# BUS 308 BUS308 BUS/308 ENTIRE COURSE HELP – ASHFORD UNIVERSITY

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## BUS 308 BUS308 BUS/308 ENTIRE COURSE HELP – ASHFORD UNIVERSITY

**BUS 308 Entire Course (New)**

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## BUS 308 BUS308 BUS/308 ENTIRE COURSE HELP – ASHFORD UNIVERSITY

**BUS 308 Entire Course (New)**

## BUS 308 BUS308 BUS/308 ENTIRE COURSE HELP – ASHFORD UNIVERSITY

**BUS 308 Week 1 DQ 1**

**Part Two – Data Characteristics**

Read Lecture One on descriptive data and review the Employee Data . Be sure to familiarize yourself with the different variables shown on the Data tab. In this course, we will be using the Employee Data and statistical tools to answer a single research question: In our BUS308 company, are the males and females paid equally for equal work?

Lecture One discusses different ways data values can be classified. In our data set for the equal pay for equal work assignment, students in the past have correctly identify the variable gender (coded M and F for male and female respectively) as nominal level data, but they often see gender1 (coded 0 and 1 for male and female respectively) as interval or ratio level data. Why? What could cause this wrong classification? What data do you use in your personal or professional lives that might suffer from not being correctly labeled/understood?

**Part Three –Descriptive Statistics**

Read Lecture Two on describing data sets and view *The Role of Data & Analytics Today* video (https://www.youtube.com/watch?v=fxroi4beKhE). Lecture Two discusses several different ways of summarizing a data set–central location, variability, etc. Often, business reports provide a mean or average value for some measure (such as average number of defects per production run). Why is the average alone not enough information to make informed judgements about the result? What other descriptive statistic should be included? Why? Can you illustrate this with an example from your personal or professional lives? (This should be started on Day 3.)

**Part Four – Probability**

Read Lecture Three on probability. Lecture Three introduces the idea of probability—a measure of how likely it is to get a particular outcome. Looking at outcomes as resulting from probabilities (somewhat random outcomes/selections) rather than fixed constants often changes the way we see things. How does considering the salary outcomes in our sample the result of a probabilistic sample rather than a completely accurate and precise reflection of the population change how we interpret the sample statistic outcomes? What results in your personal or professional lives could be viewed this way? What differences would this cause? Why?

Your responses should be separated in the initial post, addressing each part individually,

## BUS 308 BUS308 BUS/308 ENTIRE COURSE HELP – ASHFORD UNIVERSITY

**BUS 308 Week 1 DQ 2**

DQ #2: Webliography

Post a question that you had related to the material this week. Conduct research to provide the answer to the question and provide the source.

## BUS 308 BUS308 BUS/308 ENTIRE COURSE HELP – ASHFORD UNIVERSITY

**BUS 308 Week 1 Quiz (2 Set)**

## BUS 308 BUS308 BUS/308 ENTIRE COURSE HELP – ASHFORD UNIVERSITY

**BUS 308 Week 2 DQ 1**

**DQ #1: Hypothesis Testing / T-tests / F-test**

Although the initial post is due on Day 5, you are encouraged to start working on it early, as it is a three-part discussion that should be completed in sequential order.

Part One – Hypothesis Testing

Read Lecture Four. Lecture Four starts out with the five-step procedure for hypothesis testing. What is this? What does it do for us? Why do we need to follow these steps in making a judgement about the populations our samples came from? What are the “tricky” parts of developing appropriate hypotheses to test? What examples can you suggest where this process might be appropriate in your personal or professional lives? (This should be started on Day 1.)

Part Two – T-tests

Read Lecture Five. Lecture Five illustrates several t-tests on the data set. What conclusions can you draw from these tests about our research question on equal pay for equal work? What is missing from these results to give us a complete answer to the question? Why? (This should be started on Day 3.)

Part Three – F-test

Read Lecture Six. Lecture Six introduces you to the F-test for variance equality. Last week, we discussed how adding a variation measure to reports of means was a smart thing to do. Why does variation make our analysis of the equal pay for equal work question more complicated? What causes of variation impact salary that we have not discussed yet? How can you relate this issue to measures used in your personal or professional lives? (This should be completed by Day 5.)

