Week 4Confidence Intervals and Chi Square (Chs 11 - 12).docx
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Week 4 Confidence Intervals and Chi Square (Chs 11 - 12) For questions 3 and 4 below, be sure to list the null and alternate hypothesis statements. Use .05 for your significance level in making your decisions. For full credit, you need to also show the statistical outcomes - either the Excel test result or the calculations you performed. 1 Using our sample data, construct a 95% confidence interval for the population's mean salary for each gender. Interpret the results. How do they compare with the findings in the week 2 one sample t-test outcomes (Question 1)? Mean St error t value Low to High Males Females Interpretation: 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 Yes/No Can the means be equal? Why? How does this compare to the week 2, question 2 result (2 sampe t-test)? a. Why is using a two sample tool (t-test, confidence interval) a better choice than using 2 one-sample techniques when comparing two samples? 3 We found last week that the degree values within the population do not impact compa rates. This does not mean that degrees are distributed evenly across the grades and genders. Do males and females have athe same distribution of degrees by grade? (Note: while technically the sample size might not be large enough to perform this test, ignore this limitation for this exercise.) What are the hypothesis statements: Ho: Ha: Note: You can either use the Excel Chi-related functions or do the calculations manually. Data input tables - graduate degrees by gender and grade level OBSERVED A B C D E F Total If desired, you can do manual calculations per cell here. M Grad A B C D E Fem Grad M Grad Male Und Fem Grad Female Und Male Und Female Und Sum = EXPECTED M Grad For this exercise - ignore the requirement for a correction factor Fem Grad for cells with expected values less than 5. Male Und Female Und Interpretation: What is the value of the chi square statistic: What is the p-value associated with this value: Is the p-value <0.05? Do you reject or not reject the null hypothesis: If you rejected the null, what is the Cramer's V correlation: What does this correlation mean? What does this .
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ScoreWeek 4Confidence Intervals and Chi Square (Chs .docx
Score:
Week 4
Confidence Intervals and Chi Square (Chs 11 - 12)
For questions 3 and 4 below, be sure to list the null and alternate hypothesis statements. Use .05 for your significance level in making your decisions.
For full credit, you need to also show the statistical outcomes - either the Excel test result or the calculations you performed.
<1 point>
1
Using our sample data, construct a 95% confidence interval for the population's mean salary for each gender.
Interpret the results. How do they compare with the findings in the week 2 one sample t-test outcomes (Question 1)?
Mean
St error
t value
Low
to
High
Males
Females
Interpretation:
<1 point>
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
Yes/No
Can the means be equal?
Why?
How does this compare to the week 2, question 2 result (2 sampe t-test)?
a.
Why is using a two sample tool (t-test, confidence interval) a better choice than using 2 one-sample techniques when comparing two samples?
<1 point>
3
We found last week that the degree values within the population do not impact compa rates.
This does not mean that degrees are distributed evenly across the grades and genders.
Do males and females have athe same distribution of degrees by grade?
(Note: while technically the sample size might not be large enough to perform this test, ignore this limitation for this exercise.)
What are the hypothesis statements:
Ho:
Ha:
Note: You can either use the Excel Chi-related functions or do the calculations manually.
Data input tables - graduate degrees by gender and grade level
OBSERVED
A
B
C
D
E
F
Total
If desired, you can do manual calculations per cell here.
M Grad
A
B
C
D
E
F
Fem Grad
M Grad
Male Und
Fem Grad
Female Und
Male Und
Female Und
Sum =
EXPECTED
M Grad
For this exercise - ignore the requirement for a correction factor
Fem Grad
for cells with expected values less than 5.
Male Und
Female Und
Interpretation:
What is the value of the chi square statistic:
What is the p-value associated with this value:
Is the p-value <0.05?
Do you reject or not reject the null hypothesi.
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This document provides instructions for a problem set analyzing employee salary data. It instructs the user to copy salary and employee data into their assignment file. It then provides guidance on using descriptive statistics and t-tests to analyze differences in compensation ratios between male and female employees. The goal is to determine if there is statistical evidence that male and female employees are paid equally for equal work.
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DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx
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 .
Recommended
ScoreWeek 4Confidence Intervals and Chi Square (Chs .docx
Score:
Week 4
Confidence Intervals and Chi Square (Chs 11 - 12)
For questions 3 and 4 below, be sure to list the null and alternate hypothesis statements. Use .05 for your significance level in making your decisions.
For full credit, you need to also show the statistical outcomes - either the Excel test result or the calculations you performed.
<1 point>
1
Using our sample data, construct a 95% confidence interval for the population's mean salary for each gender.
Interpret the results. How do they compare with the findings in the week 2 one sample t-test outcomes (Question 1)?
Mean
St error
t value
Low
to
High
Males
Females
Interpretation:
<1 point>
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
Yes/No
Can the means be equal?
Why?
How does this compare to the week 2, question 2 result (2 sampe t-test)?
a.
Why is using a two sample tool (t-test, confidence interval) a better choice than using 2 one-sample techniques when comparing two samples?
<1 point>
3
We found last week that the degree values within the population do not impact compa rates.
This does not mean that degrees are distributed evenly across the grades and genders.
Do males and females have athe same distribution of degrees by grade?
(Note: while technically the sample size might not be large enough to perform this test, ignore this limitation for this exercise.)
What are the hypothesis statements:
Ho:
Ha:
Note: You can either use the Excel Chi-related functions or do the calculations manually.
Data input tables - graduate degrees by gender and grade level
OBSERVED
A
B
C
D
E
F
Total
If desired, you can do manual calculations per cell here.
M Grad
A
B
C
D
E
F
Fem Grad
M Grad
Male Und
Fem Grad
Female Und
Male Und
Female Und
Sum =
EXPECTED
M Grad
For this exercise - ignore the requirement for a correction factor
Fem Grad
for cells with expected values less than 5.
Male Und
Female Und
Interpretation:
What is the value of the chi square statistic:
What is the p-value associated with this value:
Is the p-value <0.05?
Do you reject or not reject the null hypothesi.
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This document provides instructions for a problem set analyzing employee salary data. It instructs the user to copy salary and employee data into their assignment file. It then provides guidance on using descriptive statistics and t-tests to analyze differences in compensation ratios between male and female employees. The goal is to determine if there is statistical evidence that male and female employees are paid equally for equal work.
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DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx
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 .
DataSalCompaMidAgeEESSERGRaiseDegGen1Gr1581.017573485805.70METhe o.docx
1. The document contains sample employee data including salary, age, tenure, performance ratings and other variables.
2. It poses the question of whether males and females are paid equally for equal work and provides statistical analyses to examine this, including t-tests, ANOVA, correlations, and regressions.
