BUS 308 Entire Course (New)

FOR MORE CLASSES VISIT www.bus308mentor.com BUS 308 Week 2 Problem Set BUS 308 Week 3 Problem Set (Anova) BUS 308 Week 4 Problem Set (Regression and Correlation) BUS 308 Week 5 Final Paper Statistics Reflection (2 Papers) BUS 308 Week 1 DQ 1 BUS 308 Week 1 DQ 2 BUS 308 Week 2 DQ 1 BUS 308 Week 2 DQ 2 BUS 308 Week 3 DQ 1 BUS 308 Week 3 DQ 2 BUS 308 Week 4 DQ 1 BUS 308 Week 4 DQ 2 BUS 308 Week 5 DQ 1 BUS 308 Week 5 DQ 2 BUS 308 Week 1 Quiz (2 Set)

BUS 308 Week 2 Quiz (3 Set) BUS 308 Week 3 Quiz (3 Set) BUS 308 Week 4 Quiz (3 Set) BUS 308 Week 5 Quiz (3 Set) ==============================================

BUS 308 Week 1 DQ 1

FOR MORE CLASSES VISIT www.bus308mentor.com Part Two â€“ 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 Week 1 DQ 2

FOR MORE CLASSES VISIT www.bus308mentor.com 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.

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BUS 308 Week 1 Problem Set

FOR MORE CLASSES VISIT www.bus308mentor.com 1. For assistance with these calculations, see the Recommended Resources for Week One. Measurement issues. Data, even numerically code variables, can be one of 4 levels â€“ nominal, ordinal, interval, or

ratio. It is important to identify which level a variable is, as this impacts the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data. Please list, under each label, the variables in our data set that belong in each group.. 2. The first step in analyzing data sets is to find some summary descriptive statistics for key variables. For salary, compa, age, Performance Rating, and Service; find the mean and standard deviation for 3 groups: overall sample, Females, and Males. You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =stdev functions. Note: Place data to the right, if you use Descriptive statistics, place that to the right as well: 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.

For each group (overall, females, and males) find::

a.

The value that cuts off the top 1/3 salary in each group.

b.

The z score for each value.

c.

The normal curve probability of exceeding this score.

d. What is the empirical probability of being at or exceeding this salary value? e.

The score that cuts off the top 1/3 compa in each group.

f.

The z score for each value.

g.

The normal curve probability of exceeding this score.

h. What is the empirical probability of being at or exceeding this salary value? i. How do you interpret the relationship between the data sets? What do they mean about our equal pay for equal work question? 5.

Equal Pay Conclusions:

a. What conclusions can you make about the issue of male and male pay equality? Are all of the results consistent? b. What is the difference between the salary and compa measures of pay? c.

Conclusions from looking at salary results:

d.

Conclusions from looking at compa results:

e.

Do both salary measures show the same results?

f. yet?

Can we make any conclusions about equal pay for equal work

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BUS 308 Week 1 Quiz (2 Set)

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BUS 308 Week 1 Quiz

Question 1. Calculating the median requires data of at least what level ? Question 2. If sales data are reported in dollar values, what is the scale of the data ? Question 3. Empirical probability is Question 4. A probability is found by dividing the number of possible outcome (0) by the number of successes (e)P = o/e. Question 5. Which of the following measure central tendency and includes data from every score? Question 6. A parameter refers to a sample characteristic. Question 7. The mean is ? Question 8. The probability of two independent events occurring together equals the product of each of the individual event probabilities. Question 9. Days of the week are considered what level of data ? Question 10. Data on the ages of customers are ratio scale data. BUS 308 Week 1 Quiz Set 2 Question 1. The probability of finding 100 defective products in a sample of 500 is 25%. Question 2. In statistical notation, M is to Âľ as s is to Ďƒ. Question 3. Empirical probability is Question 4. The standard deviation measures the central tendency of the data set.

Question 5. The mean is? Question 6. Data on the city from which members of a board of directors come from represent interval level data? Question 7. The mean is the most frequently used measure of central location. Question 8. Distances are considered an example of which data scale? Question 9. The standard deviation of normally distributed data is equal to about 1/6 of the data setâ€™s Question 10. Calculating the median requires data of at least what level?

