47621.087573795505.51ME49601.0525741952106.60ME50661.1575738801204.60ME6761.1346736701204.51MF9771.149674910010041MF21761.1346743951306.31MF29721.074675295505.40MF Week 1Week 1.Measurement and Description - chapters 1 and 21Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variabl ...

DataIDSalaryCompa-ratioMidpoint AgePerformance RatingServiceGender.docx

simonithomas47935

DataIDSalaryCompa-ratioMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GradeDo not manipuilate Data set on this page, copy to another page to make changes163.21.108573485805.70METhe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 227.10.873315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.335.31.138313075513.61FB461.41.07857421001605.51METhe column labels in the table mean:546.90.9784836901605.71MDID – Employee sample number Salary – Salary in thousands 674.61.1136736701204.51MFAge – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)740.81.0194032100815.71FCService – Years of service (rounded)Gender – 0 = male, 1 = female 823.81.035233290915.81FAMidpoint – salary grade midpoint Raise – percent of last raise974.21.108674910010041MFGrade – job/pay gradeDegree (0= BS\BA 1 = MS)1023.41.017233080714.71FAGender1 (Male or Female)Compa-ratio - salary divided by midpoint1122.30.97123411001914.81FA1264.61.1345752952204.50ME1340.61.0164030100214.70FC14230.99823329012161FA1525.21.094233280814.91FA1645.71.143404490405.70MC1770.21.2315727553131FE1834.71.1193131801115.60FB1923.91.039233285104.61MA2033.51.0813144701614.80FB21711.0606743951306.31MF2252.91.103484865613.81FD2322.10.960233665613.30FA2456.81.183483075913.80FD2524.31.0562341704040MA2624.61.071232295216.20FA2743.41.084403580703.91MC28771.149674495914.40FF2974.71.115675295505.40MF3047.80.9954845901804.30MD3120.70.898232960413.91FA3228.60.921312595405.60MB3359.21.038573590905.51ME3427.30.881312680204.91MB3522.90.996232390415.30FA3622.70.987232775314.30FA3723.91.037232295216.20FA3864.71.1355745951104.50ME39351.128312790615.50FB4023.61.024232490206.30MA4146.61.166402580504.30MC4223.31.0152332100815.71FA4376.41.1406742952015.50FF4461.21.0745745901605.21ME45511.062483695815.21FD4658.81.0315739752003.91ME4766.91.174573795505.51ME4870.71.2405734901115.31FE4963.51.1145741952106.60ME5064.51.1325738801204.60ME Week 1Week 1: Descriptive Statistics, including ProbabilityWhile the lectures will examine our equal pay question from the compa-ratio viewpoint, our weekly assignments will focus onexamining the issue using the salary measure.The purpose of this assignmnent is two fold:1. Demonstrate mastery with Excel tools.2. Develop descriptive statistics to help examine the question.3. Interpret descriptive outcomesThe first issue in examining salary data to determine if we - as a company - are paying males and females equally for doing equal work is to develop somedescriptive statistics to give us something to make a preliminary decision on whether we have an issue or not.1Descriptive Statistics: Develop basic descriptive statistics for SalaryThe first step in analyzing data sets is to find some summary descriptive statistics for key variables. Suggestion: Copy the gender1 and salary columns from the Data tab to co.

MGT 340 help A Guide to career/Snaptutorial

williamtrumpz2F

MGT 340 help Successful Learning/Snaptutorial

williamtrumpz4v

The human resource manager was asked to study differences in leadership styles between male and female managers. Survey data was collected from 10 male and 10 female managers. The document provides the raw survey scores and instructs the manager to analyze the data, draw conclusions about differences in leadership styles, and develop a training program to address any deficiencies found. It also provides salary data for female and male HR directors and instructs the manager to conduct a t-test to determine if salary differences are statistically significant.

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Multivariate Techniques

Terry Chaney

1. The document describes using binary logistic regression models to analyze whether age discrimination factored into employee termination decisions. 2. Regressing job termination on just age showed age was a significant predictor, but adding performance reduced the effect of age, showing omitted variable bias. 3. The best model used "real performance" that removed the effect of age from performance scores. Both age and real performance significantly predicted termination, with probability of firing increasing 23% per 10 additional years of age and decreasing 8.8% per higher performance score.