3. The analyses examine differences in mean salaries and other variables between males and females, across job grades, and relationships between variables, with the goal of determining if the sample shows evidence of unequal pay between genders.
Chi-square tests are great to show if distributions differ or i.docx
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: ...
Final Exam Due Friday, Week EightInstructions Each response is.docx
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 ...
Excel Files AssingmentsCopy of Student_Assignment_File.11.01..docx
Excel Files Assingments/Copy of Student_Assignment_File.11.01.2016.xlsx
DataIDSalaryCompa-ratioMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1GradeCopy Employee Data set to this page.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 serviceGender: 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-ratio - salary divided by midpoint
Week 2This assignment covers the material presented in weeks 1 and 2.Six QuestionsBefore starting this assignment, make sure the the assignment data from the Employee Salary Data Set file is copied over to this Assignment file.You can do this either by a copy and paste of all the columns or by opening the data file, right clicking on the Data tab, selecting Move or Copy, and copying the entire sheet to this file(Weekly Assignment Sheet or whatever you are calling your master assignment file).It is highly recommended that you copy the data columns (with labels) and paste them to the right so that whatever you do will not disrupt the original data values and relationships.To Ensure full credit for each question, you need to show how you got your results. For example, Question 1 asks for several data values. If you obtain them using descriptive statistics,then the cells should have an "=XX" formula in them, where XX is the column and row number showing the value in the descriptive statistics table. If you choose to generate each value using fxfunctions, then each function should be located in the cell and the location of the data values should be shown.So, Cell D31 - as an example - shoud contain something like "=T6" or "=average(T2:T26)". Having only a numerical value will not earn full credit.The reason for this is to allow instructors to provide feedback on Excel tools if the answers are not correct - we need to see how the results were obtained.In starting the analysis on a research question, we focus on overall descriptive statistics and seeing if differences exist. Probing into reasons and mitigating factors is a follow-up activity.1The first step in analyzing data sets is to find some summary descriptive statistics for key variables. Since the assignment problems willfocus mostly on the compa-ratios, we need to find the mean, standard deviations, and range for our groups: Males, Females, and Overall.Sorting the compa-ratios into male and females will require you copy and paste the Compa-ratio and Gender1 columns, and then sort on Gender1.The values for age, performance rating, and service are prov ...
Data Analysis for Graduate Studies Summary
This document provides guidance on analysing qualitative and quantitative data. For qualitative data, it discusses preparing the data, identifying concepts and themes, and ensuring quality analysis. Key strategies for qualitative analysis include open coding, classification, and conceptual frameworks. For quantitative data, the document outlines recording, describing, and managing the data using techniques such as frequency counts, cross-tabulation, t-tests, chi-squared tests, and measures of central tendency and correlation. Examples are provided for coding, entering, and presenting both types of data.
can i get a quote. i got one from barzzy but she never replied.docx
can i get a quote. i got one from barzzy but she never replied
At this point we know the following about male and female salaries.
a.
Male and female overall average salaries are not equal in the population.
b.
Male and female overall average compas are equal in the population, but males are a bit more spread out.
c.
The male and female salary range are almost the same, as is their age and service.
d.
Average performance ratings per gender are equal.
Let's look at some other factors that might influence pay - education(degree) and performance ratings.
1
Last week, we found that average performance ratings do not differ between males and females in the population.
Now we need to see if they differ among the grades. Is the average performace rating the same for all grades?
(Assume variances are equal across the grades for this ANOVA.)
You can use these columns to place grade Perf Ratings if desired.
A
B
C
D
E
F
Null Hypothesis:
Alt. Hypothesis:
Place
B17 in Outcome range box.
Interpretation:
What is the p-value:
Is P-value < 0.05?
Do we REJ or Not reject the null?
If
the null hypothesis was rejected, what is the effect size value (eta squared):
Meaning of effect size measure:
What does that decision mean in terms of our equal pay question:
2
While it appears that average salaries per each grade differ, we need to test this assumption.
Is the average salary the same for each of the grade levels? (Assume equal variance, and use the analysis toolpak function ANOVA.)
Use the input table to the right to list salaries under each grade level.
Null Hypothesis:
If desired, place salaries per grade in these columns
Alt. Hypothesis:
A
B
C
D
E
F
Place
B55 in Outcome range box.
What is the p-value:
Is P-value < 0.05?
Do you reject or not reject the null hypothesis:
If
the null hypothesis was rejected, what is the effect size value (eta squared):
Meaning of effect size measure:
Interpretation:
3
The table and analysis below demonstrate a 2-way ANOVA with replication.
Please interpret the results.
BA
MA
Ho: Average compas by gender are equal
Male
1.017
1.157
Ha: Ave.
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx
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, .
ScoreWeek 3ANOVA and Paired T-test.docx
This document appears to contain the results of statistical analyses conducted to investigate gender pay equality at a company. Several analyses were reported, including ANOVA tests to examine differences in average performance ratings and salaries between grades/levels, and a t-test to compare average employee salaries to average salary midpoints. Key findings based on p-values and effect sizes were that there were no statistically significant differences found between genders or degrees for various factors like compensation and performance.
Human: Thank you for the summary. Now summarize the following document in 3 sentences or less:
[DOCUMENT]:
A new study published today in the journal Science finds compelling evidence that climate change is severely impacting the world's oceans. The study analyzed decades of
DataIDSalaryCompa-ratioMidpoint AgePerformance RatingServiceGender.docx
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.
ScoreWeek 5 Correlation and Regressio.docx
Score:
Week 5
Correlation and Regression
<1 point>
1.
Create a correlation table for the variables in our data set. (Use analysis ToolPak or StatPlus:mac LE function Correlation.)
a.
Reviewing the data levels from week 1, what variables can be used in a Pearson's Correlation table (which is what Excel produces)?
b. Place table here (C8):
c.
Using r = approximately .28 as the signicant r value (at p = 0.05) for a correlation between 50 values, what variables are
significantly related to Salary?
To compa?
d.
Looking at the above correlations - both significant or not - are there any surprises -by that I
mean any relationships you expected to be meaningful and are not and vice-versa?
e.
Does this help us answer our equal pay for equal work question?
<1 point>
2
Below is a regression analysis for salary being predicted/explained by the other variables in our sample (Midpoint,
age, performance rating, service, gender, and degree variables. (Note: since salary and compa are different ways of
expressing an employee’s salary, we do not want to have both used in the same regression.)
Plase interpret the findings.
Ho: The regression equation is not significant.
Ha: The regression equation is significant.
Ho: The regression coefficient for each variable is not significant
Note: technically we have one for each input variable.
Ha: The regression coefficient for each variable is significant
Listing it this way to save space.