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BUS 308 Week 2 DQ 1

FOR MORE CLASSES VISIT www.bus308mentor.com 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 Week 2 DQ 2

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www.bus308mentor.com 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 Week 2 Problem Set

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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

Included in the Week Two tab of theEmployee Salary Data Set are 2 one-sample t-tests comparing male and female average salaries to the overall sample mean. 1. Below are 2 one-sample t-test comparing male and female average salaries to the overall sample mean. 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 data set, perform a 2-sample t-test to see if the population male and female average salaries could be equal to each other. (Since we have not yet covered testing for variance equality, assume the data sets have statistically equal variances.) 3. Based on our sample data set, can the male and female compas in the population be equal to each other? (Another 2-sample t-test.) 4. Since performance is often a factor in pay levels, is the average Performance Rating the same for both genders? 5. If the salary and compa mean tests in questions 2 and 3 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 2 Quiz (3 Set)

FOR MORE CLASSES VISIT www.bus308mentor.com BUS 308 Week 2 Quiz Question 1. To find the z-score of a value, which Excel function could be used?

Question 2. Which statement is not true? Question 3. What is the alternate hypothesis in a problem where sales group two is predicted to be ―…significantly less productive than sales group one?‖ Question 4. Using the T-test: Two-sample Assuming Equal Variances test, the output can provide… Question 5. To find the normal curve probability of exceeding a specific z-score, which Excel function could be used? Question 6. When interpreting the effect size, a high effect is shown by a value equal to or greater than‖. Question 7. Using the Data Analysis Descriptive Statistics tool, the output can provide the … Question 8. Using alpha = .05, what is your decision if the p-value is 0.01 for a one-tail test? Question 9. Which of the following defines statistical significance Question 10. If the p-value is greater than (>) our decision criteria, alpha, then we reject the null hypothesis claim of no difference. BUS 308 Week 2 Quiz Set 2 Question 1. Which statement is not true? Question 2. If the p-value is less than (<) our decision criteria, alpha, then we reject the null hypothesis claim of no difference. Question 3. Using alpha = .05, what is your decision if the p-value is 0.01 for a one-tail test? Question 4. To use Excel to compare a single sample mean against a specific value, a Two Sample with unequal variance t-test can be used if

the second ―sample‖ has the same count and consists of only the specific Ho value (resulting in no variation). This gives us a test outcome that is the same as a one sample t-test result. Question 5. What is the alternate hypothesis in a problem where sales group two is predicted to be ―…significantly less productive than sales group one?‖ Question 6. To find the normal curve probability of exceeding a specific z-score, which Excel function could be used? Question 7. Which statement is correctly stated? Question 8. Using alpha = .05, what is your decision if the p-value is 0.01 for a two-tail test? Question 9. To find the z-score of a value, which Excel function could be used? Question 10. Using alpha = .05, what is your decision if the p-value is 0.04 for a two-tail test? BUS 308 Week 2 Quiz Set 3 Question 1. To use Excel to compare a single sample mean against a specific value, a Two Sample with unequal variance t-test can be used if the second ―sample‖ has the same count and consists of only the specific Ho value (resulting in no variation). This gives us a test outcome that is the same as a one sample t-test result. Question 2. The sign of =/= means ―not equal‖. Question 3. We can use the F-test for variance‖ to decide if we should use the equal or unequal variance version of the Two Sample T-test. Question 4. The arrow head in the null hypothesis shows which tail the result needs to be in to reject the null.

Question 5. If the p-value is less than (<) our decision criteria, alpha, then we reject the null hypothesis claim of no difference. Question 6. Using the Data Analysis Descriptive Statistics tool, the output can provide the … Question 7. Which of the following defines statistical significance Question 8. In a one-tail test, which of the following statements is true? Question 9. When interpreting the effect size, a high effect is shown by a value equal to or greater than‖. Question 10. To find the z-score of a value, which Excel function could be used? ==============================================

BUS 308 Week 3 DQ 1

FOR MORE CLASSES VISIT www.bus308mentor.com 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 Week 3 DQ 2

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www.bus308mentor.com 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 Week 3 Problem Set (Anova)

FOR MORE CLASSES VISIT www.bus308mentor.com 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 â€“ 29K. 30 â€“ 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 Week 3 Quiz (3 Set)

FOR MORE CLASSES VISIT www.bus308mentor.com BUS 308 Week 3 Quiz Question 1. A single factor ANOVA output includes information on Question 2. ANOVA tests for variance differences Question 3. Excelâ€™s single factor ANOVA does not have a related Effect Size measure associated with it. Question 4. The effect size measure for the single factor ANVOA is called eta squared and equals the SS Between/SS Total. Question 5. The Two Factor ANOVA with Replication primarily tests for interactions between the variables.

