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##### BUS 308 Statistics for Managers, Ashford, Week 1 to 5

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Question;BUS 308 Week 1 DQ 1 Language;Numbers and measurements are the;language of business.. Organizations look at results, expenses, quality levels;efficiencies, time, costs, etc. What measures does your department keep track;of? How are the measures collected, and how are they summarized/described? How;are they used in making decisions? (Note: If you do not have a job where;measures are available to you, ask someone you know for some examples or;conduct outside research on an interest of yours.);BUS 308 Week 1 DQ 2 Levels;Managers and professionals often pay;more attention to the levels of their measures (means, sums, etc.) than to the;variation in the data (the dispersion or the probability patterns/distributions;that describe the data). For the measures you identified in Discussion 1, why;must dispersion be considered to truly understand what the data is telling us;about what we measure/track? How can we make decisions about outcomes and;results if we do not understand the consistency (variation) of the data? Does;looking at the variation in the data give us a different understanding of;results?;BUS 308 Week 1 Problem Set Week One;Problem Set Week One. All;statistical calculations will use the Employee Salary Data set (in Appendix;section).;Using the Excel Analysis ToolPak;function Descriptive Statistics, generate descriptive statistics for the salary;data. Which variables does this function not work properly for, even though we;have some generated results?;Sort the data by either the variable;G or GEN1 (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;Descriptive for one gender and the fx functions (AVERAGE and STDEV) for the;other.;What is the probability distribution;table for;A randomly selected person being a;male in a specific grade?;A randomly selected person being in;a specific grade?;Find;The z score for each male salary;based on the male salary distribution.;The z score for each female salary, based;on the female salary distribution.;Repeat question 4 for COMPA for each;gender.;What conclusions can you make about;the issue of male and female pay equality? Are all of the results;consistent? If not, why not?;For additional assistance with these;calculations reference the Recommended Resources for Week One.;BUS 308 Week 2 DQ 1 t-Tests;t-Tests.;In looking at your business, when;and why would you want to use a one-sample mean test (either z or t) or a two-;sample t-test? Create a null and alternate hypothesis for one of these issues.;How would you use the results?;BUS 308 Week 2 DQ 2 Variation;Variation exists in virtually all;parts of our lives. We often see variation in results in what we spend (utility;costs each month, food costs, business supplies, etc.). Consider the measures;and data you use (in either your personal or job activities). When are;differences (between one time period and another, between different production;lines, etc.) between average or actual results important? How can you or your;department decide whether or not the variation is important? How could using a;mean difference test help?;BUS 308 Week 2 Problem Set Week Two;Problem Set Week Two. 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 the Employee Salary Data set (in Appendix section).;Problems;Is either that male or female salary;equal to the overall mean salary? (Two hypotheses, one-sample tests needed.);Are male and female average salaries;statistically equal to each other?;Are the male and female compa;average measures equal to each other?;4. If the salary and compa mean;tests in questions 2 and 3 provide different equality results, which would be;more appropriate to use in answering the question about salary equity? Why?;5. What other information would you;like to know to answer the question about salary equity between the genders?;Why?;BUS 308 Week 3 DQ 1 ANOVA;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?;BUS 308 Week 3 DQ 2 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?;BUS 308 Week 3 Problem Set Week;Three;Problem Set Week Three. 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 the Employee Salary Data set (in Appendix section).;1.Is the average salary the same for;each of the grade levels? (Assume equal variance, and use the Analysis ToolPak;function ANOVA.) Set up the data input table/range to use as follows:?Put;all of the salary values for each grade under the appropriate grade label.;2.The factorial ANOVA with only two;variables can be done with the Analysis ToolPak function two-way ANOVA with;replication. Set up a data input table like the following: Grade For each empty;cell, randomly pick a male or female salary from each grade. Interpret the;results. Are the average salaries for each gender (listed as sample) equal? Are;the average salaries for each grade (listed as column) equal?;3.Repeat question 2 for the compa;values. Grade;For each empty cell randomly pick a;male or female compa from each grade. Interpret the results. Are the average;compas for each gender (listed as sample) equal? Are the average compas for;each grade (listed as column) equal?;4.Pick any other variable you are;interested in and do a simple two-way ANOVA without replication. Why did you;pick this variable, and what do the results show?;5.What are your conclusions about;salary equity now?;BUS 308 Week 4 DQ 1 Confidence Intervals;Earlier we discussed issues with;looking at only a single measure to assess job-related results. 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?;BUS 308 Week 4 DQ 2 Chi-Square Tests;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?;BUS 308 Week 4 Problem Set Week Four;Problem Set Week Four. Let?s look at;some other factors that might influence pay. 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 the;Employee Salary Data set (in Appendix section).;Is the probability of having a;graduate degree independent of the grade the employee is in?;Construct a 95% confidence interval;on the mean service for each gender. Do they intersect?;Are males and females distributed;across grades in a similar pattern?;Do 95% confidence intervals on the;mean length of service for each gender intersect?;How do you interpret these results;in light of our equity question?;BUS 308 Week 5 DQ 1 Correlation;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?;BUS 308 Week 5 DQ 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?;BUS 308 Week 5 Final Paper;Identify;an issue in your life (work place, home, social organization, etc.) where a;statistical analysis could be used to help make a managerial decision. Develop;a sampling plan, an appropriate set of hypotheses, and an inferential;statistical procedure to test them. You do not need to collect any data on this;issue, but you will discuss what a significant statistical test would mean and;how you would relate this result to the real-world issue you identified. Your;paper should be three to five pages in length (excluding the cover and;reference pages). In addition to the text, utilize at least three sources to to;support your points. No abstract is required. Use the following research plan;format to structure the paper;Step 1: Identification of the;problem;Describe what is known about the situation, why it is a concern, and what we do;not know.;Step 2: Research Question;What exactly do we want our study to find out? This should not be phrased as a;yes/no question.;Step 3: Data collection;What data is needed to answer the question, how will we collect it, and how;will we decide how much we need?;Step 4: Data Analysis;Describe how you would analyze the data. Provide at least one hypothesis test;(null and alternate) and an associated statistical test.;Step 5: Results and Conclusions;Describe how you would interpret the results. For example, what would you;recommend if your null hypothesis was rejected and what would you do if the;null was not rejected?;A quick example: Concern if gender;is impacting employee?s pay. H0: Gender is not related to pay. H1: Gender is;related to pay. Approach: Multiple regression equation to see if gender impacts;pay after considering the legal factors of grade, appraisal, education, etc. If;regression coefficient for gender is significant, will need to create residual;list to see which employees show excessive variation from predicted salaries;when gender is not considered.

Paper#37951 | Written in 18-Jul-2015

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