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**Instructions**: Complete each problem on a separate worksheet in a single Excel file. Rename the separate worksheets with the respective problem number. You may have to copy and paste the datasets into your homework file first. Name the file with your last name, first initial, and HW #10. Label each part of the question. When calculating statistics, label your outputs. **Submit your completed file in Blackboard**. If not significance level is specified, assume 5%.

1. The file **310homework10data.xlsx**, problem #1 data contains midterm and final exam data for 96 students in a corporate finance course. Each row contains the two exam scores for a given student, so you might expect them to be positively correlated.

a. Create a scatterplot of the final exam score (y) versus the midterm score (x). Based on the visual evidence, would you say that the scores for the two exams are strongly related? Is the relationship a linear one?

b. Superimpose a trendline on the scatterplot and use the option to display the equation and the value. What does this equation indicate in terms of predicting a student’s final exam score from his/her midterm score? Be specific.

2. DataPro is a small, but rapidly growing firm that provides electronic data processing services to commercial firms, hospitals, and other organization. For each of the past several months, Data Pro has tracked the number of contracts sold, the average contract price, advertising expenditures, and personal selling expenditures. This data is in the file **310homework10data.xlsx** in Problem #2. Using the number of contracts sold as the dependent variable, estimate a multiple regression equation with three explanatory variables. Interpret each of the estimated regression coefficients, the standard error estimate, and .

3. A regional express delivery service company recently conducted a study to investigate the relationship between the cost of shipping a package (y), the package weight (), and the distance shipped (). Several packages were randomly selected from among the large number received for shipment, and a detailed analysis of the shipping cost was conducted for each package. These are provided in the file **310homework10data.xlsx** in Problem #3.

a. Estimate a simple linear regression equation involving shipping cost and package weight. Interpret the slope coefficient of the least squares line and the value.

b. Add another explanatory variable, distance shipped, to the regression model in a. Estimate and interpret the expanded model. How does the value for this multiple regression model compare to that of the simple regression equation in a? Explain any difference between the two values. Interpret the adjusted value for the revised equation.

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