Question 6. The null hypothesis for the Single Factor ANOVA states that all means are equal. Question 7. ANOVA’s SS within is an estimate of the average variance of the data samples. Question 8. The mean difference calculation involves using Question 9. The single factor ANOVA tests for mean differences between 3 or more groups by comparing Question 10. ANOVA’s SS within is an estimate of the overall variance in the data set.

BUS 308 Week 3 Quiz Set 2 Question 1. Question 1.1. ANOVA’s SS within is an estimate of the overall variance in the data set. Question 2. The null hypothesis for the Single Factor ANOVA states that all means are equal. Question 3. A single factor ANOVA output includes information on Question 4. The alternate hypothesis for the single factor ANOVA states that all means differ. Question 5. A significance of F value equaling 3.5E-03 means Question 6. The single factor ANOVA tests for mean differences between 3 or more groups by comparing Question 7. Excel’s single factor ANOVA output includes the effect size measure.

Question 8. In calculating which means differ, each pair of means needs a unique range. Question 9. Setting up data entry for the single factor ANOVA in Excel involves Question 10. What is the best reason to perform an ANOVA test rather than multiple t-tests? BUS 308 Week 3 Quiz Set 3 Question 1. The single factor ANOVA mean difference calculation involves Question 2. Excel’s ANOVA output Question 3. A significance of F value equaling 3.5E-03 means Question 4. Excel’s options for performing an ANOVA include Question 5. The Two Factor ANOVA with Replication primarily tests for interactions between the variables. Question 6. Excel’s single factor ANOVA does not have a related Effect Size measure associated with it. Question 7. ANOAV uses which statistical distribution to determine the significance of the results? Question 8. What is the best reason to perform an ANOVA test rather than multiple t-tests? Question 9. The alternate hypothesis for the single factor ANOVA states that all means differ. Question 10. The Two Factor ANOVA with Replication primarily tests for mean

differences.

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BUS 308 Week 4 DQ 1

FOR MORE CLASSES VISIT www.bus308mentor.com Part One – Correlation Read Lecture Ten. Lecture Ten introduces the idea that different variables may move together—sometimes due to causation and at other times due to an unknown influence. An example involves the perfect (+1.0) correlation between annual number of rum barrels imported into the New England region of the U.S. between the years 1790 and 1820 and the number of churches built each of those years (citation lost). Discuss this correlation: What does it tell us? Does rum drinking cause church building? Does church building cause rum drinking? Or what else could it tell us? If this correlation shows a cause and effect relationship, what drives what? If not, why does it exist? What could this correlation be used for? (This should be started on Day 1.) Part Two – Linear Regression Read Lecture Eleven. Lecture Eleven provides information showing a strong positive correlation and a significant linear regression existed

between the individual’s salary and midpoint (used as a substitute for grade). This is not an unexpected outcome in a company. How useful are these in understanding what drives salary differences? Why? What examples of a linear regression might be useful in your personal or professional lives? Why? (This should be started on Day 3.) Part Three – Multiple Regression Read Lecture Twelve. In Lecture Twelve, a multiple-regression equation was developed that showed the factors that influenced a person’s salary and—almost as important—factors that did not influence salary. How do we interpret a multiple-regression equation? Pick one of the factors— whether statistically significant or not—used in the analysis, and describe its impact on salary, what the coefficient is and what it means, what its significance is, and whether you expected this outcome or not. (This should be completed by Day 5.) ==============================================

BUS 308 Week 4 DQ 2

FOR MORE CLASSES VISIT www.bus308mentor.com 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.

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BUS 308 Week 4 Problem Set (Regression and Correlation)

FOR MORE CLASSES VISIT www.bus308mentor.com 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 Comparatio? 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?