## BUS 308 BUS308 BUS/308 ENTIRE COURSE HELP – ASHFORD UNIVERSITY

**BUS 308 Week 2 DQ 2**

Post a question that you had related to the material this week. Conduct research to provide the answer to the question and provide the source

## BUS 308 BUS308 BUS/308 ENTIRE COURSE HELP – ASHFORD UNIVERSITY

**BUS 308 Week 2 Problem Set**

Before starting this assignment, make sure the the assignment data from the Employee Salary Data Set file is copied over to this Assignment file. You can do this either by a copy and paste of all the columns or by opening the data file, right clicking on the Data tab, selecting Move or Copy, and copying the entire sheet to this file (Weekly Assignment Sheet or whatever you are calling your master assignment file).

It is highly recommended that you copy the data columns (with labels) and paste them to the right so that whatever you do will not disrupt the original data values and relationships.

To Ensure full credit for each question, you need to show how you got your results. For example, Question 1 asks for several data values. If you obtain them using descriptive statistics, then the cells should have an “=XX” formula in them, where XX is the column and row number showing the value in the descriptive statistics table. If you choose to generate each value using fxfunctions, then each function should be located in the cell and the location of the data values should be shown. So, Cell D31 – as an example – shoud contain something like “=T6” or “=average(T2:T26)”. Having only a numerical value will not earn full credit. The reason for this is to allow instructors to provide feedback on Excel tools if the answers are not correct – we need to see how the results were obtained.

In starting the analysis on a research question, we focus on overall descriptive statistics and seeing if differences exist. Probing into reasons and mitigating factors is a follow-up activity.

1 The first step in analyzing data sets is to find some summary descriptive statistics for key variables. Since the assignment problems will focus mostly on the compa-ratios, we need to find the mean, standard deviations, and range for our groups: Males, Females, and Overall. Sorting the compa-ratios into male and females will require you copy and paste the Compa-ratio and Gender1 columns, and then sort on Gender1.

The values for age, performance rating, and service are provided for you for future use, and – if desired – to test your approach to the compa-ratio answers (see if you can replicate the values).

You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =stdev functions. The range can be found using the difference between the =max and =min functions with Fx functions or from Descriptive Statistics.

Suggestion: Copy and paste the compa-ratio data to the right (Column T) and gender data in column U. If you use Descriptive statistics, Place the output table in row 1 of a column to the right. If you did not use Descriptive Statistics, make sure your cells show the location of the data (Example: =average(T2:T51)

A key issue in comparing data sets is to see if they are distributed/shaped the same.

At this point we can do this by looking at the probabilities that males and females are distributed in the same way for a grade levels.

2 Empirical Probability: What is the probability for a:

a. Randomly selected person being in grade E or above?

b. Randomly selected person being a male in grade E or above?

c. Randomly selected male being in grade E or above?

d. Why are the results different?

3 Normal Curve based probability: For each group (overall, females, males), what are the values for each question below?:

Make sure your answer cells show the Excel function and cell location of the data used.

A The probability of being in the top 1/3 of the compa-ratio distribution.

Note, we can find the cutoff value for the top 1/3 using the fx Large function: =large(range, value).

Value is the number that identifies the x-largest value. For the top 1/3 value would be the value that starts the top 1/3 of the range,

For the overall group, this would be the 50/3 or 17th (rounded), for the gender groups, it would be the 25/3 = 8th (rounded) value.

i. How nany salaries are in the top 1/3 (rounded to nearest whole number) for each group?

ii What Compa-ratio value starts the top 1/3 of the range for each group?

iii What is the z-score for this value?

iv. What is the normal curve probability of exceeding this score?

B How do you interpret the relationship between the data sets? What does this suggest about our equal pay for equal work question?

4 Based on our sample data set, can the male and female compa-ratios in the population be equal to each other?

A First, we need to determine if these two groups have equal variances, in order to decide which t-test to use.

What is the data input ranged used for this question:

Step 1:

Ho:

Ha:

Step 2:

Decision Rule:

Step 3:

Statistical test:

Why?

Step 4: C

Conduct the test – place cell B77 in the output location box.

Step 5: Conclusion and Interpretation

What is the p-value:

Is the P-value < 0.05 (for a one tail test) or 0.025 (for a two tail test)?

What is your decision:

REJ or NOT reject the null?

What does this result say about our question of variance equality?

B Are male and female average compa-ratios equal?

(Regardless of the outcome of the above F-test, assume equal variances for this test.)

What is the data input ranged used for this question:

Step 1:

Ho:

Ha:

Step 2: Decision Rule:

Step 3: Statistical test:

Why?

Step 4: Conduct the test – place cell B109 in the output location box.

Step 5: Conclusion and Interpretation

What is the p-value: Is the P-value < 0.05 (for a one tail test) or 0.025 (for a two tail test)?

