BUS 308 Full Week 5
BUS 308 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- five double-spaced pages in length, and formatted according to APA style as outlined in the Ashford Writing Center.
- Must include a title page with the following: (a). Title of paper (b). Student’s name (c). Course name and number (d). Instructor’s name (e). Date submitted
- …… begin with an introductory paragraph that has a succinct thesis statement.
- ……. address the topic of the paper with critical thought.
- ….. end with a conclusion that reaffirms your thesis.
- ……… use at least three scholarly sources, in addition to the text.
- ……….. document all sources in APA style, as outlined in the Ashford Writing Center.
- Must include a separate reference page, formatted according to APA style as outlined in the Ashford Writing Center.
BUS-308 ASSIGNMENT WEEK 5
Problem Set Week Five
- Create a correlation table for the variables in our Employee Salary 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 the table here. c. Using r= approximately .28 as the significant r value (at p = .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 information help us answer our equal pay for equal work question?
- Below is a regression analysis for salary being predicted/explained by the other variables in our sample (Midpoint, age, performance rating, service, raise, 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. Please interpret the findings.
- 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? If so, which gender gets paid more? How do we know? Which is the best variable to use in analyzing pay practices – salary or compa? Why? What is the most interesting or surprising thing about the results we got doing the analyses during the last 5 weeks?
- 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 Discussion 5-1/ Correlation: 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?
BUS-308 Discussion 5-2/ Regression: At times we can generate a regression equation to explain outcomes. For example, an employee’s 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?