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BUS 308 Week 4 Quiz (3 Set)

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Question 1. The t Stat value is used to determine the statistical significance of each of the variables listed in a regression analysis. Question 2. A correlation of .90 and above is generally considered too strong to be of any practical significance. Question 3. A p-value of 9.22E-36 equals 0.00000000000000000000000000000000000922 and is less than .05

Question 4. If two variables are known to be correlated, it is possible to predict the value of y (dependent variable) from an x (independent) variable. Question 5. When determining statistical significance of correlations, (as a rule of thumb), variable pairs with coefficients greater than (>) 70% are generally not very valuable for prediction purposes. Question 6. Which statement does not belong? Question 7. Pearson Correlation Coefficient is a mathematical value that shows the strength of the linear (straight line) relationship between two variables. Question 8. A regression analysis uses two distinct types of data. The first are variables that are at least nominal level. Question 9. The ANOVA table provides the Significance of F to use to see if we reject or fail to reject the null hypothesis of no significance. The Significance of F is also known as the P-value. Question 10. When performing a regression analysis using the Regression option in Data Analysis, the input for the Y range is the independent variable (can generally control) and the input X range is for the dependent variables. BUS 308 Week 4 Quiz Set 2

Question 1. When determining statistical significance of correlations, (as a rule of thumb), variable pairs with coefficients greater than (>) 70% are generally not very valuable for prediction purposes. Question 2. A p-value of 9.22E-36 equals 0.00000000000000000000000000000000000922 and is less than .05

Question 3. Pearson Correlation Coefficient is a mathematical value that shows the strength of the linear (straight line) relationship between two variables. Question 4. A Pearson correlation of +1.00 is considered a ―perfect positive correlation‖. This means…. Question 5. Spearman’s rank order correlation (rho) can be performed on ordinal or any ranked data. Question 6. The t Stat value is used to determine the statistical significance of each of the variables listed in a regression analysis. Question 7. Pearson’s Correlation requires at least interval level data. Question 8. If two variables are known to be correlated, it is possible to predict the value of y (dependent variable) from an x (independent) variable. Question 9. A correlation of .90 and above is generally considered too strong to be of any practical significance. Question 10. When looking at a regression statistics table, Multiple R displays the percent of variation in common between the dependent and all of the independent variables.

BUS 308 Week 4 Quiz Set 3 Question 1. Pearson’s Correlation requires at least interval level data. Question 2. A p-value of 9.22E-36 equals 0.00000000000000000000000000000000000922 and is less than .05 Question 3. When plotting variables on a scatter diagram, the variables plotted on the Y-axis is the horizontal axis and the X-axis is the vertical axis.

Question 4. If two variables are known to be correlated, it is possible to predict the value of y (dependent variable) from an x (independent) variable. Question 5. When determining statistical significance of correlations, (as a rule of thumb), variable pairs with coefficients greater than (>) 70% are generally not very valuable for prediction purposes. Question 6. A correlation of .90 and above is generally considered too strong to be of any practical significance. Question 7. A Pearson correlation of +1.00 is considered a ―perfect positive correlation‖. This means…. Question 8. When looking at a regression statistics table, Multiple R displays the percent of variation in common between the dependent and all of the independent variables. Question 9. Which statement does not belong? Question 10. The t Stat value is used to determine the statistical significance of each of the variables listed in a regression analysis.

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BUS 308 Week 5 DQ 1

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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 Week 5 DQ 2

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www.bus308mentor.com What are common mistakes in linear regression analysis? ==============================================

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

FOR MORE CLASSES VISIT www.bus308mentor.com This tutorial contains 2 Different Papers

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: Title of paper Studentâ€™s name Course name and number Instructorâ€™s name Date submitted Must begin with an introductory paragraph that has a succinct thesis statement. Must address the topic of the paper with critical thought. Must end with a conclusion that reaffirms your thesis. Must use at least three scholarly sources, in addition to the text. Must 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 Week 5 Problem Set

FOR MORE CLASSES VISIT www.bus308mentor.com ASSIGNMENT WEEK 5

1. Create a correlation table for the variables in our (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? 2. 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 employeeâ€™s salary, we do not want to have both used in the same regression. Please interpret the findings. 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? 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? 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? ==============================================

BUS 308 Week 5 Quiz (3 Set)

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www.bus308mentor.com 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 â€“ 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 â€“ 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 â€“ 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 â€“ 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). ==============================================

FOR MORE CLASSES VISIT www.bus308mentor.com BUS 308 Week 2 Problem Set BUS 308 Week 3 Problem Set (Anova) BUS 308 Week 4 Problem Set (Regre...

Published on Aug 14, 2019

FOR MORE CLASSES VISIT www.bus308mentor.com BUS 308 Week 2 Problem Set BUS 308 Week 3 Problem Set (Anova) BUS 308 Week 4 Problem Set (Regre...

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