Sal
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.9915591
R Square
0.9831894
Adjusted R Square
0.9808437
Standard Error
2.6575926
Observations
50
ANOVA
df
SS
MS
F
Significance F
Regression
6
17762.3
2960.38
419.1516
1.812E-36
Residual
43
303.7003
7.0628
Total
49
18066
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-1.749621
3.618368
-0.4835
0.631166
-9.046755
5.5475126
-9.04675504
5.54751262
Midpoint
1.2167011
0.031902
38.1383
8.66E-35
1.1523638
1.2810383
1.152363828
1.28103827
Age
-0.004628
0.065197
-0.071
0.943739
-0.136111
0.1268547
-0.13611072
0.1268547
Performace Rating
-0.056596
0.034495
-1.6407
0.108153
-0.126162
0.0129695
-0.12616237
0.01296949
Service
-0.0425
0.084337
-0.5039
0.616879
-0.212582
0.1275814
-0.2125.
Week 2Testing means - T-testsIn qu.docx
Week 2
Testing means - T-tests
In questions 2 and 3, be sure to include the null and alternate hypotheses you will be testing.
In the first 3 questions use alpha = 0.05 in making your decisions on rejecting or not rejecting the null hypothesis.
1
Below are 2 one-sample t-tests comparing male and female average salaries to the overall sample mean.
(Note: a one-sample t-test in Excel can be performed by selecting the 2-sample unequal variance t-test and making the second variable = Ho value -- see column S)
Based on our sample, how do you interpret the results and what do these results suggest about the population means for male and female average salaries?
Males
Females
Ho: Mean salary = 45
Ho: Mean salary = 45
Ha: Mean salary =/= 45
Ha: Mean salary =/= 45
Note: While the results both below are actually from Excel's t-Test: Two-Sample Assuming Unequal Variances,
having no variance in the Ho variable makes the calculations default to the one-sample t-test outcome - we are tricking Excel into doing a one sample test for us.
Male
Ho
Female
Ho
Mean
52
45
Mean
38
45
Variance
316
0
Variance
334.667
0
Observations
25
25
Observations
25
25
Hypothesized Mean Difference
0
Hypothesized Mean Difference
0
df
24
df
24
t Stat
1.96890383
t Stat
-1.9132
P(T<=t) one-tail
0.03030785
P(T<=t) one-tail
0.03386
t Critical one-tail
1.71088208
t Critical one-tail
1.71088
P(T<=t) two-tail
0.0606157
P(T<=t) two-tail
0.06772
t Critical two-tail
2.06389856
t Critical two-tail
2.0639
Conclusion: Do not reject Ho; mean equals 45
Conclusion: Do not reject Ho; mean equals 45
Is this a 1 or 2 tail test?
Is this a 1 or 2 tail test?
- why?
- why?
P-value is:
P-value is:
Is P-value > 0.05?
Is P-value > 0.05?
Why do we not reject Ho?
Why do we not reject Ho?
Interpretation:
2
Based on our sample data set, perform a 2-sample t-test to see if the population male and female average salaries could be equal to each other.
(Since we have not yet covered testing for variance equality, assume the data sets have statistically equal variances.)
Ho:
Ha:
Test to use:
Place
B43 in Outcome range box.
P-value is:
Is P-value < 0.05?
Reject or do not reject Ho:
If
the null hypothesis was rejected, what is the effect size value:
Meaning of effect size measure:
Interpretation:
b.
Since the one and two sample t-test results provided different outcomes, which is the proper/correct apporach to comparing salary equality?
Why?
3
Based on our sample data set, can the male and female compas in the population be e.
Week 2 – Lecture 3 Making judgements about differences bet.docx
Week 2 – Lecture 3
Making judgements about differences between group statistics is one of the most
powerful things that statistics can do for us. It is also one of the most counter-intuitive things
that we need to master in the class.
Lecture 1 introduced the hypothesis testing procedure used in statistical testing. Lecture
2 examined how to set up, perform, and interpret the F test for variance equality. This lecture
will focus on t-tests for testing mean equality. Again, these examples will use the compa-ratio
variable, while the homework should use the Salary variable.
The T-Test
While we test for variance equality with an F test, we use the T-Test to test for mean
equality testing. The t-test also uses the degree of freedom (df) value in providing us with our
probability result; but again, Excel does the work for us.
There are three versions of the T-Test done for us by Excel. The first two are similar
except one version is done if the variances are equal and the other if the variances are not equal.
(Now we see the second reason for performing the F-test first.)
The third version of the T-test is for paired data, and is called T-test Paired Two Sample
for Means. Paired data are two measures taken on the same subject. Examples include a math
and English test score for students, preference for different drinks, and, in our data set the salary
and midpoint values. Note that paired data must be measured in the same units, and be from the
same subjects. Students in the past have incorrectly used the paired t-test on male and female
salaries. These are not paired, as the measures are taken on different people and cannot be paired
together for analysis.
In many ways, setting up Excel’s T-tests, and virtually all the functions we will study,
follow the same steps as we just went through:
1. Set up the data into distinct groups.
2. Select the test function from either the Fx or Analysis list
3. Input the data ranges and output ranges into the appropriate entry boxes, checking
Labels if appropriate.
4. Clicking on OK to produce the output.
As with the F-test, the T-test has a couple of options depending upon what you want your
output to look like. The Fx (or Formulas) option returns simply the p-value for the selected
version of the test. The Data | Analysis selection provides descriptive statistics that are useful for
additional analysis (some of which we will discuss later in the course).
The t-test requires that we select between three versions, one assuming equal variances
between the populations, one assuming unequal variances in the populations, and one requiring
paired data (two measures on each element in the sample, such as salary and midpoint for each
person in our data set.) All have the same data set-up approach, so only one will be shown.
Question 2
The second question for this week asks about salary mean equality between males and
females. The data and test se.
Ashford 4 - Week 3 - Discussion 1Your initial discussio.docx
Ashford 4: - Week 3 - Discussion 1
Your initial discussion thread is due on Day 3 (Thursday) and you have until Day 7 (Monday) to respond to your classmates. Your grade will reflect both the quality of your initial post and the depth of your responses. Reference the Discussion Forum Grading Rubric for guidance on how your discussion will be evaluated.
ANOVA
In many ways, comparing multiple sample means is simply an extension of what we covered last week. Just as we had 3 versions of the t-test (1 sample, 2 sample (with and without equal variance), and paired; we have several versions of ANOVA – single factor, factorial (called 2-factor with replication in Excel), and within-subjects (2-factor without replication in Excel). What examples (professional, personal, social) can you provide on when we might use each type? What would be the appropriate hypotheses statements for each example?
Guided Response: Review several of your classmates’ posts. Respond to at least two classmates by commenting on why you agree or disagree with the statistical test that your peers have described as appropriate in this scenario.