What is your decision:

REJ or NOT reject the null?

What does your decision on rejecting the null hypothesis mean?

If the null hypothesis was rejected, calculate the effect size value:

If the effect size was calculated, what doe the result mean in terms of why the null hypothesis was rejected?

What does the result of this test tell us about our question on salary equality?

5 Is the Female average compa-ratio equal to or less than the midpoint value of 1.00?

This question is the same as:

Does the company, pay its females – on average – at or below the grade midpoint (which is considered the market rate)?

Suggestion: Use the data column T to the right for your null hypothesis value.

What is the data input ranged used for this question:

Step 1:

Ho:

Ha:

Step 2: Decision Rule:

Step 3: Statistical test: Why?

Step 4: Conduct the test – place cell B162 in the output location box.

Step 5: Conclusion and Interpretation

What is the p-value: Is the P-value < 0.05 (for a one tail test) or 0.025 (for a two tail test)?

What, besides the p-value, needs to be considered with a one tail test?

Decision: Reject or do not reject Ho?

What does your decision on rejecting the null hypothesis mean?

If the null hypothesis was rejected, calculate the effect size value:

If the effect size was calculated, what doe the result mean in terms of why the null hypothesis was rejected?

What does the result of this test tell us about our question on salary equality?

6 Considering both the salary information in the lectures and your compa-ratio information, what conclusions can you reach about equal pay for equal work?

Why – what statistical results support this conclusion?

## BUS 308 BUS308 BUS/308 ENTIRE COURSE HELP – ASHFORD UNIVERSITY

**BUS 308 Week 2 Quiz (3 Set)**

## BUS 308 BUS308 BUS/308 ENTIRE COURSE HELP – ASHFORD UNIVERSITY

**BUS 308 Week 3 DQ 1**

**Part One – Multiple Testing**

Read Lecture Seven. The lectures from last week and Lecture Seven discuss issues around using a single test versus multiple uses of the same tests to answer questions about mean equality between groups. This suggests that we need to master—or at least understand—a number of statistical tests. Why can’t we just master a single statistical test—such as the t-test—and use it in situations calling for mean equality decisions? (This should be started on Day 1.)

**Part Two – ANOVA**

Read Lecture Eight. Lecture Eight provides an ANOVA test showing that the mean salary for each job grade significantly differed. It then shows a technique to allow us to determine which pair or pairs of means actually differ. What other factors would you be interested in knowing if means differed by grade level? Why? Can you provide an ANOVA table showing these results? (Do not bother with which means differ.) How does this help answer our research question of equal pay for equal work? What kinds of results in your personal or professional lives could use the ANOVA test? Why? (This should be started on Day 3.)

**Part Three – Effect Size**

Read Lecture Nine. Lecture Nine introduces you to Effect size measure. There are two reasons we reject a null hypothesis. One is that the interaction of the variables causes significant differences to occur – our typical understanding of a rejected null hypothesis. The other is having a large sample size – virtually any difference can be made to appear significant if the sample is large enough. What is the Effect size measure? How does it help us decide what caused us reject the null hypothesis?

## BUS 308 BUS308 BUS/308 ENTIRE COURSE HELP – ASHFORD UNIVERSITY

**BUS 308 Week 3 DQ 2**

DQ #2: Webliography

Post a question that you had related to the material this week. Conduct research to provide the answer to the question and provide the source.

## BUS 308 BUS308 BUS/308 ENTIRE COURSE HELP – ASHFORD UNIVERSITY

**BUS 308 Week 3 Problem Set (Anova)**

During this week, we will look at ways of testing multiple (more than two) data samples at the same time.

We will continue to use the data and assignment file that we opened in Week 2, we just move on to the Week 3 tab.

The first question asks us to determine if the average compa-ratio is equal across 10K salary groups (20 – 29K. 30 – 39K, etc.). The second question asks us to identify which of the salary groups have different averages. The final question asks us to interpret the new information presented in the lecture and assignment; how does the new information we analyzed help us answer our equal pay for equal work question.

The data and assignment file can be found in the Course Materials link, at the bottom in the Multi-Media section. If you save the files from last week, you do not need to open them again.

Week 3 ANOVA Three Questions

Remember to show how you got your results in the appropriate cells. For questions using functions, show the input range when asked.

1 One interesting question is are the average compa-ratios equal across salary ranges of 10K each. While compa-ratios remove the impact of grade on salaries, are they different for different pay levels, that is are people at different levels paid differently relative to the midpoint? (Put data values at right.)

