# BUS 308 Week 5 DQ 2 Correlation and Confidence Intervals

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A car dealer is using the number of years customers have owned their vehicles to predict how long it will be before they elect to replace them. The correlation between the two is (the longer they have owned their present vehicles, the more quickly they are expected to replace them). The other relevant data are as follows for 32 customers:

Based on the information above, answer the following questions:

a. How long is the time to expected replacement for a customer who has owned a vehicle 6.5 years?

b. Calculate .95 and .99 confidence intervals and explain your results.

c. How will a larger standard deviation in the criterion variable affect the width of the confidence intervals? Why?

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# BUS 308 Week 5 DQ 1 Confidence Intervals

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A hardware retailer has average sales of \$64,235 with a standard deviation of \$5,918 for a 12-month period. The mean monthly sales for all retailers in the chain are \$59,844. Is this hardware retailer’s sales significantly different from all retailers in the chain at ? Are they significantly different at ? Calculate a .95 confidence interval for the data in problem.

Guided Response: Review several of your classmates’ postings. Respond to at least two classmates by commenting on whether or not you think changing the confidence intervals will result in a different outcome. Explain if you agree or not with the role of a confidence interval in the interpretation of the answer

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# BUS 308 Week 4 Quiz

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1.   With reference to problem 1, what statistic determines the correlation of experience with productivity, controlling for age in experience?

2.  In a problem where interest rates and growth of the economy are used to predict consumer spending, which of the following will increase prediction error?

3.  With reference to problem 3, how is the regression constant or the a value interpreted?

4.  Which of the following is a problem in simple regression?

5.  In a problem where average temperature and number of daylight hours are used to predict energy consumption in homes, what does the standard error of multiple estimate gauge?

6.  What does “shrinkage” mean in reference to regression solutions?

7.  The degree to which years of education and years of experience together correlate with annual salary is indicated in multiple correlation.

8.  The criterion variable in regression is the variable used to predict the value of y.

9.  Which of the following are consistent with the requirements of simple regression?

10. Larger sample diminish the standard error of the estimate.

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# BUS 308 Week 4 Problem Set

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Problem Set Week Four.

Problem One

The manager of a catering company is using the number of people in the party to predict the cost of the drinks that are required for the event. The following are the data for 12 recently catered events:

Complete the calculations below using this data. Show all of your work and clearly label each of your calculations.

a. Provide a scatterplot

b. Calculate a linear regression

c. Calculate the residuals

d. Calculate the correlation between the two variables

e. Calculate the mean, median, and standard deviation of the number of people and cost of drinks

For additional assistance with these calculations reference the Recommended Materials for Week Four.

Problem Two

You are a real estate agent and you are trying to predict home prices for your clients that want to list their house for sale. You have a very small city without much data. You will need to use the data that you have available for the past year on homes that have been sold.

Complete the calculations below using this data. Show all of your work and clearly label each of your calculations.

Conduct a multiple regression analysis to predict home prices. In your analysis complete the following:

a. Calculate the multiple regression analysis and report your data.

b. Determine the list price for your client’s home if it has three bedrooms, three bathrooms, and 1900 square footage. Provide your analysis and show all of your calculations.

For additional assistance with these calculations reference the Recommended Materials for Week Four.

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# BUS 308 Week 4 DQ 2 Multiple Regressions Analysis

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Develop a multiple linear regression equation that describes the relationship between tenure and the other variables in the chart above. Use the Analysis Toolpak located in Excel to perform this multiple regression.

Do these two variables explain a reasonable amount of the variation in the dependent variable?

Estimate the tenure of someone that could have \$5.8(\$k) and 15 years of job satisfaction. Make sure to state your multiple regression equation in your example. What are some of things that you can estimate from the model? How effective is evaluating the R-squared of the model? What is the relationship between the independent and dependent variables?

Guided Response: Review several of your classmates’ postings. Respond to at least two classmates by commenting on how this information might be used to make business decisions.