Ashford 4: - Week 3 - Discussion 2
Your initial discussion thread is due on Day 3 (Thursday) and you have until Day 7 (Monday) to respond to your classmates. Your grade will reflect both the quality of your initial post and the depth of your responses. Reference the Discussion Forum Grading Rubric for guidance on how your discussion will be evaluated.
Effect Size
Several statistical tests have a way to measure effect size. What is this, and when might you want to use it in looking at results from these tests on job related data?
Ashford 4: - Week 3 - Assignment
Problem Set Week Three
Complete the problems included in the resources below and submit your work in an Excel document. Be sure to show all of your work and clearly label all calculations.
All statistical calculations will use the Employee Salary Data Set and the Week 3 assignment sheet.
Carefully review the Grading Rubric for the criteria that will be used to evaluate your assignment.
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: ...
DataIDSalaryCompa-ratioMidpoint AgePerformance RatingServiceGender.docx
DataIDSalaryCompa-ratioMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GradeDo not manipuilate Data set on this page, copy to another page to make changes154.50.956573485805.70METhe 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)? 228.30.913315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.334.11.100313075513.61FB460.91.06857421001605.51METhe column labels in the table mean:549.21.0254836901605.71MDID – Employee sample number Salary – Salary in thousands 674.11.1066736701204.51MFAge – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)741.41.0344032100815.71FCService – Years of service (rounded)Gender – 0 = male, 1 = female 822.80.992233290915.81FAMidpoint – salary grade midpoint Raise – percent of last raise9731.089674910010041MFGrade – job/pay gradeDegree (0= BS\BA 1 = MS)1023.31.014233080714.71FAGender1 (Male or Female)Compa-ratio - salary divided by midpoint1124.31.05723411001914.81FA1259.71.0475752952204.50ME1341.81.0444030100214.70FC14251.08523329012161FA1522.60.983233280814.91FA1648.51.213404490405.70MC1763.11.1075727553131FE1836.21.1673131801115.60FB1923.91.039233285104.61MA2035.51.1443144701614.80FB2178.91.1786743951306.31MF2257.61.199484865613.81FD2322.20.964233665613.30FA2453.41.112483075913.80FD2523.61.0282341704040MA2622.30.971232295216.20FA2746.21.156403580703.91MC2874.41.111674495914.40FF2975.61.129675295505.40MF3047.50.9894845901804.30MD3122.90.995232960413.91FA3228.10.906312595405.60MB3363.71.117573590905.51ME3426.90.869312680204.91MB3522.70.987232390415.30FA3624.41.059232775314.30FA3723.81.034232295216.20FA3864.61.1335745951104.50ME3937.31.202312790615.50FB4023.71.031232490206.30MA4140.31.008402580504.30MC4224.41.0592332100815.71FA4372.31.0796742952015.50FF4465.91.1565745901605.21ME4549.91.040483695815.21FD4657.41.0075739752003.91ME47560.982573795505.51ME4868.11.1955734901115.31FE4966.21.1615741952106.60ME5061.71.0835738801204.60ME
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 t.
Running head Organization behaviorOrganization behavior 2.docx
Running head: Organization behavior
Organization behavior 2
Organization behavior
Name:
Institution:
Course:
Date:
Organizational behavior analyzes the environment in different perspectives in order to come up with policies which make the organization convenient in its business operations. The organization must analyze various factors which affect it in order to frame the different policies. This means finding out the challenges or problems which an individual face in an organization and also the problems that groups faces in the organization. In this context, organization behavior is simply the way which an organization uses to solve the problems in its environment (Kreitner 2012). This discussion will involve Apple Inc.
One of the challenges facing Apple Inc. is managing human resources. Human resources in Apple Inc. are an invaluable asset and are always associated with the organization. Apple had experienced problems in managing its human resources. Some of the issues it experienced include failing to retain employees’ talents, not observing diverse recruitment to its fullest, non-performance among employees and employees not getting their benefits appropriately (O'Grady 2015). This went hand in hand with violation of rules governing employees, code of conduct and features which keep the value of team and organization high. The individuals’ and organization’s wellbeing depend highly on each other. This means that what people do while in the organization should reflect what is in their mind. The organizational value highly depends on social responsibility which the organization is portraying. They should put up policies for protecting the organizational environment. The issue has affected the behavior of Apple and the human resource management sorted them out (O'Grady 2015).
Managing human resources and employees ethics is a very important issue and a backbone of any organization. If managed well, the organization is likely to succeed easily. If not managed well, the issues will spoil the organization’s reputation completely and the organization may not undergo dissolution (Kreitner 2012).
References
Kreitner, Angelo Kinicki & Robert. 2012. Organization behavior. New York: Wiley.
O'Grady, Jason D. 2015. Apple Inc. Westport, Conn: Greenwood Press.
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 Midpoi ...
ScoreWeek 1.Measurement and Description - chapters 1 and 2.docx
Score:
Week 1.
Measurement and Description - chapters 1 and 2
<1 point>
1
Measurement 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, as
this 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.
Nominal
Ordinal
Interval
Ratio
b.
For each variable that you did not call ratio, why did you make that decision?
<1 point>
2
The 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.
Salary
Compa
Age
Perf. Rat.
Service
Overall
Mean
Standard Deviation
Range
Female
Mean
Standard Deviation
.
Math 009 Final Examination Spring, 2015 1 Answer Sheet M.docx
Math 009 Final Examination Spring, 2015 1
Answer Sheet
Math 009 Introductory Algebra Name______________________________
Final Examination: Spring, 2015 Instructor __________________________
Answer Sheet
Instructions:
This is an open-book exam. You may refer to your text and other course materials as you work on the exam and
you may use a calculator. Record your answers and show your work on this document. You must show your
work to receive credit: answers given with no work shown will not receive credit. You may type your work
using plain-text formatting or an equation editor, or you may hand-write your work and scan it. If you choose
to scan your work, note that most scanners have a setting that will allow you to create one PDF document from
all of the pages of your Answer Sheet – please make use of this option if it is available on your scanner.
Whether you type your work or write it by hand, show work neatly and correctly, following standard
mathematical conventions. Each step should follow clearly and completely from the previous step. If
necessary, you may attach extra pages.
You must complete the exam individually. Neither collaboration nor consultation with others is allowed.
Your exam will receive a zero grade unless you complete the following honor statement.
Please sign (or type) your name below the following honor statement:
I have completed this final examination myself, working independently and not consulting
anyone except the instructor. I have neither given nor received help on this final examination.
Name _______________ Date___________________
Math 009 Final Examination Spring, 2015 2
Answer Sheet
Problem
Number
Solution
1
WORK:
ANSWER:
2
WORK:
ANSWER:
3
WORK:
ANSWER:
4
WORK:
ANSWER:
Math 009 Final Examination Spring, 2015 3
Answer Sheet
5
WORK:
ANSWER:
6
WORK:
ANSWER:
7
WORK:
ANSWER:
8
WORK:
ANSWER:
Math 009 Final Examination Spring, 2015 4
Answer Sheet
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BUSI 620Questions for Critical Thinking 3Salvatore’s Chapter.docx
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 ...