What is the data input ranged used for this question:

Step 1:

Ho:

Ha:

Step 2: Decision Rule:

Step 3: Statistical test:

Why?

Step 4: Conduct the test – place cell b16 in the output location box.

Step 5: Conclusions and Interpretation

What is the p-value?

Is P-value < 0.05?

What is your decision: REJ or NOT reject the null?

If the null hypothesis was rejected, what is the effect size value (eta squared)?

If calculated, what does the effect size value tell us about why the null hypothesis was rejected?

What does that decision mean in terms of our equal pay question?

2 If the null hypothesis in question 1 was rejected, which pairs of means differ? Why?

Groups Compared Diff T +/- Term Low to High Difference Significant? Why?

G1 G2

G1 G3

G1 G4

G1 G5

G1 G6

G2 G3

G2 G4

G2 G5

G2 G6

G3 G4

G3 G5

G3 G6

G4 G5

G4 G6

G5 G6

3 Since compa is already a measure of pay for equal work, do these results impact your conclusion on equal pay for equal work? Why or why not?

## BUS 308 BUS308 BUS/308 ENTIRE COURSE HELP – ASHFORD UNIVERSITY

**BUS 308 Week 3 Quiz (3 Set)**

## BUS 308 BUS308 BUS/308 ENTIRE COURSE HELP – ASHFORD UNIVERSITY

**BUS 308 Week 4 DQ 1**

## BUS 308 BUS308 BUS/308 ENTIRE COURSE HELP – ASHFORD UNIVERSITY

**BUS 308 Week 4 DQ 2**

DQ #2: Webliography

Post a question that you had related to the material this week. Conduct research to provide the answer to the question and provide the source.

## BUS 308 BUS308 BUS/308 ENTIRE COURSE HELP – ASHFORD UNIVERSITY

**BUS 308 Week 4 Problem Set (Regression and Correlation)**

Problem Set Week Four

This week we get to answer our equal pay for equal work question by looking at relationships between and among the different variables.

The first question this week looks at correlations and the creation of a correlation table for our variables. The second question asks for a regression equation showing how the different variables impact the compa-ratio measure. The third questions asks you to discuss the benefits of using a regression equation approach over the single variable tests we have been doing.

The forth question asks for what other information you would have liked to have analyzed in our research. The fifth question asks for your answer to the equal pay for equal work question of: Is the company paying fairly or not? If not, who benefits and why?

Regression and Corellation

Remember to show how you got your results in the appropriate cells. For questions using functions, show the input range when asked.

1. Create a correlation table using Compa-ratio and the other interval level variables, except for Salary.

Suggestion, place data in columns T – Y

a What range was placed in the Correlation input range box: Place C9 in output box.

b What are the statistically significant correlations related to Compa-ratio? T = Significant r =

c Are there any surprises – correlations you though would be significant and are not, or non significant correlations you thought would be?

d Why does or does not this information help answer our equal pay question?

2 Perform a regression analysis using compa as the dependent variable and the variables used in Q1 along with including the dummy variables. Show the result, and interpret your findings by answering the following questions. Suggestion: Place the dummy variables values to the right of column Y. What range was placed in the Regression input range box: Note: be sure to include the appropriate hypothesis statements.

Regression hypotheses

Ho:

Ha:

Coefficient hyhpotheses (one to stand for all the separate variables)

Ho:

Ha:

Place B36 in output box.

Interpretation: For the Regression as a whole:

What is the value of the F statistic:

What is the p-value associated with this value:

Is the p-value < 0.05?

What is your decision:

REJ or NOT reject the null?

What does this decision mean?

For each of the coefficients: Midpoint Age Perf. Rat. Service Gender Degree

What is the coefficient’s p-value for each of the variables: Is the p-value < 0.05?

Do you reject or not reject each null hypothesis:

What are the coefficients for the significant variables?

Using the intercept coefficient and only the significant variables, what is the equation?

Compa-ratio =

Is gender a significant factor in compa-ratio?

Regardless of statistical significance, who gets paid more with all other things being equal?

How do we know?

3 What does regression analysis show us about analyzing complex measures?

4 Between the lecture results and your results, what else would you like to know before answering our question on equal pay? Why?

5 Between the lecture results and your results, what is your answer to the question of equal pay for equal work for males and females? Why?

