BUS 308 NEW (Ashford) Week 1 to Week 5 – Complete Class
BUS 308 NEW (Ashford) Week 1: Problem Set
All statistical calculations will use Employee Salary Data Set
Using the Excel Analysis ToolPak or the StatPlus:mac LE software function descriptive statistics, generate and show the descriptive
statistics for each appropriate variable in the sample data set.
For which variables in the data set does this function not work correctly for? Why?
Sort the data by Gen or Gen 1 (into males and females) and find the mean and standard deviation
for each gender for the following variables:
sal, compa, age, sr and raise. Use either the descriptive stats function or the Fx functions (average and stdev).
What is the probability for a:
Randomly selected person being a male in grade E?
Randomly selected male being in grade E?
Why are the results different?
Find:
The z score for each male salary, based on only the male salaries.
The z score for each female salary, based on only the female salaries.
The z score for each female compa, based on only the female compa values.
The z score for each male compa, based on only the male compa values.
What do the distributions and spread suggest about male and female salaries?
Why might we want to use compa to measure salaries between males and females?
Based on this sample, what conclusions can you make about the issue of male and female pay equality?
Are all of the results consistent with your conclusion? If not, why not?
BUS 308 NEW (Ashford) Week 2: Problem Set
Complete the problems 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 theEmployee Salary Data Set.
Included in the Week Two tab of the Employee Salary Data Set are 2 one-sample t-tests comparing male and female average salaries to the overall sample mean.
Based on our sample, how do you interpret the results and what do these results suggest about the population means for male and female salaries?
Based on our sample results, perform a 2-sample t-test to see if the population male and female salaries could be equal to each other.
Based on our sample results, can the male and female compas in the population be equal to each other? (Another 2-sample t-test.)
What other information would you like to know to answer the question about salary equity between the genders? Why?
If the salary and compa mean tests in questions 3 and 4 provide different results about male and female salary equality, which would be more appropriate to use in answering the question about salary equity? Why? What are your conclusions about equal pay at this point?
BUS 308 NEW (Ashford) Week 3: Problem Set
Complete the problems 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 Employee Salary Data Set.
Based on the sample data, can the average (mean) salary in the population be the same for each of the grade levels? (Assume equal variance, and use the Analysis Toolpak or the StatPlus:mac LE software function ANOVA.) Set up the input table/range to use as follows: Put all of the salary values for each grade under the appropriate grade label.Be sure to include the null and alternate hypothesis along with the statistical test and result.
The table and analysis below demonstrate a 2-way ANOVA with replication. Please interpret the results.
Using our sample results, can we say that the compa values in the population are equal by grade and/or gender, and are independent of each factor?
Pick any other variable you are interested in and do a simple 2-way ANOVA without replication. Why did you pick this variable and what do the results show?
Using the results for this week, What are your conclusions about gender equal pay for equal work at this point?
BUS 308 NEW (Ashford) Week 4: Problem Set
Lets look at some other factors that might influence pay. Complete the Employee Salary Data Set.
One question we might have is if the distribution of graduate and undergraduate degrees independent of the grade the employee? (Note: this is the same as asking if the degrees are distributed the same way.) Based on the analysis of our sample data (shown below), what is your answer?
Using our sample data, we can 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)?
Based on our sample data, can we conclude that males and females are distributed across grades in a similar pattern within the population?
Using our sample data, construct a 95% confidence interval for the population’s mean service difference for each gender. Do they intersect or overlap? How do these results compare to the findings in Statistics Assignment 2,question 2?
How do you interpret these results in light of our question about equal pay for equal work?
BUS 308 NEW (Ashford) Week 5: Problem Set
Create a correlation table for the variables in our Employee Salary Data Set. (Use analysis ToolPak or the StatPlus:mac LE software function Correlation.)
Interpret the results. What variables seem to be important in seeing if we pay males and females equally for equal work?
Below is a regression analysis for salary being predicted/explained by the other variables in our sample (Mid, age, ees, sr, raise, and deg variables.)
Note: since salary and compa are different ways of expressing an employees salary, we do not want to have both used in the same regression.
Perform a regression analysis using compa as the dependent variable and the same independent variables as used in question 2. Show the result, and interpret your findings by answering the same questions.
Note: be sure to include the appropriate hypothesis statements.
Based on all of your results to date, is gender a factor in the pay practices of this company? Why or why not? Which is the best variable to use in analyzing pay practices – salary or compa? Why?
Why did the single factor tests and analysis (such as t and single factor ANOVA tests on salary equality) not provide a complete answer to our salary equality question? What outcomes in your life or work might benefit from a multiple regression examination rather than a simpler one variable test?
BUS 308 NEW (Ashford) Week 5: Final Paper
The final paper provides you with an opportunity to integrate and reflect on what you have learned during the class.
The question to address is: What have you learned about statistics? In developing your responses, consider at a minimum and discuss the application of each of the course elements in analyzing and making decisions about data (counts and/or measurements).
The course elements include:
Descriptive statistics
Inferential statistics
Hypothesis development and testing
Selection of appropriate statistical tests
Evaluating statistical results.
Writing the Final Paper
The Final Paper:
Must be three to four pages in length, and formatted according to APA style.
Must include a title page with the following:
Title of paper
Students name
Course name and number
Instructors name
Date submitted
Must begin with an introductory paragraph that has a succinct thesis statement.
Must address the topic of the paper with critical thought.
Must end with a conclusion that reaffirms your thesis.
Must use at least 4 scholarly sources, in addition to the text.
Must document all sources in APA style.
Must include a separate reference page, formatted according to APA style
BUS 308 NEW (Ashford) Week 3 to Week 5 Discussions
Week 3 DQs
DQ 1:In many ways, comparing multiple sample means is simply an extension of what we covered last week. What situations exist where a multiple (more than two) group comparison would be appropriate? (Note: Situations could relate to your work, home life, social groups, etc.). Create a null and alternate hypothesis for one of these issues. What would the results tell you?
DQ 2: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?
Week 4 DQs
DQ 1: Looking back at the data examples you have provided in the previous discussion questions on this issue, how might adding confidence intervals help managers understand results better?
DQ 2:What are some examples of variables that you might want to check using the chi-square tests?
Week 5 DQs
DQ 1: What results in your departments seem to be correlated or related to other activities? How could you verify this? Create a null and alternate hypothesis for one of these issues. What are the managerial implications of a correlation between these variables?
DQ 2:What times we can generate a regression equation to explain outcomes. For example, an employees salary can often be explained by their pay grade, appraisal rating, education level, etc. What variables might explain or predict an outcome in your department or life? If you generated a regression equation, how would you interpret it and the residuals from it?