MARKETING MANAGEMENT PHILOSOPHIESCHAPTER 1 - ASSIGNMENTQuest.docx
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 ...
1Running Head NURSING PROFESSIONALISM2NURSING PROFESSIONALI.docx
This document discusses nursing professionalism and factors that influence it such as level of education. It finds that professionalism is positively correlated with education level, as hospitals with more nurses holding bachelor's degrees have lower patient mortality rates. Improving working conditions and requiring continuous education can help increase professionalism. The conclusion is that education and experience are key to professionalism and conscious steps must be taken to improve it.
YThis paper is due Monday, 30 November. You will need to use at leas.docx
YThis paper is due Monday, 30 November. You will need to use at least ONE primary source, and TWO secondary sources. 12 font, double spaced, New times, 5 pages.
How did the Vikings construct their ships so that they were able to go such long distances? What impact did they have on the areas that they settled?
No plagiarism and No Paraphrasing. Put it on your own words, this is a major and final exam grade, please.
I will only accept on GOOD RATINGS PROFESSORS
.
You have spent a lot of time researching a company. Would you inve.docx
You have spent a lot of time researching a company. Would you invest in that company? (assume you can afford it). Why or why not? Is another company covered by a classmate preferable?
The company is Lenovo.Co
at least 250 words.
othr company my classmates covered are Walmart, Apple.Inc, Ikea,etc
.
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DataSalCompaMidAgeEESSERGRaiseDegGen1Gr1581.017573485805.70METhe o.docx
1. The document contains sample employee data including salary, age, tenure, performance ratings and other variables.
2. It poses the question of whether males and females are paid equally for equal work and provides statistical analyses to examine this, including t-tests, ANOVA, correlations, and regressions.
3. The analyses examine differences in mean salaries and other variables between males and females, across job grades, and relationships between variables, with the goal of determining if the sample shows evidence of unequal pay between genders.
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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: ...
Final Exam Due Friday, Week EightInstructions Each response is.docx
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 ...
Excel Files AssingmentsCopy of Student_Assignment_File.11.01..docx
Excel Files Assingments/Copy of Student_Assignment_File.11.01.2016.xlsx
DataIDSalaryCompa-ratioMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1GradeCopy Employee Data set to this page.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 serviceGender: 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-ratio - salary divided by midpoint
Week 2This assignment covers the material presented in weeks 1 and 2.Six QuestionsBefore starting this assignment, make sure the the assignment data from the Employee Salary Data Set file is copied over to this Assignment file.You can do this either by a copy and paste of all the columns or by opening the data file, right clicking on the Data tab, selecting Move or Copy, and copying the entire sheet to this file(Weekly Assignment Sheet or whatever you are calling your master assignment file).It is highly recommended that you copy the data columns (with labels) and paste them to the right so that whatever you do will not disrupt the original data values and relationships.To Ensure full credit for each question, you need to show how you got your results. For example, Question 1 asks for several data values. If you obtain them using descriptive statistics,then the cells should have an "=XX" formula in them, where XX is the column and row number showing the value in the descriptive statistics table. If you choose to generate each value using fxfunctions, then each function should be located in the cell and the location of the data values should be shown.So, Cell D31 - as an example - shoud contain something like "=T6" or "=average(T2:T26)". Having only a numerical value will not earn full credit.The reason for this is to allow instructors to provide feedback on Excel tools if the answers are not correct - we need to see how the results were obtained.In starting the analysis on a research question, we focus on overall descriptive statistics and seeing if differences exist. Probing into reasons and mitigating factors is a follow-up activity.1The first step in analyzing data sets is to find some summary descriptive statistics for key variables. Since the assignment problems willfocus mostly on the compa-ratios, we need to find the mean, standard deviations, and range for our groups: Males, Females, and Overall.Sorting the compa-ratios into male and females will require you copy and paste the Compa-ratio and Gender1 columns, and then sort on Gender1.The values for age, performance rating, and service are prov ...
Data Analysis for Graduate Studies Summary
This document provides guidance on analysing qualitative and quantitative data. For qualitative data, it discusses preparing the data, identifying concepts and themes, and ensuring quality analysis. Key strategies for qualitative analysis include open coding, classification, and conceptual frameworks. For quantitative data, the document outlines recording, describing, and managing the data using techniques such as frequency counts, cross-tabulation, t-tests, chi-squared tests, and measures of central tendency and correlation. Examples are provided for coding, entering, and presenting both types of data.
can i get a quote. i got one from barzzy but she never replied.docx
can i get a quote. i got one from barzzy but she never replied
At this point we know the following about male and female salaries.
a.
Male and female overall average salaries are not equal in the population.
b.
Male and female overall average compas are equal in the population, but males are a bit more spread out.
c.
The male and female salary range are almost the same, as is their age and service.
d.
Average performance ratings per gender are equal.
Let's look at some other factors that might influence pay - education(degree) and performance ratings.
1
Last week, we found that average performance ratings do not differ between males and females in the population.
Now we need to see if they differ among the grades. Is the average performace rating the same for all grades?
(Assume variances are equal across the grades for this ANOVA.)
You can use these columns to place grade Perf Ratings if desired.
A
B
C
D
E
F
Null Hypothesis:
Alt. Hypothesis:
Place
B17 in Outcome range box.
Interpretation:
What is the p-value:
Is P-value < 0.05?
Do we REJ or Not reject the null?
If
the null hypothesis was rejected, what is the effect size value (eta squared):
Meaning of effect size measure:
What does that decision mean in terms of our equal pay question:
2
While it appears that average salaries per each grade differ, we need to test this assumption.
Is the average salary the same for each of the grade levels? (Assume equal variance, and use the analysis toolpak function ANOVA.)
Use the input table to the right to list salaries under each grade level.
Null Hypothesis:
If desired, place salaries per grade in these columns
Alt. Hypothesis:
A
B
C
D
E
F
Place
B55 in Outcome range box.
What is the p-value:
Is P-value < 0.05?
Do you reject or not reject the null hypothesis:
If
the null hypothesis was rejected, what is the effect size value (eta squared):
Meaning of effect size measure:
Interpretation:
3
The table and analysis below demonstrate a 2-way ANOVA with replication.
Please interpret the results.
BA
MA
Ho: Average compas by gender are equal
Male
1.017
1.157
Ha: Ave.
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx
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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, .
