Here is part of the Minitab output for regressing selling price on appraised val
ID: 3054617 • Letter: H
Question
Here is part of the Minitab output for regressing selling price on appraised value, along with prediction for a unit with appraised value $800,000 Predictor Constant Appraisal 1.2699 0.1938 6.55 0.000 Coef SE Coef 86.0 156.8 0.55 0.588 S 235.410 R-Sq 62.3% R-Sq (adj ) -60.8% Predicted Values for New Observations New Obs Fit SE Fit 95% CI 95% PI 1 1101.9 44.7 (1010.0, 1193.9) (609.4, 1594.5) Exercises 24. 16 to 24.24 are based on this information 24.16 The equation of the least-squares regression line for predicting selling price from appraised value is (a) price 86.0+1.2699 X appraised value (b) price = 1.2699 + 86.0 × appraised value (c) price 156.8 0.1938 X appraised value 24.17 What is the correlation between selling price and app- raised value? a) 0.789 (b) 0.623 (c) 0.388 24.18 The slope ? of the population regression line describes (a) the average selling price in a population of units when a unit's appraised value is 0 (b) the average increase in selling price in a population of units when appraised value increases by $1000Explanation / Answer
24.16) A option
price = 86 + 1.2699 * app value
24.17) Corr = sqrt(0.623)
Corr = 0.789
A option
24.18) B option
24.20) C option
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