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##### Ashford BUS 308 Course / New Syllabus Entire Course {Instant Download} 100% ORIGINAL

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BUS 308 Course / New Syllabus;BUS 308 Week 1 DQ 1 Language;BUS 308 Week 1 DQ 2 Levels;BUS 308 Week 1 Problem Set Week One;BUS 308 Week 1 Quiz;BUS 308 Week 2 DQ 1 t-Tests;BUS 308 Week 2 DQ 2 Variation;BUS 308 Week 2 Problem Set Week Two;BUS 308 Week 2 Quiz;BUS 308 Week 3 DQ 1 ANOVA;BUS 308 Week 3 DQ 2 Effect Size;BUS 308 Week 3 Final Outline Draft;BUS 308 Week 3 Problem Set Week Three;BUS 308 Week 4 DQ 1 Confidence Intervals;BUS 308 Week 4 DQ 2 Chi-Square Tests;BUS 308 Week 4 Problem Set Week Four;BUS 308 Week 4 Quiz;BUS 308 Week 5 DQ 1 Correlation;BUS 308 Week 5 DQ 2 Regression;BUS 308 Week 5 Final Paper;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 measurescollected, 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.);Guided Response: Review several of your classmates? posts. Respond to at least two of your classmates by providing recommendations for the measures being discussed.;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?;Guided Response: Review several of your classmates? posts. Respond to at least two classmates by commenting on the situations that are being illustrated.;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).;1. Using the Excel Analysis ToolPak or StatPlus:mac LE function descriptive statistics, generate and show the;descriptive statistics for each appropriate variable in the sample data set.;a. For which variables in the data set does this function not work correctly for? Why?;2. Sort the data by Gen or Gen 1 (into males and females) and find the mean and standard deviation for each gender for the following variables;a. sal, compa, age, sr and raise. Use either the descriptive stats function or the Fx functions (average and stdev).;3. What is the probability for a;a. Randomly selected person being a male in grade E?;b. Randomly selected male being in grade E?;c. Why are the results different?;4. Find;a. The z score for each male salary, based on only the male salaries.;b. The z score for each female salary, based on only the female salaries.;c. The z score for each female compa, based on only the female compa values;d. The z score for each male compa, based on only the male compa values.;e. What do the distributions and spread suggest about male and female salaries?;f. Why might we want to use compa to measure salaries between males and females?;5. Based on this sample, what conclusions can you make about the issue of male and female pay equality?;6. Are all of the results consistent with your conclusion? If not, why not?;BUS 308 Week 2 DQ 1 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 twosample t-test? Create a null and alternate hypothesis for one of these issues. How would you use the results?;Guided Response: Review several of your classmates? posts. Respond to at least two classmates by commenting on the potential differences in the results and how that might affect decision making.;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?;Guided Response: Review several of your classmates? posts. Respond to at least two classmates and comment on the use of the test.;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.;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.;1. 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?;2. 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.;3. Based on our sample results, can the male and female compas in the population be equal to each other? (Another 2-sample t-test.);4. What other information would you like to know to answer the question about salary equity between the genders? Why?;5. 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 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: Situationscould 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?;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.;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. 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 StatPlus:mac LE 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.;2. The table and analysis below demonstrate a 2-way ANOVA with replication. Please interpret the results.;3. 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?;4. 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?;5. Using the results for this week, What are your conclusions about gender equal pay for equal work at this point;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?;Guided Response: Review several of your classmates? posts. Respond to at least two classmates by commenting on whether or not you think changing the confidence intervals will result in a different outcome. Explain if you agree or disagree with the role of a confidence interval in the interpretation of the answer.;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?;Guided Response: Review several of your classmates? posts. Respond to at least two classmates by commenting on how this information might be used to make business decisions.;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).;1. How do you interpret these results in light of our equity question? 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?;2. 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)?;3. Based on our sample data, can we conclude that males and females are distributed across grades in a similar pattern within the population?;4. 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 week 2, question 2?;5. How do you interpret these results in light of our question about equal pay for equal work?;BUS 308 Week 5 DQ 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?;Guided Response: Review several of your classmates? posts. Respond to at least two classmates by;explaining whether or not you think that there is a relationship between the variables discussed.;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?;Guided Response: Review several of your classmates? posts. Respond to at least two classmates by commenting on how this information might be used to make business decisions.;BUS 308 Week 5 Final Paper;The final assignment for this course is a Final Paper. The purpose of the Final Paper is for you to culminate the learning achieved in the course by creating a sales report. The Final Paper represents 25% of the overall course grade.;Writing the 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;BUS 308 Week 5 Problem Set Week Five;Problem Set Week Five.;1. Create a correlation table for the variables in our Employee Salary Data Set. (Use analysis ToolPak or StatPlus:mac LE function Correlation.) Interpret the results.;a. What variables seem to be important in seeing if we pay males and females equally for equal work?;2. 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 employee?s salary, we do not want to have both used in the same regression.;3. 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;4. 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?;5. 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?

Paper#81067 | Written in 18-Jul-2015

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