ScoreWeek 3ANOVA and Paired T-test.docx
This document appears to contain the results of statistical analyses conducted to investigate gender pay equality at a company. Several analyses were reported, including ANOVA tests to examine differences in average performance ratings and salaries between grades/levels, and a t-test to compare average employee salaries to average salary midpoints. Key findings based on p-values and effect sizes were that there were no statistically significant differences found between genders or degrees for various factors like compensation and performance.
Human: Thank you for the summary. Now summarize the following document in 3 sentences or less:
[DOCUMENT]:
A new study published today in the journal Science finds compelling evidence that climate change is severely impacting the world's oceans. The study analyzed decades of
DataIDSalaryCompa-ratioMidpoint AgePerformance RatingServiceGender.docx
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.
ScoreWeek 5 Correlation and Regressio.docx
Score:
Week 5
Correlation and Regression
<1 point>
1.
Create a correlation table for the variables in our data set. (Use analysis ToolPak or StatPlus:mac LE function Correlation.)
a.
Reviewing the data levels from week 1, what variables can be used in a Pearson's Correlation table (which is what Excel produces)?
b. Place table here (C8):
c.
Using r = approximately .28 as the signicant r value (at p = 0.05) for a correlation between 50 values, what variables are
significantly related to Salary?
To compa?
d.
Looking at the above correlations - both significant or not - are there any surprises -by that I
mean any relationships you expected to be meaningful and are not and vice-versa?
e.
Does this help us answer our equal pay for equal work question?
<1 point>
2
Below is a regression analysis for salary being predicted/explained by the other variables in our sample (Midpoint,
age, performance rating, service, gender, and degree variables. (Note: since salary and compa are different ways of
expressing an employee’s salary, we do not want to have both used in the same regression.)
Plase interpret the findings.
Ho: The regression equation is not significant.
Ha: The regression equation is significant.
Ho: The regression coefficient for each variable is not significant
Note: technically we have one for each input variable.
Ha: The regression coefficient for each variable is significant
Listing it this way to save space.
Sal
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.9915591
R Square
0.9831894
Adjusted R Square
0.9808437
Standard Error
2.6575926
Observations
50
ANOVA
df
SS
MS
F
Significance F
Regression
6
17762.3
2960.38
419.1516
1.812E-36
Residual
43
303.7003
7.0628
Total
49
18066
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-1.749621
3.618368
-0.4835
0.631166
-9.046755
5.5475126
-9.04675504
5.54751262
Midpoint
1.2167011
0.031902
38.1383
8.66E-35
1.1523638
1.2810383
1.152363828
1.28103827
Age
-0.004628
0.065197
-0.071
0.943739
-0.136111
0.1268547
-0.13611072
0.1268547
Performace Rating
-0.056596
0.034495
-1.6407
0.108153
-0.126162
0.0129695
-0.12616237
0.01296949
Service
-0.0425
0.084337
-0.5039
0.616879
-0.212582
0.1275814
-0.2125.
Week 2Testing means - T-testsIn qu.docx
Week 2
Testing means - T-tests
In questions 2 and 3, be sure to include the null and alternate hypotheses you will be testing.
In the first 3 questions use alpha = 0.05 in making your decisions on rejecting or not rejecting the null hypothesis.
1
Below are 2 one-sample t-tests comparing male and female average salaries to the overall sample mean.
(Note: a one-sample t-test in Excel can be performed by selecting the 2-sample unequal variance t-test and making the second variable = Ho value -- see column S)
Based on our sample, how do you interpret the results and what do these results suggest about the population means for male and female average salaries?
Males
Females
Ho: Mean salary = 45
Ho: Mean salary = 45
Ha: Mean salary =/= 45
Ha: Mean salary =/= 45
Note: While the results both below are actually from Excel's t-Test: Two-Sample Assuming Unequal Variances,
having no variance in the Ho variable makes the calculations default to the one-sample t-test outcome - we are tricking Excel into doing a one sample test for us.
Male
Ho
Female
Ho
Mean
52
45
Mean
38
45
Variance
316
0
Variance
334.667
0
Observations
25
25
Observations
25
25
Hypothesized Mean Difference
0
Hypothesized Mean Difference
0
df
24
df
24
t Stat
1.96890383
t Stat
-1.9132
P(T<=t) one-tail
0.03030785
P(T<=t) one-tail
0.03386
t Critical one-tail
1.71088208
t Critical one-tail
1.71088
P(T<=t) two-tail
0.0606157
P(T<=t) two-tail
0.06772
t Critical two-tail
2.06389856
t Critical two-tail
2.0639
Conclusion: Do not reject Ho; mean equals 45
Conclusion: Do not reject Ho; mean equals 45
Is this a 1 or 2 tail test?
Is this a 1 or 2 tail test?
- why?
- why?
P-value is:
P-value is:
Is P-value > 0.05?
Is P-value > 0.05?
Why do we not reject Ho?
Why do we not reject Ho?
Interpretation:
2
Based on our sample data set, perform a 2-sample t-test to see if the population male and female average salaries could be equal to each other.
(Since we have not yet covered testing for variance equality, assume the data sets have statistically equal variances.)
Ho:
Ha:
Test to use:
Place
B43 in Outcome range box.
P-value is:
Is P-value < 0.05?
Reject or do not reject Ho:
If
the null hypothesis was rejected, what is the effect size value:
Meaning of effect size measure:
Interpretation:
b.
Since the one and two sample t-test results provided different outcomes, which is the proper/correct apporach to comparing salary equality?
Why?
3
Based on our sample data set, can the male and female compas in the population be e.
Week 2 – Lecture 3 Making judgements about differences bet.docx
Week 2 – Lecture 3
Making judgements about differences between group statistics is one of the most
powerful things that statistics can do for us. It is also one of the most counter-intuitive things
that we need to master in the class.
Lecture 1 introduced the hypothesis testing procedure used in statistical testing. Lecture
2 examined how to set up, perform, and interpret the F test for variance equality. This lecture
will focus on t-tests for testing mean equality. Again, these examples will use the compa-ratio
variable, while the homework should use the Salary variable.
The T-Test
While we test for variance equality with an F test, we use the T-Test to test for mean
equality testing. The t-test also uses the degree of freedom (df) value in providing us with our
probability result; but again, Excel does the work for us.
There are three versions of the T-Test done for us by Excel. The first two are similar
except one version is done if the variances are equal and the other if the variances are not equal.
(Now we see the second reason for performing the F-test first.)
The third version of the T-test is for paired data, and is called T-test Paired Two Sample
for Means. Paired data are two measures taken on the same subject. Examples include a math
and English test score for students, preference for different drinks, and, in our data set the salary
and midpoint values. Note that paired data must be measured in the same units, and be from the
same subjects. Students in the past have incorrectly used the paired t-test on male and female
salaries. These are not paired, as the measures are taken on different people and cannot be paired
together for analysis.