Chi-square tests are great to show if distributions differ or i.docx

MARRY7

Chi-square tests are great to show if distributions differ or if two variables interact in producing outcomes. What are some examples of variables that you might want to check using the chi-square tests? What would these results tell you? DataSee comments at the right of the data set.IDSalaryCompaMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1Grade8231.000233290915.80FAThe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10220.956233080714.70FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.11231.00023411001914.80FA14241.04323329012160FAThe column labels in the table mean:15241.043233280814.90FAID – Employee sample number Salary – Salary in thousands 23231.000233665613.31FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)26241.043232295216.21FAService – Years of service (rounded)Gender: 0 = male, 1 = female 31241.043232960413.90FAMidpoint – salary grade midpoint Raise – percent of last raise35241.043232390415.31FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)36231.000232775314.31FAGender1 (Male or Female)Compa - salary divided by midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.870315280703.90MB32280.903312595405.60MB34280.903312680204.91MB16471.175404490405.70MC27401.000403580703.91MC41431.075402580504.30MC5470.9794836901605.71MD30491.0204845901804.30MD1581.017573485805.70ME4661.15757421001605.51ME12601.0525752952204.50ME33641.122573590905.51ME38560.9825745951104.50ME44601.0525745901605.21ME46651.1405739752003.91ME47621.087573795505.51ME49601.0525741952106.60ME50661.1575738801204.60ME6761.1346736701204.51MF9771.149674910010041MF21761.1346743951306.31MF29721.074675295505.40MF Week 1Week 1.Measurement and Description - chapters 1 and 21Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: ...

Mgt 340 Success Begins / snaptutorial.com

WilliamsTaylor32

The CEO of your company has asked you, the human resource manager, to conduct a study to determine whether or not male managers and female managers perceive leadership styles differently and, if differences are found, to develop a training program that will benefit gender differences in leadership. To this end you selected, randomly, 10 male managers and 10 female managers to participate in your study. To each manager a leadership style survey questionnaire was administered. The questionnaire chosen was the Blake & Mouton Managerial Grid as it assesses leadership behavior. The numeric raw data results of the 18 question study are as follows: Raw Numeric Questionnaire Data Question

MGT 340 Massive Success / snaptutorial.com

Stephenson173

BUSI 620Questions for Critical Thinking 3Salvatore’s Chapter.docx

humphrieskalyn

BUSI 620 Questions for Critical Thinking 3 Salvatore’s Chapter 6: a. Discussion Questions: 1, 7, and 15. b. Problems: 7 and appendix problems 1 and 3 (pp. 256–257). Note: 1. Revised P7: Just construct the diffusion index from month 2 to 3. In this problem, we have three leading indicators. The diffusion index from month 1 to 2 is 66.7 (=2/3) because two indicators move up and move down (see p. 236). 2. Appendix problem 1: Delete “Eliminating the data for 2000.” You need to calculate the moving average forecasts and RMSEs for year 2000, not the whole data period. 3. Appendix problem 3: Compare RMSEs for moving average and exponential forecasts to answer “Is this a better forecast than the moving average” (see also p. 234)? Use 166.63, the mean of all 36 months, as the initial forecast for Jan. 1998 for both exponential smoothing forecasts. Salvatore’s Chapter 7: a. Discussion Questions: 3, 11, and 12. b. Problems: 4, 12, and 13. Note: 1. P4: Ms. Smith should hire workers as long as their marginal revenue product (MRP) exceeds their marginal resource cost (MRC) and until MRP=MRC. MRP=MR x MP = P x MP = $10 x MP (use information in the problem to calculate MP). MRC=wages=$40. 2. P12(a): Calculate Q when L=1and K=1, and L=2 and K=2. Then compare and answer the question about the returns to scale. 3. P12(b): Given K=1, show the change in Q if L changes from 1 to 2 and 2 to 3. Answer the question about diminishing returns. 4. P13(a): See figure (7-4) on page 276. DataSee comments at the right of the data set.IDSalaryCompaMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1Grade8231.000233290915.80FAThe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10220.956233080714.70FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.11231.00023411001914.80FA14241.04323329012160FAThe column labels in the table mean:15241.043233280814.90FAID – Employee sample number Salary – Salary in thousands 23231.000233665613.31FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)26241.043232295216.21FAService – Years of service (rounded)Gender: 0 = male, 1 = female 31241.043232960413.90FAMidpoint – salary grade midpoint Raise – percent of last raise35241.043232390415.31FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)36231.000232775314.31FAGender1 (Male or Female)Compa - salary divided by midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.870315280703.90MB32280.903312595405.60MB34280.903312680204.91 ...