## BUS 308 BUS308 BUS/308 ENTIRE COURSE HELP – ASHFORD UNIVERSITY

**BUS 308 Week 4 Quiz (3 Set)**

## BUS 308 BUS308 BUS/308 ENTIRE COURSE HELP – ASHFORD UNIVERSITY

**BUS 308 Week 5 DQ 1**

**Part One – Confidence Intervals**

Read Lecture Thirteen. Lecture Thirteen introduces you to confidence intervals. What is a confidence interval, and why do some prefer them to single point estimates? Ask your manager what is preferred and why? What are the strengths and weaknesses of using confidence intervals in making decisions? (This should be started on Day 1.)

**Part Two – Chi Square**

Read Lecture Fourteen. As Lecture Fourteen notes, the chi-square test is—in some ways—fundamentally different than the previous tests we have looked at. In what ways and why is this approach important? Examples were shown of gender-degree distributions and employees per grade. How do these tests help with understanding our equal pay for equal work question? Do they change or reinforce our decision from last week? What situations in your personal or professional lives could use a chi-square approach?

**Part Three – Overall Reactions**

Has your opinion about statistics changed? How can statistical analysis help your professional career?

## BUS 308 BUS308 BUS/308 ENTIRE COURSE HELP – ASHFORD UNIVERSITY

**BUS 308 Week 5 DQ 2**

What are common mistakes in linear regression analysis?

## BUS 308 BUS308 BUS/308 ENTIRE COURSE HELP – ASHFORD UNIVERSITY

**BUS 308 Week 5 Final Paper Statistics Reflection (2 Papers)**

**This tutorial contains 2 Different Papers**

## BUS 308 BUS308 BUS/308 ENTIRE COURSE HELP – ASHFORD UNIVERSITY

**BUS 308 Week 5 Quiz (3 Set)**

**BUS 308 Week 5 Quiz**

Question 1. Compared to the ANOVA test, Chi Square procedures are not powerful (able to detect small differences).

Question 2. In confidence intervals, the width of the interval depends only on the variation within the data set.

Question 3. The percent confidence interval is the range having the percent probability of containing the actual population parameter.

Question 4. The Chi Square test can be performed on categorical (nominal) level data.

Question 5. For a one sample confidence interval, the interval is calculated around the estimated population or standard.

Question 6. The chi square test is very sensitive to small differences in frequency distributions.

Question 7. The probability that the actual population mean will be outside of a 98% confidence interval is

Question 8. A confidence interval is generally created when statistical tests fail to reject the null hypothesis – that is, when results are not statistically significant.

Question 9. A contingency table is a multiple row and multiple column table showing counts in each cell.

Question 10. For a one sample confidence interval, if the interval contains the population mean, the corresponding t-test will have a statistically significant result – rejecting the null hypothesis.

**BUS 308 Week 5 Quiz Set 2**

Question 1. A contingency table is a multiple row and multiple column table showing counts in each cell.

Question 2. The Chi Square test for independence needs a known (rather than calculated) expected frequency distribution.

Question 3. For a two-sample confidence interval, the interval shows the difference between the means.

Question 4. Statistical significance in the Chi Square test means the population distribution (expected) is not the source of the sample (observed) data.

Question 5. The chi square test is very sensitive to small differences in frequency distributions.

Question 6. The chi square test measures differences in frequency counts rather than measures differences (such as done in the t and ANOVA tests).

Question 7. The Chi Square test can be performed on categorical (nominal) level data.

Question 8. The degrees of freedom for both forms of the Chi Square test are calculated the same way.

Question 9. In confidence intervals, the width of the interval depends only on the variation within the data set.

Question 10. Compared to the ANOVA test, Chi Square procedures are not powerful (able to detect small differences).

**BUS 308 Week 5 Quiz Set 3**

Question 1. For a one sample confidence interval, if the interval contains the population mean, the corresponding t-test will have a statistically significant result – rejecting the null hypothesis.

Question 2. While rejecting the null hypothesis for the goodness of fit test indicates that distributions differ, rejecting the null for the test of independence means the variables interact.

Question 3. A contingency table is a multiple row and multiple column table showing counts in each cell.

Question 4. For a one sample confidence interval, the interval is calculated around the calculated sample mean.

Question 5. Having expected frequencies of 5 or less in a Chi Square test can increase the likelihood of a type I error – wrongly rejecting the null hypothesis.

Question 6. The degrees of freedom for the goodness of fit test equals

Question 7. For a one sample confidence interval, the interval is calculated around the estimated population or standard.

Question 8. The null hypothesis for the test of independence states that no correlation exists between the variables.

Question 9. The chi square test is very sensitive to small differences in frequency distributions.

Question 10. The chi square test measures differences in frequency counts rather than measures differences (such as done in the t and ANOVA tests).