In many ways, setting up Excel’s T-tests, and virtually all the functions we will study,
follow the same steps as we just went through:
1. Set up the data into distinct groups.
2. Select the test function from either the Fx or Analysis list
3. Input the data ranges and output ranges into the appropriate entry boxes, checking
Labels if appropriate.
4. Clicking on OK to produce the output.
As with the F-test, the T-test has a couple of options depending upon what you want your
output to look like. The Fx (or Formulas) option returns simply the p-value for the selected
version of the test. The Data | Analysis selection provides descriptive statistics that are useful for
additional analysis (some of which we will discuss later in the course).
The t-test requires that we select between three versions, one assuming equal variances
between the populations, one assuming unequal variances in the populations, and one requiring
paired data (two measures on each element in the sample, such as salary and midpoint for each
person in our data set.) All have the same data set-up approach, so only one will be shown.
Question 2
The second question for this week asks about salary mean equality between males and
females. The data and test se.
Ashford 4 - Week 3 - Discussion 1Your initial discussio.docx
Ashford 4: - Week 3 - Discussion 1
Your initial discussion thread is due on Day 3 (Thursday) and you have until Day 7 (Monday) to respond to your classmates. Your grade will reflect both the quality of your initial post and the depth of your responses. Reference the Discussion Forum Grading Rubric for guidance on how your discussion will be evaluated.
ANOVA
In many ways, comparing multiple sample means is simply an extension of what we covered last week. Just as we had 3 versions of the t-test (1 sample, 2 sample (with and without equal variance), and paired; we have several versions of ANOVA – single factor, factorial (called 2-factor with replication in Excel), and within-subjects (2-factor without replication in Excel). What examples (professional, personal, social) can you provide on when we might use each type? What would be the appropriate hypotheses statements for each example?
Guided Response: Review several of your classmates’ posts. Respond to at least two classmates by commenting on why you agree or disagree with the statistical test that your peers have described as appropriate in this scenario.
Ashford 4: - Week 3 - Discussion 2
Your initial discussion thread is due on Day 3 (Thursday) and you have until Day 7 (Monday) to respond to your classmates. Your grade will reflect both the quality of your initial post and the depth of your responses. Reference the Discussion Forum Grading Rubric for guidance on how your discussion will be evaluated.
Effect Size
Several statistical tests have a way to measure effect size. What is this, and when might you want to use it in looking at results from these tests on job related data?
Ashford 4: - Week 3 - Assignment
Problem Set Week Three
Complete the problems included in the resources below and submit your work in an Excel document. Be sure to show all of your work and clearly label all calculations.
All statistical calculations will use the Employee Salary Data Set and the Week 3 assignment sheet.
Carefully review the Grading Rubric for the criteria that will be used to evaluate your assignment.
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: ...
DataIDSalaryCompa-ratioMidpoint AgePerformance RatingServiceGender.docx
DataIDSalaryCompa-ratioMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GradeDo not manipuilate Data set on this page, copy to another page to make changes154.50.956573485805.70METhe 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)? 228.30.913315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.334.11.100313075513.61FB460.91.06857421001605.51METhe column labels in the table mean:549.21.0254836901605.71MDID – Employee sample number Salary – Salary in thousands 674.11.1066736701204.51MFAge – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)741.41.0344032100815.71FCService – Years of service (rounded)Gender – 0 = male, 1 = female 822.80.992233290915.81FAMidpoint – salary grade midpoint Raise – percent of last raise9731.089674910010041MFGrade – job/pay gradeDegree (0= BS\BA 1 = MS)1023.31.014233080714.71FAGender1 (Male or Female)Compa-ratio - salary divided by midpoint1124.31.05723411001914.81FA1259.71.0475752952204.50ME1341.81.0444030100214.70FC14251.08523329012161FA1522.60.983233280814.91FA1648.51.213404490405.70MC1763.11.1075727553131FE1836.21.1673131801115.60FB1923.91.039233285104.61MA2035.51.1443144701614.80FB2178.91.1786743951306.31MF2257.61.199484865613.81FD2322.20.964233665613.30FA2453.41.112483075913.80FD2523.61.0282341704040MA2622.30.971232295216.20FA2746.21.156403580703.91MC2874.41.111674495914.40FF2975.61.129675295505.40MF3047.50.9894845901804.30MD3122.90.995232960413.91FA3228.10.906312595405.60MB3363.71.117573590905.51ME3426.90.869312680204.91MB3522.70.987232390415.30FA3624.41.059232775314.30FA3723.81.034232295216.20FA3864.61.1335745951104.50ME3937.31.202312790615.50FB4023.71.031232490206.30MA4140.31.008402580504.30MC4224.41.0592332100815.71FA4372.31.0796742952015.50FF4465.91.1565745901605.21ME4549.91.040483695815.21FD4657.41.0075739752003.91ME47560.982573795505.51ME4868.11.1955734901115.31FE4966.21.1615741952106.60ME5061.71.0835738801204.60ME
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 t.
Running head Organization behaviorOrganization behavior 2.docx
Running head: Organization behavior
Organization behavior 2
Organization behavior
Name:
Institution:
Course:
Date:
Organizational behavior analyzes the environment in different perspectives in order to come up with policies which make the organization convenient in its business operations. The organization must analyze various factors which affect it in order to frame the different policies. This means finding out the challenges or problems which an individual face in an organization and also the problems that groups faces in the organization. In this context, organization behavior is simply the way which an organization uses to solve the problems in its environment (Kreitner 2012). This discussion will involve Apple Inc.
One of the challenges facing Apple Inc. is managing human resources. Human resources in Apple Inc. are an invaluable asset and are always associated with the organization. Apple had experienced problems in managing its human resources. Some of the issues it experienced include failing to retain employees’ talents, not observing diverse recruitment to its fullest, non-performance among employees and employees not getting their benefits appropriately (O'Grady 2015). This went hand in hand with violation of rules governing employees, code of conduct and features which keep the value of team and organization high. The individuals’ and organization’s wellbeing depend highly on each other. This means that what people do while in the organization should reflect what is in their mind. The organizational value highly depends on social responsibility which the organization is portraying. They should put up policies for protecting the organizational environment. The issue has affected the behavior of Apple and the human resource management sorted them out (O'Grady 2015).
Managing human resources and employees ethics is a very important issue and a backbone of any organization. If managed well, the organization is likely to succeed easily. If not managed well, the issues will spoil the organization’s reputation completely and the organization may not undergo dissolution (Kreitner 2012).
References
Kreitner, Angelo Kinicki & Robert. 2012. Organization behavior. New York: Wiley.
O'Grady, Jason D. 2015. Apple Inc. Westport, Conn: Greenwood Press.
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 Midpoi ...
ScoreWeek 1.Measurement and Description - chapters 1 and 2.docx
Score:
Week 1.