BUS 308 Week 2 Quiz (Statistics For Managers - entirecourse.com)

John Sperling

This document contains a 10 question quiz for the BUS 308 Week 2 course. The questions cover topics related to statistical tests including the differences between the one-sample t-test and z-test, types of decision errors, defining alternate hypotheses, statistical significance, calculating standard errors of the mean, comparing variability of sample means versus individual scores, assumptions of the z-test, advantages of one-tailed versus two-tailed tests, and the definition of standard error of the mean. The document provides the question and possible multiple choice answers for each item.

Descriptive Statistics, Numerical Description

getyourcheaton

This document discusses descriptive statistics and numerical measures used to describe data sets. It introduces measures of central tendency including the mean, median, and mode. The mean is the average value calculated by summing all values and dividing by the number of values. The median is the middle value when values are arranged in order. The mode is the most frequently occurring value. The document also discusses measures of dispersion like range and standard deviation which describe how spread out the data is. Examples are provided to demonstrate calculating the mean, median and other descriptive statistics.

Final Exam Due Friday, Week EightInstructions Each response is.docx

mydrynan

Final Exam Due Friday, Week Eight Instructions: Each response is worth a maximum of 50 points. Number and state the question. Space and then give your response. Each response will be a minimum 175 and maximum 225 words. Utilize the book, at least one resource beyond the course book and your personal examples in each response. This means that beyond the course text you should have a minimum of 4 references for this midterm exam. Create appropriate headings/subheading for each response and then give your detailed answer to the question. Use Word doc. or docx only. Times New Roman, 12 font and double-space everything. Include a cover page and reference page. Return exam via email attachment NLT. Follow APA 6th ed. formatting requirements. NO LATE EXAMS WILL BE ACCEPTED! 1. In determining rates of pay, how are the decisions made as to who gets paid $7.00 per hour and who will receive $1 million for a years work? 2. How can team effort result in improved organizational profitability? 3. What is reverse discrimination and how does it influence the design of an executive compensation plan? 4. With benefits consuming approximately 40 percent of the compensation dollar of most organizations, among many managers the “fringe benefit” concept still exists. Develop a detailed outline of the various components of a typically benefits program, indicating their importance and scope. Develop the outline so that it can be used for a presentation. (This response length may vary, so ensure you meet the requirements as stated.) DataSee comments at the right of the data set.IDSalaryCompaMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1Grade8231.000233290915.80FAThe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10220.956233080714.70FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.11231.00023411001914.80FA14241.04323329012160FAThe column labels in the table mean:15241.043233280814.90FAID – Employee sample number Salary – Salary in thousands 23231.000233665613.31FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)26241.043232295216.21FAService – Years of service (rounded)Gender: 0 = male, 1 = female 31241.043232960413.90FAMidpoint – salary grade midpoint Raise – percent of last raise35241.043232390415.31FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)36231.000232775314.31FAGender1 (Male or Female)Compa - salary divided by midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.87 ...