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# BUS 308 Week 4 DQ 1 Simple Regression Analysis

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Use the data in the chart to answer the questions below. The data indicates the number of “sick days” appliance installers take during a three month period, and the number of complaints filed by customers during the same interval. Use the Analysis Toolpak in Excel to perform this simple regression and answer the questions.

a. Is the correlation between number of sick days and number of customer complaints statistically significant?

b. What is the best prediction for the number of complaints that will be registered for an installer who takes five sick days during the period?

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# BUS 308 Week 3 DQ 2 Correlation

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An employment agency gathers the following data on its clients:

a) Age

b) Gender

c) Educational level (no high school, high school, associate’s, bachelor’s, master’s)

d) Years of past experience

e) Whether or not they have been successfully placed in employment by the agency

Additionally, the following data is gathered for those who have been successfully placed:

a) Starting salary

b) Current salary

c) Tenure in months

Based on the information above answer the following questions:

a. Which type of correlation procedure would be most appropriate to gauge the relationship between each pair of variables?

b. Do you expect each pair of variables to be significantly correlated or not? Why?

c. For the variables you expect to be significantly correlated, what do you expect the direction of the relationship to be? Why?

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# BUS 308 Week 3 DQ 1 Interval Data

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A courier service in a large city tracks the number of deliveries they are asked to make by 10 clients both before and after offering a progressive discount for repeat business. Their goal is to assess the effects of the discount.

a. What is the most appropriate statistical test in this situation? Why?

b. Are there significant differences in the number of deliveries?

c. If the goal is to promote repeat business, should the discount be continued?

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# BUS 308 Week 2 Quiz

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1.  How is the sum of squares unlike either the standard deviation or the variance?

2.  If sums of squares statistics are calculated for shoppers at three different retail outlets, what statistic will indicate the variability among those at each outlet?

3.  Which is the symbol used for the test statistic in ANOVA?

4.  If ANOVA reveals that four different departments have significantly different levels of productivity, what will a post-hoc test indicate?

5.  The independent t-test is based on which distribution?

6.  What does omega-squared indicate?

7.  Each different t-distribution is defined by which of the following?

8.  When a significant interaction is graphed, what is indicated on the vertical axis?

9.  Four different groups of employees are randomly selected from a common population for a study of differences in the impact of a wage increase.  Why will there be differences even before the incentive is applied?

10. What is the probability of type II error when the null hypothesis is rejected?

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# BUS 308 Week 2 Problem Set

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Problem One

Suppose that an automotive parts and accessories chain is experimenting with a new sales promotion. Two similar stores are selected for the experiment. For Store 1, nothing changes. This store constitutes the control group. For Store 2, the treatment group, the promotion is implemented.
Sales in hundreds of dollars over a five-day period are as follows:

Control: 6, 6, 7, 10, 12, 9, 6, 5, 5, 7
Treatment: 2, 5, 2, 4, 7, 1, 2, 3, 4, 5

The expectation that sales will be higher in the treatment group makes this a one-tailed test; the alternate hypothesis is m1 < m2. Use Excel to determine whether differences between the two groups are statistically significant.

Problem Two

Suppose that a home builder is approached by a customer who wants to move in as soon as possible. The customer chooses three home designs that she likes and asks the home builder which one could be completed the fastest. To compare the three designs on speed of completion, the builder randomly selects 10 homes that he built in the past based on each of the three designs.

Use the Excel Analysis ToolPak to run an ANOVA test in order to determine which design would be best for the customer. Show all of your work and clearly label each of your calculations. Make sure to also clearly describe the respective data and your conclusions.
The data for the number of days to build each home are as follows:

Design A: 15, 17, 19, 21, 23, 25, 27, 29, 31, 33
Design B: 29, 34, 39, 44, 49, 54, 59, 64, 69, 74
Design C: 22, 24, 25, 27, 28, 28, 29, 31, 33, 34
For more information on conducting an ANOVA test in Excel reference the Week Two Recommended Resources.

Problem Three

An insurance company is reviewing its current policy rates. When originally setting the rates they believed that the average claim amount was \$1,800. Now there are concerns that if the true mean is actually higher than this they could potentially lose a lot of money. They randomly select 40 claims, and calculate a sample mean of \$1,950. Assuming that the standard deviation of claims is \$500, and set significance , test to see if the insurance company should be concerned.

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