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BUS 308 ENTIRE COURSE – Latest Version Oct 2013

 

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

A

B

C

D

E

F

 

Gender

A

B

C

D

E

F

M

F

 

Gender

A

B

C

D

E

F

M

F

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.

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