Stat170 - Introductory Statistics Semester 2, 2015 Assignmen.docx

dessiechisomjj4

Stat170 - Introductory Statistics Semester 2, 2015 Assignment 2 Instructions: 1. Type your answers directly into this document. 2. The answers to all questions are to be word processed. You can either type formulae into your solution or you can use the equation editor in Word or you may include hand-written equations and diagrams by photographing them so that you have the image saved as a picture file and then pasting (inserting) the image/s into your solution. 3. Your assignment should be uploaded as a Word (.doc or .docx) or PDF file (created from a Word processed document) ONLY. Other formats, including a PDF created from an image, will not be accepted by the system. Information: Q1 Q2 Q3 Q4 Total 10 13 10 17 50 1) Question 1 (10 marks) Include an appropriate diagram for each part of this question. You can sketch these diagrams and paste in a photo of your sketch, along with your solutions. a. BranCrunch is a new breakfast cereal. Boxes of BranCrunch are labelled ‘ 675 grams’ but there is some variation. The actual mean weight is 675 grams with a standard deviation of 21 grams. i. Dan’s Discount Store sells BranCrunch in mega-packs of 8 boxes. Assuming the weights of boxes of BranCrunch are normally distributed, find the probability that the average weight of a mega-pack of BranCrunch is higher than 665 grams. ii. Louie’s Convenience Store receives a shipment of 30 boxes of BranCrunch. Louie’s will complain to the manufacturers if the total weight of this shipment is lower than 20 kg. Find the probability that Louie’s Convenience Store will complain about the shipment and explain why the information about the weights of BranCrunch following a normal distribution is not necessary to answer this part of the question. b. In 2014 the Department of Social Services reported that 32% of current marriages in Australia were expected to end in divorce. Find the probability that more than 8 marriages out of a random sample of 20 marriages which were current in 2014 would end in divorce. Question 2 (13 marks) Lean body mass is the amount of weight carried on the body that is not fat. Metabolic rate is the rate at which the body consumes energy. The following Minitab output was constructed using data recorded in a fitness study which was designed to compare the lean body masses and also the metabolic rates of adolescent males and adolescent females. Use this output to answer the questions which follow. Two-sample T for LeanBodyMass Sex N Mean StDev SE Mean Male 75 52.63 6.66 0.77 Female 75 43.42 6.05 0.70 Difference = μ (Male) - μ (Female) Estimate for difference: 9.21 95% CI for difference: (****, ****) T-Test of difference = 0 (vs ≠): T-Value = **** P-Value = **** DF = **** Both use Pooled StDev = 6.3633 Two-sample T for MetRate SE Sex N Mean StDev Mean Male 75 1626 227 26 Female 75 1258 172.

Bus 308 Effective Communication - snaptutorial.com

HarrisGeorg10

BUS 308 Education Organization - snaptutorial.com

donaldzs179

Problem Set Week OneAll statistical calculations will use Employ.docx

wkyra78

Problem Set Week One All statistical calculations will use Employee Salary Data Set 1. Using the Excel Analysis ToolPak function descriptive statistics, generate and show the descriptive statistics for each appropriate variable in the sample data set. 1. For which variables in the data set does this function not work correctly for? Why? 2. Sort the data by Gen or Gen 1 (into males and females) and find the mean and standard deviation for each gender for the following variables: 1. sal, compa, age, sr and raise. Use either the descriptive stats function or the Fx functions (average and stdev). 3. What is the probability for a: 1. Randomly selected person being a male in grade E? 2. Randomly selected male being in grade E? 3. Why are the results different? 4. Find: 1. The z score for each male salary, based on only the male salaries. 2. The z score for each female salary, based on only the female salaries. 3. The z score for each female compa, based on only the female compa values. 4. The z score for each male compa, based on only the male compa values. 5. What do the distributions and spread suggest about male and female salaries? 6. Why might we want to use compa to measure salaries between males and females? 5. Based on this sample, what conclusions can you make about the issue of male and female pay equality? 6. Are all of the results consistent with your conclusion? If not, why not? ID Sal Compa Mid Age EES SER G Raise Deg Gen1 Gr 1 58 1.017 57 34 85 8 0 5.7 0 M E The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 2 27 0.870 31 52 80 7 0 3.9 0 M B Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work. 3 34 1.096 31 30 75 5 1 3.6 1 F B 4 66 1.157 57 42 100 16 0 5.5 1 M E The column labels in the table mean: 5 47 0.979 48 36 90 16 0 5.7 1 M D ID – Employee sample number Sal – Salary in thousands 6 76 1.134 67 36 70 12 0 4.5 1 M F Age – Age in years EES – Appraisal rating (Employee evaluation score) 7 41 1.025 40 32 100 8 1 5.7 1 F C SER – Years of service G – Gender (0 = male, 1 = female) 8 23 1.000 23 32 90 9 1 5.8 1 F A Mid – salary grade midpoint Raise – percent of last raise 9 77 1.149 67 49 100 10 0 4 1 M F Grade – job/pay grade Deg (0= BS\BA 1 = MS) 10 22 0.956 23 30 80 7 1 4.7 1 F A Gen1 (Male or Female) Compa - salary divided by midpoint, a measure of salary that removes the impact of grade 11 23 1.000 23 41 100 19 1 4.8 1 F A 12 60 1.052 57 52 95 22 0 4.5 0 M E This data should be treated as a sample of employees taken from a company that has about 1,000 13 42 1.050 40 30 100 2 1 4.7 0 F C employees using a random sampling approach. 14 24 1.043 23 32 90 12 1 6 1 F A 15 24 1.043 23 32 80 8 1 4.9 1 F A 16 47 1.175 40 44 90 4 0 5.7 0 M C Mac Users: The homework in ...

