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Open the "AmesHousing.csv" data set again and (if it is not there already) create a new variable named "totalsf" to represent the total square footage of a house. It will be composed of the variables "First_Flr_SF," "Low_Qual_Fin_SF," "Open_Porch_SF," "Scnd_Flr_SF," and "TotalBsmtSF " added together. As a check, if you do this correctly, the total square footage for the first house should be 2798: Also, create a variable called "SalesPrice000s" to represent the sales price of a home in thousands and a variable called "age" to represent the difference between the year of sale and the year the house was built.
(a) Produce a scatterplot of SalesPrice000s by totalsf. State whether the appearance of the plot makes sense to you and why you feel that way.
(b) Suppose your supervisor wants to predict SalePrice000s using totalsf using least- squares linear regression. Explain, in a way understandable to a typical manager, what the "least-squares method" does to find the estimates b_{0} and b_{1}:
(c) Write down simple linear regression model, using variable names in place of Y and x: Then fit the regression line, using Excel, R, or any other program you want.
Display the output from whatever program you use (a screen shot will probably be easiest), and write down the estimated regression equation.
(d) Interpret the estimated slope and intercept term in the context of the problem.
(e) Using software, calculate a 95% confidence interval for the slope parameter, β1; and interpret the interval in the context of the problem. Show the components of the calculation.
(f) What does the interval imply about the test of H_{0}: β_{1} = 0 vs. H_{1}: β_{1} ≠ 0 at α = 0:05; and why?
(g) Write down the first-order multiple linear regression model to predict Sales000s using "age" and "totalsf". Again use the actual variable names in place of Y and x: Then .t the regression line, using Excel, R, or any other program you want.
Display the output from whatever program you use (a screen shot will probably be easiest), and write down the estimated regression equation.
(h) Interpret the estimated slope coefficients in the context of the problem.
Summary
The question belongs to Statistics and it discusses about producing a scatterplot and interpreting the data given in the Excel sheet.
Total Word Count 522
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