Measurement and Description - chapters 1 and 2
<1 point>
1
Measurement 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, as
this 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.
Nominal
Ordinal
Interval
Ratio
b.
For each variable that you did not call ratio, why did you make that decision?
<1 point>
2
The 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.
Salary
Compa
Age
Perf. Rat.
Service
Overall
Mean
Standard Deviation
Range
Female
Mean
Standard Deviation
.
Math 009 Final Examination Spring, 2015 1 Answer Sheet M.docx
Math 009 Final Examination Spring, 2015 1
Answer Sheet
Math 009 Introductory Algebra Name______________________________
Final Examination: Spring, 2015 Instructor __________________________
Answer Sheet
Instructions:
This is an open-book exam. You may refer to your text and other course materials as you work on the exam and
you may use a calculator. Record your answers and show your work on this document. You must show your
work to receive credit: answers given with no work shown will not receive credit. You may type your work
using plain-text formatting or an equation editor, or you may hand-write your work and scan it. If you choose
to scan your work, note that most scanners have a setting that will allow you to create one PDF document from
all of the pages of your Answer Sheet – please make use of this option if it is available on your scanner.
Whether you type your work or write it by hand, show work neatly and correctly, following standard
mathematical conventions. Each step should follow clearly and completely from the previous step. If
necessary, you may attach extra pages.
You must complete the exam individually. Neither collaboration nor consultation with others is allowed.
Your exam will receive a zero grade unless you complete the following honor statement.
Please sign (or type) your name below the following honor statement:
I have completed this final examination myself, working independently and not consulting
anyone except the instructor. I have neither given nor received help on this final examination.
Name _______________ Date___________________
Math 009 Final Examination Spring, 2015 2
Answer Sheet
Problem
Number
Solution
1
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ANSWER:
2
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Math 009 Final Examination Spring, 2015 3
Answer Sheet
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Math 009 Final Examination Spring, 2015 4
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ANSWE ...
BUSI 620Questions for Critical Thinking 3Salvatore’s Chapter.docx
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 ...
MARKETING MANAGEMENT PHILOSOPHIESCHAPTER 1 - ASSIGNMENTQuest.docx
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
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1.
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A)
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B)
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C)
15
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D)
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2.
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t
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D)
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3.
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A)
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B)
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C)
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D)
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4.4) = 1.42
For the following question(s): Professor Juarez thinks the students in her statistics class this term are more creative than most students at this university. A previous study found that students at this university had a mean score of 35 on a standard creativity test. Professor Juarez finds that her class scores an average of 40 on this scale, with an estimated population standard deviation of 7. The standard deviation of the distribution of means comes out to 1.63.
4.
What is the
t
score?
A)
(40
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35)/7 = .71
B)
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35)/1.63 = 3.07
C)
(40
-
35)/7
2
= 5/49 = .10
D)
(40
-
35)/1.63
2
= 5/2.66 = 1.88
5.
What effect size did Professor Juarez find?
A)
(40
-
35)/7 = .71
B)
(40
-
35)/1.63 = 3.07
C)
(40
-
35)/7
2
= 5/49 = .10
D)
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-
35)/1.63
2
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6.
If Professor Juarez had 30 students in her class, and she wanted to test her hypothesis using the 5% level of significance, what cutoff
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A)
304.11
B)
1.699.
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Subject:
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1.
Specific Hypothesis
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2.
Applicable Sociological Concepts
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3.
Practical Implications
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Conclusions
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I.
Specific Hypothesis.
II.
Applicable Sociological Concepts.
a.
Theory A
b.
Concept 1
c.
Concept 2
III.
Practical Implications.
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ii.
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References
:
Baker, A. & Abel, E (2005) Villagers reject modern attitudes about car washing.
International Journal of Sociology
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Doe, J. (2010, April 1) Villagers retain traditional attitudes despite bombardment with western television.
The New York Times
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www.nytimes.com/village_update
Steiner, H. (2012, January 4) Revolt against local ordinances in the village.
Time Magazine
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The specific course learning outcomes associated with this assignment are:
Evaluate the various methodologies for sociological research.
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Use technology and information resources to research issues in sociology.
Write clearly and concisely about sociology using proper writing mechanics.
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Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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- 1. Week 4 Confidence Intervals and Chi Square (Chs 11 - 12) For questions 3 and 4 below, be sure to list the null and alternate hypothesis statements. Use .05 for your significance level in making your decisions. For full credit, you need to also show the statistical outcomes - either the Excel test result or the calculations you performed.
- 2. 1 Using our sample data, construct a 95% confidence interval for the population's mean salary for each gender. Interpret the results. How do they compare with the findings in the week 2 one sample t-test outcomes (Question 1)? Mean St error t value Low to High Males
- 4. 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?
- 7. Yes/No Can the means be equal? Why?
- 8. How does this compare to the week 2, question 2 result (2 sampe t-test)? a. Why is using a two sample tool (t-test, confidence interval) a better choice than using 2 one-sample techniques when comparing two samples?
- 9. 3 We found last week that the degree values within the population do not impact compa rates. This does not mean that degrees are distributed evenly across the grades and genders.
- 10. Do males and females have athe same distribution of degrees by grade? (Note: while technically the sample size might not be large enough to perform this test, ignore this limitation for this exercise.) What are the hypothesis statements:
- 11. Ho: Ha:
- 12. Note: You can either use the Excel Chi-related functions or do the calculations manually. Data input tables - graduate degrees by gender and grade level OBSERVED A B C D E F Total If desired, you can do manual calculations per cell here. M Grad
- 13. A B C D E Fem Grad M Grad Male Und Fem Grad Female Und
- 16. For this exercise - ignore the requirement for a correction factor Fem Grad for cells with expected values less than 5. Male Und Female Und
- 19. Interpretation: What is the value of the chi square statistic:
- 20. What is the p-value associated with this value: Is the p-value <0.05? Do you reject or not reject the null hypothesis:
- 21. If you rejected the null, what is the Cramer's V correlation: What does this correlation mean? What does this decision mean for our equal pay question:
- 23. 4 Based on our sample data, can we conclude that males and females are distributed across grades in a similar pattern within the population? What are the hypothesis statements:
- 24. Ho: Ha:
- 25. Do manual calculations per cell here (if desired) A B C D E F A B
- 26. C D OBS COUNT - m M OBS COUNT - f F
- 27. Sum = EXPECTED
- 29. What is the value of the chi square statistic: What is the p-value associated with this value:
- 30. Is the p-value <0.05? Do you reject or not reject the null hypothesis: If you rejected the null, what is the Phi correlation:
- 31. What does this correlation mean?
- 32. What does this decision mean for our equal pay question: 5. How do you interpret these results in light of our question about equal pay for equal work?