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Author Question: A county real estate appraiser wants to develop a statistical model to predict the appraised value ... (Read 64 times)

shenderson6

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on: Feb 14, 2020
A county real estate appraiser wants to develop a statistical model to predict the appraised value of houses in a section of the county called East Meadow. One of the many variables thought to be an important predictor of appraised value is the number of rooms in the house. Consequently, the appraiser decided to fit the simple linear regression model:

E(y) = β0 + β1x,


where y = appraised value of the house (in thousands of dollars) and x = number of rooms.

What set of hypotheses would you test to determine whether appraised value is positively linearly related to number of rooms?
◦ H0: β1 < 0 vs. Ha: β1 > 0
◦ H0: β1 = 0 vs. Ha: β1 ≠ 0
◦ H0: β1 = 0 vs. Ha: β1 > 0
◦ H0: β1 = 0 vs. Ha: β1 < 0
Statistics ¦ McClave, Sincich, Tamarack ¦ 12th Edition
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Marked as best answer by shenderson6 on Feb 14, 2020

cpetit11

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Reply #1 on: Feb 14, 2020
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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shenderson6

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Reply 2 on: Feb 14, 2020
Thanks for the timely response, appreciate it

ecabral0

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Reply 3 on: Yesterday
Great answer, keep it coming :)

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