Question 1. 1.You are given only three quarterly seasonal indi.docx

teofilapeerless

Question 1. 1. You are given only three quarterly seasonal indices and quarterly seasonally adjusted data for the entire year. What is the raw data value for Q4? Raw data is not adjusted for seasonality. Quarter Seasonal Index Seasonally Adjusted Data Q1 .80 295 Q2 .85 299 Q3 1.15 270 Q4 --- 271 (Points : 3) 325 225 252 271 Question 2. 2. One model of exponential smoothing will provide almost the same forecast as a liner trend method. What are linear trend intercept and slope counterparts for exponential smoothing? (Points : 3) Alpha and Delta Delta and Gamma Alpha and Gamma Std Dev and Mean Question 3. 3. Why is the residual mean value important to a forecaster? (Points : 3) Large mean values indicate nonautoregressiveness. Small mean values indicate the total amount of error is small. Large absolute mean values indicate estimate bias. Large mean values indicate the standard error of the model is small. Question 4. 4. When performing correlation analysis what is the null hypothesis? What measure in Minitab is used to test it and to be 95% confident in the significance of correlation coefficient. (Points : 3) Ho: r = .05 p < .5 Ho: r = 1 p =.05 Ho: r ≠ 0 p≤.05 Ho: r = 0 p≤.05 Question 5. 5. In decomposition what does the cycle factor (CF) of .80 represent for a monthly forecast estimate of a Y variable? (Points : 3) The estimated value is 80% of the average monthly seasonal estimate. The estimate is .80 of the forecasted Y trend value. The estimated value is .80 of the historical average CMA values. The estimated value has 20% more variation than the average historical Y data values. Question 6. 6. A Burger King franchise owner notes that the sales per store has fallen below the stated national Burger King outlet average of $1,258,000. He asserts a change has occurred that reduced the fast food eating habits of Americans. What is his hypothesis (H1) and what type of test for significance must be applied? (Points : 3) H1: u ≥ $1.258,000 A one-tailed t-test to the left. H1: u = $1.258,000 A two-tailed t-test. H1: u < $1.258,000 A one-tailed t-test to the left. H1: p < $1.258,000 A one-tailed test to the right. Question 7. 7. The CEO of Home Depot wants to see if city size has any relationship to the current profit margins of the company stores. What data type will he likely use to determine this? (Points : 3) Time series data of profits by store. Recent 10 year sample of profits by stores. Recent cross section of store profits by city. Trend of a random sample of store profits over time. Question 8. 8. Sometimes forecasters get lazy or forgetful and do not.

Project Report for Mostan Superstore.pptx

ChristianahEfunniyi

This project aimed to address slow response times to refund failed transactions for Mostan Superstore. The student conducted research over 6 months, including a literature review, current state analysis, possible solutions, and a customer survey. Descriptive statistics and t-tests were used to analyze the survey data. The analysis found that while customers prefer keeping their banks as PTSPs, they would be more satisfied with the refund process if responses occurred within 2 days. It was recommended Mostan open a dedicated email for chargebacks and explore using loyalty points for payments to help address transaction failures and speed up refunds.

