# BUS 308 Week 5 Quiz (3 Set) NEW

BUS 308 Week 5 Quiz (3 Set) NEW

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).

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

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

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:
1. Must be three to- five double-spaced pages in length, and formatted according to APA style as outlined in the Ashford Writing Center.
2. Must include a title page with the following:
a. Title of paper
b. Student’s name
c. Course name and number
d. Instructor’s name
e. Date submitted
3. Must begin with an introductory paragraph that has a succinct thesis statement.
4. Must address the topic of the paper with critical thought.
5. Must end with a conclusion that reaffirms your thesis.
6. Must use at least three scholarly sources, in addition to the text.
7. Must document all sources in APA style, as outlined in the Ashford Writing Center.
8. Must include a separate reference page, formatted according to APA style as outlined in the Ashford Writing Center.

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# BUS 308 Week 5 DQ 1 NEW

BUS 308 Week 5 DQ 1 NEW

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

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# BUS 308 Week 4 Quiz (3 Set) NEW

BUS 308 Week 4 Quiz (3 Set) NEW

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 4 Problem Set (Regression and Correlation) NEW

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

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?
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 DQ 1 NEW

BUS 308 Week 4 DQ 1 NEW

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.)

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# BUS 308 Week 3 Quiz (3 Set) NEW

BUS 308 Week 3 Quiz (3 Set) NEW

Question 1.  A single factor ANOVA output includes information on
Question 2.  ANOVA tests for variance differences
Question 3.  Excel’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 3 Problem Set (Anova) NEW

BUS 308 Week 3 Problem Set (Anova) NEW

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?

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