A simple random sample of beachfront condo listings in Myrtle Beach comparing weekly rent (S) and size (square feet) yileds the following computer output. 1800 150 1500 75- 1200- 0- 900- -75 600 1000 1250 1500 1750 2000 900 1200 1S00 2000 Predicted Rent Size Variable Conatant Size . - 102.4 R-squ - 94.78 R-equ(adj) = 94.0% Coef -311.341 1.07707 s.e. Coef 117.6 0.09047 -2.65 11.9 0.0294 0.0001 Is a linear model appropriate for these data? Explain. b) What is the equation for the least squares regression line? Define the varaibles within your equation. c) Interpret the slope of the regression line. Interpret r in context.

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section4.6: Regression And Median-fit Lines
Problem 2CYU
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3.
A simple random sample of beachfront condo listings in Myrtle Beach comparing weekly
rent ($) and size (square feet) yileds the following computer output.
Lock
150
1800
75-
1500
1200
-75
900-
900 1200 15o0 2000
..
600
1000 1250 1500 1750 2000
Size
Predicted Rent
to
Coef
-311.341
07ר1.07
s.e. Coef
117.6
0.09047
-2.65
11.9
= 94.0%
0.0294
0.0001
Variable
Conatant
Size
= 94.78 R-squ (adj)
102.4 R-aqu
%3D
Is a linear model appropriate for these data? Explain.
b) What is the equation for the least squares regression line? Define the varaibles within
your equation.
c) Interpret the slope of the regression line.
c) Interpret r² in context.
Ultra S
SS
CLL
%24
2.
Transcribed Image Text:ReWritable DVD DVD+ReWritable MULTI RECORDER Esc F1 F2 F3 F4 F5 F6 Tab 3. A simple random sample of beachfront condo listings in Myrtle Beach comparing weekly rent ($) and size (square feet) yileds the following computer output. Lock 150 1800 75- 1500 1200 -75 900- 900 1200 15o0 2000 .. 600 1000 1250 1500 1750 2000 Size Predicted Rent to Coef -311.341 07ר1.07 s.e. Coef 117.6 0.09047 -2.65 11.9 = 94.0% 0.0294 0.0001 Variable Conatant Size = 94.78 R-squ (adj) 102.4 R-aqu %3D Is a linear model appropriate for these data? Explain. b) What is the equation for the least squares regression line? Define the varaibles within your equation. c) Interpret the slope of the regression line. c) Interpret r² in context. Ultra S SS CLL %24 2.
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