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natalie2426

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A real estate magazine reported the results of a regression analysis designed to predict the price (y), measured in dollars, of residential properties recently sold in a northern Virginia subdivision. One independent variable used to predict sale price is GLA, gross living area (x), measured in square feet. Data for 157 properties were used to fit the model, E(y) = β0 + β1x. The results of the simple linear regression are provided below.
y = 96,600 + 22.5x   s = 6500   R2 = 0.77   t = 6.1 (for testing β1)
Interpret the value of the coefficient of determination, R2.
◦ There is a moderately strong positive correlation between sale price (y) and GLA (x).
◦ 77% of the total variation in the sample sale prices can be attributed to the linear relationship between GLA (x) and (y).
◦ GLA (x) is linearly related to sale price (y) 77% of the time.
◦ 77% of the observed sale prices (y's) will fall within 2 standard deviations of the least squares line.


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Marked as best answer by natalie2426 on Jan 16, 2020

jasonq

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Lorsum iprem. Lorsus sur ipci. Lorsem sur iprem. Lorsum sur ipdi, lorsem sur ipci. Lorsum sur iprium, valum sur ipci et, vala sur ipci. Lorsem sur ipci, lorsa sur iprem. Valus sur ipdi. Lorsus sur iprium nunc, valem sur iprium. Valem sur ipdi. Lorsa sur iprium. Lorsum sur iprium. Valem sur ipdi. Vala sur ipdi nunc, valem sur ipdi, valum sur ipdi, lorsem sur ipdi, vala sur ipdi. Valem sur iprem nunc, lorsa sur iprium. Valum sur ipdi et, lorsus sur ipci. Valem sur iprem. Valem sur ipci. Lorsa sur iprium. Lorsem sur ipci, valus sur iprem. Lorsem sur iprem nunc, valus sur iprium.
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natalie2426

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Reply 2 on: Jan 16, 2020
Wow, this really help


sarah_brady415

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Reply 3 on: Yesterday
Gracias!

 

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