The data in the accompanying table include the appraised value, land area of the property in acres, and age, in years, for a small sample of 30 single-family homes in a small city. Perform a multiple regression analysis to predict appraised value based on land area of the property (X4) and age, in years, (X2) and determine the VIF for each independent variable in the model. Is there reason to suspect the existence of collinearity? E Click the icon to view the data table. Determine the VIF for each independent variable in the model. VIF, =O VIF2 =D (Round to three decimal places as needed.) Appraised Land Area (аcres) 0.2974 0.1567 0.6107 0.3828 0.2298 0.1548 0.5018 0.3649 0.1172 0.4083 0.2885 0.5581 0.0984 Age Value 667.5 43 555.8 314.2 36 19 320.1 91 321.2 677.2 706.6 21 25 50 344.7 95 339.2 622.1 409.7 38 17 98 688.9 63 326.8 6 757.4 0.0819 71 0.4382 0.6719 0.3956 0.4178 0.2879 0.4865 0.4497 0.3662 0.1017 0.3915 0.2421 0.2365 0.4534 0.5747 0.6001 0.4602 345.9 69 486.4 768.5 624.2 493.7 384.6 286.5 765.8 786.2 308.6 695.6 518.6 796.9 611.8 762.9 306.1 23

The data in the accompanying table include the appraised value, land area of the property in acres, and age, in years, for a small sample of 30 single-family homes in a small city. Perform a multiple regression analysis to predict appraised value based on land area of the property (X4) and age, in years, (X2) and determine the VIF for each independent variable in the model. Is there reason to suspect the existence of collinearity? E Click the icon to view the data table. Determine the VIF for each independent variable in the model. VIF, =O VIF2 =D (Round to three decimal places as needed.) Appraised Land Area (аcres) 0.2974 0.1567 0.6107 0.3828 0.2298 0.1548 0.5018 0.3649 0.1172 0.4083 0.2885 0.5581 0.0984 Age Value 667.5 43 555.8 314.2 36 19 320.1 91 321.2 677.2 706.6 21 25 50 344.7 95 339.2 622.1 409.7 38 17 98 688.9 63 326.8 6 757.4 0.0819 71 0.4382 0.6719 0.3956 0.4178 0.2879 0.4865 0.4497 0.3662 0.1017 0.3915 0.2421 0.2365 0.4534 0.5747 0.6001 0.4602 345.9 69 486.4 768.5 624.2 493.7 384.6 286.5 765.8 786.2 308.6 695.6 518.6 796.9 611.8 762.9 306.1 23

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Concept explainers

Correlation

Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.

Linear Correlation

A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.

Regression Analysis

Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.

Question

796.9
The data in the accompanying table include the appraised value, land area of the property in acres, and age, in years, for a small sample of 30 single-family homes in a
small city. Perform a multiple regression analysis to predict appraised value based on land area of the property (X,) and age, in years, (X2) and determine the VIF
for each independent variable in the model. Is there reason to suspect the existence of collinearity?
E Click the icon to view the data table.
Determine the VIF for each independent variable in the model.
VIF, =D VIF, = I
(Round to three decimal places as needed.)
Appraised Land Area
(acres)
0.2974
0.1567
0.6107
0.3828
0.2298
0.1548
Age
Value
667,5
43
555.8
314.2
36
19
320.1
91
321.2
677.2
706.6
0.5018
344.7
0.3649
0.1172
0.4083
0.2885
0.5581
339.2
622.1
409.
.7
98
688.9
63
326
.8
0.0984
6
757.4
0.0819
71
345.9
0.4382
69
486.4
0.6719
21
768.5
0.3956
98
624.2
0.4178
98
493.7
0.2879
77
85
99
384.6
0.4865
286.5
0.4497
765.8
0.3662
92
786.2
0.1017
0.3915
0.2421
0.2365
308.6
37
695.6
98
518.6
40
0.4534
45
611.8
0.5747
31
762.9
0.6001
84
306.1
0.4602
23

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