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Statistics HW 9

Statistics HW 9 - Question 1 0.5 out of 0.5 points In...

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Question 1 0.5 out of 0.5 points In Florida, real estate agents refer to homes having a swimming pool as pool homes . In this case, Sunshine Pools Inc. markets and installs pools throughout the state of Florida. The company wishes to predict house prices, based on the house size, the number of bathrooms, how “nice” the house is, and if the house comes with a swimming pool. The first set of questions will use the PoolHome data, of the Florida Pool Home case, from the Bowerman Data folder in the BB classroom. The table below shows the data contained in the Minitab worksheet. As shown in the table, the variables are the selling price (in thousands \$), the total square footage of the home, the number of bathrooms, and the niceness rating, expressed as an interval between 1 and 7, where one is the low rating. The 80 randomly selected homes sold within a Florida city within a six month period, and were between 2,000 and 3,500 square feet. In addition, the Pool indicator variable was created to identify those homes with and without a swimming pool. A value of 1 was given to the homes with a pool and 0 was given to those without a pool.

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Remember from the lecture, when running multiple regression analysis: • Test assumptions • Run fitted line plots on each variable, testing for linearity • Run multiple regression, run probability plot on residuals • Test for collinearity • Determine best multiple regression model • Test fit When running the fitted line plots for each variable (except the indicator variables), select Cubic as the “Type of Regression Model”. This will force the program to test all three (linear, quadratic, cubic), where the null hypothesis is that the variables are not either linear, quadratic, or cubic, respectively. After running the fitted line plots for Size, Number of Bathrooms, and Niceness, all

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Pattern Match 0.944 Question 4 0 out of 0.5 points Before running the general regression, look at the variables and decide if there is a possibility of interacting predictor variables. Based on the types of predictor variables, Selected Answer: there is a possible interaction with number of bathrooms and pools.
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