1. You are given only three quarterly seasonal indices and quarter.docx

jackiewalcutt

1. You are given only three quarterly seasonal indices and quarterly seasonally adjusted data for the entire year. What is the raw data value for Q4? Raw data is not adjusted for seasonality. Quarter Seasonal Index Seasonally Adjusted Data Q1 .80 295 Q2 .85 299 Q3 1.15 270 Q4 --- 271 (Points : 3) 325 225 252 271 Question 2. 2. One model of exponential smoothing will provide almost the same forecast as a liner trend method. What are linear trend intercept and slope counterparts for exponential smoothing? (Points : 3) Alpha and Delta Delta and Gamma Alpha and Gamma Std Dev and Mean Question 3. 3. Why is the residual mean value important to a forecaster? (Points : 3) Large mean values indicate nonautoregressiveness. Small mean values indicate the total amount of error is small. Large absolute mean values indicate estimate bias. Large mean values indicate the standard error of the model is small. Question 4. 4. When performing correlation analysis what is the null hypothesis? What measure in Minitab is used to test it and to be 95% confident in the significance of correlation coefficient. (Points : 3) Ho: r = .05 p < .5 Ho: r = 1 p =.05 Ho: r ≠ 0 p≤.05 Ho: r = 0 p≤.05 Question 5. 5. In decomposition what does the cycle factor (CF) of .80 represent for a monthly forecast estimate of a Y variable? (Points : 3) The estimated value is 80% of the average monthly seasonal estimate. The estimate is .80 of the forecasted Y trend value. The estimated value is .80 of the historical average CMA values. The estimated value has 20% more variation than the average historical Y data values. Question 6. 6. A Burger King franchise owner notes that the sales per store has fallen below the stated national Burger King outlet average of $1,258,000. He asserts a change has occurred that reduced the fast food eating habits of Americans. What is his hypothesis (H1) and what type of test for significance must be applied? (Points : 3) H1: u ≥ $1.258,000 A one-tailed t-test to the left. H1: u = $1.258,000 A two-tailed t-test. H1: u < $1.258,000 A one-tailed t-test to the left. H1: p < $1.258,000 A one-tailed test to the right. Question 7. 7. The CEO of Home Depot wants to see if city size has any relationship to the current profit margins of the company stores. What data type will he likely use to determine this? (Points : 3) Time series data of profits by store. Recent 10 year sample of profits by stores. Recent cross section of store profits by city. Trend of a random sample of store profits over time. Question 8. 8. Sometimes forecasters get lazy or forgetful and do not check the significance of XY data correlations ...

The following calendar-year information is taken from the December.docx

cherry686017

The following calendar-year information is taken from the December 31, 2011, adjusted trial balance and other records of Azalea Company. 1. Each team member is to be responsible for computing one of the following amounts. You are not to duplicate your teammates' work. Get any necessary amounts from teammates. Each member is to explain the computation to the team in preparation for reporting to class. a. Materials used. b. Factory overhead. c. Total manufacturing costs. d. Total cost of goods in process. e. Cost of goods manufactured. 2. Check your cost of goods manufactured with the instructor. If it is correct, proceed to part (3). 3. Each team member is to be responsible for computing one of the following amounts. You are not to duplicate your teammates' work. Get any necessary amounts from teammates. Each member is to explain the computation to the team in preparation for reporting to class. a. Net sales. b. Cost of goods sold. c. Gross profit. d. Total operating expenses. e. Net income or loss before taxes. CALCULATE T TEST Calculate the “t” value for independent groups for the following data using the formula provided in the attached word document. Using the raw measurement data presented, determine whether or not there exists a statistically significant difference between the salaries of female and male human resource managers using the appropriate t-test. Develop a testable hypothesis, confidence level, and degrees of freedom. Report the required “t” critical values based on the degrees of freedom. Show calculations. Answer The null hypothesis tested is H0: There is no significant difference between the average salaries of female and male human resource managers. (µ1= µ2) The alternative hypothesis is H1: There is significant difference between the average salaries of female and male human resource managers. (µ1≠ µ2) The test statistic used is 12 12 2 ~ NN DM MM tt S +- - = Where 22 1122 1212 (1)(1) 11 2 DM NsNs S NNNN éùéù -+- =+ êúêú +- ëûëû Here M1 = 62,200, M2 = 63,700 s1 = 9330.95, s2 = 6912.95 N1 = 10, N2 = 10 (See the excel sheet) Then, ( ) ( ) 22 (101)9330.95(101)6912.95 11 101021010 DM S éù -+- éù =+ êú êú +- ëû êú ëû = 3672.267768 Therefore test statistic, 62,20063,700 3672.267768 t - = = -0.408466946 Degrees of freedom = N1 + N2 – 2 = 10 + 10 – 2 = 18 Let the significance level be 0.05. Rejection criteria: Reject the null hypothesis, if the calculated value of t is greater than the critical value of t at 0.05 significance level. The critical values can be obtained from the student’s t tables with 18 d.f. at 0.05 significance level. Upper critical value = 2.1 Lower critical value = -2.1 0 . 4 0 . 3 0 . 2 0 . 1 0 . 0 X D e n s i t y - 2 . 1 0 0 . 0 2 5 2 . 1 0 0 . 0 2 5 0 D i s t r i b u t i o n P l o t T , d f = 1 8 Conclusion: Fails to reject the null hypothesis. The sample does not provide enough evidence to support the claim that there is significant difference ...

