Multiple Linear regression paying special attention to the Analytics in Practice on page 253.
Complete exercises 3, 4, 15, 16, 18, and the Performance Lawn Equipment Case on page 272. post your work

3. Using the data in the Excel file Demographics, determine
   if a linear relationship exists between unemployment
   rates and the cost of living indexes by
   constructing a scatter chart. Visually, do there appear
   to be any outliers? If so, delete them and then find the best-fitting linear regression line using the Excel
   Trendline tool. What would you conclude about the
   strength of any relationship? Would you use regression
   to make predictions of the unemployment rate
   based on the cost of living?
   Exercises
4. Using the data in the Excel file Weddings construct
   scatter charts to determine whether any linear relationship
   appears to exist between (1) the wedding
   cost and attendance, (2) the wedding cost and the
   value rating, and (3) the couple’s income and wedding
   cost only for the weddings paid for by the bride
   and groom. Then find the best-fitting linear regression
   lines using the Excel Trendline tool for each of
   these charts.
   Exercises
5. The Excel file Concert Sales provides data on sales
   dollars and the number of radio, TV, and newspaper
   ads promoting the concerts for a group of cities. Develop
   simple linear regression models for predicting
   sales as a function of the number of each type of ad.
   Compare these results to a multiple linear regression
   model using both independent variables. Examine
   the residuals of the best model for regression assumptions
   and possible outliers.
   Exercises
6. Using the data in the Excel file Home Market Value,
   develop a multiple linear regression model for estimating
   the market value as a function of both the age
   and the size of the house. Predict the value of a house
   that is 30 years old and has 1,800 square feet, and
   one that is 5 years old and has 2,800 square feet.
   Exercises
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7. The Excel file Salary Data provides information on
   current salary, beginning salary, previous experience
   (in months) when hired, and total years of education
   for a sample of 100 employees in a firm.
   a. Develop a multiple regression model for predicting
   current salary as a function of the other
   variables.
   b. Find the best model for predicting the current salary
   using the t-value criterion.
   Problem
   Performance Lawn Equipment Case, (Page 272).
   In reviewing the PLE data, Elizabeth Burke noticed that
   defects received from suppliers have decreased (
   worksheet
   Defects
   After Delivery). Upon investigation, she learned
   that in 2010, PLE experienced some quality problems
   due to an increasing number of defects in materials
   received from suppliers. The company instituted an initiative
   in August
   2011 to work with suppliers to reduce
   these defects, to more closely coordinate deliveries, and to
   improve
   materials quality through reengineering supplier
   production policies. Elizabeth noted that the program appeared
   to reverse an increasing trend in defects; she would
   like to predict what might have happened had the supplier
   initiative not been implemented and how the number of
   defects might further be reduced in the near future.
   In meeting with PLE’s human resources director,
   Elizabeth also discovered a concern about the high rate
   of turnover in its field service staff. Senior managers have
   suggested that the department look closer at its recruiting
   policies, particularly to try to identify the characteristics
   of individuals that lead to greater retention. However, in
   a recent staff meeting, HR managers could not agree on
   these characteristics. Some argued that years of education
   and grade point averages were good predictors. Others
   argued that hiring more mature applicants would lead to
   greater retention. To study these factors, the staff agreed
   to conduct a statistical study to determine the effect that
   years of education, college grade point average, and age
   when hired have on retention. A sample of 40 field service
   engineers hired 10 years ago was selected to determine
   the influence of these variables on how long each individual
   stayed with the company. Data are compiled in the
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   Employee Retention worksheet.
   Finally, as part of its efforts to remain competitive,
   PLE tries to keep up with the latest in production technology.
   This is especially important in the highly competitive
   lawn-mower line, where competitors can gain a real
   advantage if they develop more cost-effective means of
   production. The lawn-mower division therefore spends a
   great deal of effort in testing new technology. When new
   production technology is introduced, firms often experience
   learning, resulting in a gradual decrease in the time
   required to produce successive units. Generally, the rate of
   improvement declines until the production time levels off.
   One example is the production of a new design for lawnmower
   engines. To determine the time required to produce
   these engines, PLE produced 50 units on its production
   line; test results are given on the worksheet Engines in
   the database. Because PLE is continually developing new
   technology, understanding the rate of learning can be useful
   in estimating future production costs without having to
   run extensive prototype trials, and Elizabeth would like a
   better handle on this.
   Use techniques of regression analysis to assist her in
   evaluating the data in these three worksheets and reaching
   useful conclusions. Summarize your work in a formal
   report
   with all appropriate results and analyses.
   Note: Complete and post for review and grading the following “Problems and Exercises” starting on page 268,
   270, and 272 (all work in Excel).