JMP output appears below for simple linear regression with data from the price, y (in $1000), of n = 28 Seattle home prices. The explanatory variable is the total number of square feet in the home. - Response Price ($000) Regression Plot 600 500 400 300 200 - Distributions 1000 1500 2000 2500 3000 3500 Square Feet Square Feet Summary of Fit RSquare RSquare Adi Root Mean Square Error Mean of Response Observations (or Sum Wgts) 0.560503 0.543599 356.8214 28 Analysis of Variance Sum of 1000 1500 2000 2500 3000 3500 Source DF F Ratio Squares Mean Square 1 249200.64 Model 249201 33.1585 - Summary Statistics Error 26 Prob > F C. Total 27 <.0001 Mean 1923.1071 Std Dev 653.11574 v Parameter Estimates Std Er Mean 123.42727 Term Estimate Std Error t Ratio Prob>lt Upper 95% Mean 2176.359 Lower 95% Mean 1669.8553 Intercept Square Feet 0.1470966 0.025545 73.938964 51.78554 1.43 0.1653 5.76 <.0001 28 (ooos) eoad

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