See comments at the right of the data set..docx

potmanandrea

See comments at the right of the data set. ID Salary Compa Midpoint Age Performance Rating Service Gender Raise Degree Gender1 Grade 8 23 1.000 23 32 90 9 1 5.8 0 F A The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10 22 0.956 23 30 80 7 1 4.7 0 F A Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work. 11 23 1.000 23 41 100 19 1 4.8 0 F A 14 24 1.043 23 32 90 12 1 6 0 F A The column labels in the table mean: 15 24 1.043 23 32 80 8 1 4.9 0 F A ID – Employee sample number Salary – Salary in thousands 23 23 1.000 23 36 65 6 1 3.3 1 F A Age – Age in years Performance Rating – Appraisal rating (Employee evaluation score) 26 24 1.043 23 22 95 2 1 6.2 1 F A Service – Years of service (rounded) Gender: 0 = male, 1 = female 31 24 1.043 23 29 60 4 1 3.9 0 F A Midpoint – salary grade midpoint Raise – percent of last raise 35 24 1.043 23 23 90 4 1 5.3 1 F A Grade – job/pay grade Degree (0= BS\BA 1 = MS) 36 23 1.000 23 27 75 3 1 4.3 1 F A Gender1 (Male or Female) Compa - salary divided by midpoint 37 22 0.956 23 22 95 2 1 6.2 1 F A 42 24 1.043 23 32 100 8 1 5.7 0 F A 3 34 1.096 31 30 75 5 1 3.6 0 F B 18 36 1.161 31 31 80 11 1 5.6 1 F B 20 34 1.096 31 44 70 16 1 4.8 1 F B 39 35 1.129 31 27 90 6 1 5.5 1 F B 7 41 1.025 40 32 100 8 1 5.7 0 F C 13 42 1.050 40 30 100 2 1 4.7 1 F C 22 57 1.187 48 48 65 6 1 3.8 0 F D 24 50 1.041 48 30 75 9 1 3.8 1 F D 45 55 1.145 48 36 95 8 1 5.2 0 F D 17 69 1.210 57 27 55 3 1 3 0 F E 48 65 1.140 57 34 90 11 1 5.3 1 F E 28 75 1.119 67 44 95 9 1 4.4 1 F F 43 77 1.149 67 42 95 20 1 5.5 1 F F 19 24 1.043 23 32 85 1 0 4.6 1 M A 25 24 1.043 23 41 70 4 0 4 0 M A 40 25 1.086 23 24 90 2 0 6.3 0 M A 2 27 0.870 31 52 80 7 0 3.9 0 M B 32 28 0.903 31 25 95 4 0 5.6 0 M B 34 28 0.903 31 26 80 2 0 4.9 1 M B 16 47 1.175 40 44 90 4 0 5.7 0 M C 27 40 1.000 40 35 80 7 0 3.9 1 M C 41 43 1.075 40 25 80 5 0 4.3 0 M C 5 47 0.979 48 36 90 16 0 5.7 1 M D 30 49 1.020 48 45 90 18 0 4.3 0 M D 1 58 1.017 57 34 85 8 0 5.7 0 M E 4 66 1.157 57 42 100 16 0 5.5 1 M E 12 60 1.052 57 52 95 22 0 4.5 0 M E 33 64 1.122 57 35 90 9 0 5.5 1 M E 38 56 0.982 57 45 95 11 0 4.5 0 M E 44 60 1.052 57 45 90 16 0 5.2 1 M E 46 65 1.140 57 39 75 20 0 3.9 1 M E 47 62 1.087 57 37 95 5 0 5.5 1 M E 49 60 1.052 57 41 95 21 0 6.6 0 M E 50 66 1.157 57 38 80 12 0 4.6 0 M E 6 76 1.134 67 36 70 12 0 4.5 1 M F 9 77 1.149 67 49 100 10 0 4 1 M F 21 76 1.134 67 43 95 13 0 6.3 1 M F 29 72 1.074 67 52 95 5 0 5.4 0 M F Score: Week 1. Measurement and Description - chapters 1 and 2 .

DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx

simonithomas47935

DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.The column labels in the table mean:ID – Employee sample number Salary – Salary in thousands Age – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)Service – Years of service (rounded)Gender – 0 = male, 1 = female Midpoint – salary grade midpoint Raise – percent of last raiseGrade – job/pay gradeDegree (0= BS\BA 1 = MS)Gender1 (Male or Female)Compa - salary divided by midpoint Week 1Week 1.Measurement and Description - chapters 1 and 2The goal this week is to gain an understanding of our data set - what kind of data we are looking at, some descriptive measurse, and a look at how the data is distributed (shape).1Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: overall sample, Females, and Males.You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =stdev functions. (the range must be found using the difference between the =max and =min functions with Fx) functions.Note: Place data to the right, if you use Descriptive statistics, place that to the right as well.Some of the values are completed for you - please finish the table.SalaryCompaAgePerf. Rat.ServiceOverallMean35.785.99.0Standard Deviation8.251311.41475.7177Note - data is a sample from the larger company populationRange304521FemaleMean32.584.27.9Standard Deviation6.913.64.9Range26.045.018.0MaleMean38.987.610.0Standard Deviation8.48.76.4Range28.030.021.03What is the probability for a:Probabilitya. Randomly selected person being a male in grade E?b. Randomly selected male being in grade E? Note part b is the same as given a male, what is probabilty of being in grade E?c. Why are the results different?4A key issue in comparing data sets is to see if they are distributed/shaped the same. We can do this by looking at some measures of wheresome selected values are within each data set - that .

Lab TA_______________ID_________________Name__________________.docx

MARRY7

Lab TA:_______________ID:_________________Name:___________________ Soc 3811 Basic Social Statistics Extra Credit A total of 20 points 1. The following table presents the total sample size and the percentage distribution of a grouped variable, “Hours of Shopping this Year.” Calculate the mean and the variance. Note that you need to find “frequency (fi)”, “midpoint (Yi)”, and “deviation (di)” for each row before you can get the mean and the variance. Hours Shopping Percentage (%) Frequency (fi) Midpoint (Yi) Deviation di2 di2(fi) 1-4 5 5-8 25 9-12 15 13-16 30 17-20 5 21-24 20 Total 100.0 550 Mean___________ Variance___________ 2. One of the Stat’s TA’s believes that cats are more popular than dogs. Test the hypothesis using the data within the data tables provided below. Cats Popularity Freq Percent Cum. % low 15 75 75 high 5 25 100 Total: 20 Dogs Popularity Freq Percent Cum. % low 22 68.75 68.75 high 10 45.45 100 Total: 32 1: State the research and null hypothesis in symbolic form. 2: Perform a T-test. 3: Find the critical value of T relative to the .05 alpha level. 4: Make a decision relative to the null hypothesis and interpret the result. 3. The following equation is a predicted regression line based on an analysis of a sample of 2,500 people. “Happiness” is the dependent continuous variable measured by a 100 point happiness scale. Income is a dummy variable, 0 for low-income, 1 for middle-income, and 2 for upper-income. Happiness = a + b * Income Here is the STATA output: Happiness Coefficient St. Error t P>t Income 22.45 4.76 7.65 .000 Constant 19.1 4.09 2.25 .029 Observations = 2500 F(1, 2498) = 13.32 Prob > F=.0001 R^2 = .217 Adj. R^2= .201 Answer the following questions. 1. Are the variables happiness and income related, and if so, by how much? How do you know this? 2. What is the strength of this relationship? Happiness = a + b * Income+ b * Health - b * Age The above equation is a predicted multivariable regression line based on an analysis of a sample of 2,500 people. “Happiness” is the dependent continuous variable measured by a 100 point happiness scale. Health is a continuous measure, scored 0 (poor health) to 10 (excellent health). Income is a dummy variable, 0 for low-income, 1 for middle- to upper-income. Age is a continuous measure and is measured in years. Here is the STATA. Happiness Coefficient St. Error t P>t Income 15.6 3.76 3.65 .001 Health 10.4 1.77 3.09 .001 Age -5.9 .87 2.76 .01 Constant 19.1 4.09 2.25 .029 Observations = 2500 F(1, 2498) = 9.44 Prob > F= .0001 R^2 = .381 Adj. R^2= .341 Answer the following. 3. Compare the coefficients from this model to the bivariate model above. How are they different, and why do you think they are different? Which is a better model? 4. Write the predicted equations for both the bivariate and multivariate regressions. ...

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