Suppose multiple linear regression model is fitted with seven independent variable and revealed the following results.Variables Entered/RemovedaMethodEnterModelVariables EnteredVariables RemovedX7, X2, X4, X1, х6, хз, х5ba. Dependent Variable: Yb. All requested variables enteredModel SummaryStd. Error of theAdjusted RSquare.500ModelRR SquareEstimate10.99021754a568a. Predictors: (Constant), X7, X2, X4, X1, X6, X3, X5ANOVAaModelSum of Squares6998.009DfMean Square999.716FSig.1Regression8.277.000b7Residual5314.50344120.784Total12312.51251a. Dependent Variable: Yb. Predictors: (Constant), X7, X2, X4, X1, X8, X3, X5CoefficientsaUnstandardizedStandardizedCollinearity StatisticsVIFCoefficientsCoefficients2 www.youtube.com · 10mModelBStd. ErrorBetatSig.Tolerance1 (Constant)60.5549.4956.377.000PSL Transfer News | Kaizer Chiefs NewX1.001.001.1641.472.148.7881.269Recommended: Madi Media FootballX2.087.0482001.809.077.8011.249000000n401.000P Type here to search VIT1 (Constant)60.5549.4956.377.000X1.001.001.1641.472.148.7881.269X2.087.0482001.809.077.8011.249X3.009.009.133.948.349.5001.999X4-.043.017-.290-2.466.018.7101.408X5.047.012.5593.905.000.4782.090X6209.130.1881.607.115.7191.391X7005.006.1168753875551.800a. Dependent Variable: Ya) Is it necessary to use the t-test to test for the significance of each coefficient? Justify your answer.b) What can you say about the correlation coefficient value in the summary model table?Question (c) and (d) are based on backward elimination method application:c) Which variable would be removed first? Justify your answer.d) After your findings in part (c), is it possible to remove another variable from the same results? Justify your answer.e)i) In the output, what does the statistics 'VIF' and Tolerance' assess?ii) How do they differ from each other?iii) Explain whether you are concerned about these values?f) If a simple regression analysis is performed with X3 as the only independent variable, the Sum of Squares Error (SSE) e www.above multiple regression model with seven independent variables. Test if this simple regression model is signifi PSL TransRecommeFor the toolbar. press ALT+F10 (PC) or ALT+FN+F10 (Mac).BI U S Paragraphv ArialQ 6 2 = = = = E E x' X 814px

Variables Entered/Removeda Model Variables Entered Variables Removed Method Enter 1 X7, X2, X4, X1, X6, X3, X5b a. Dependent Variable: Y b. All requested variables entered Model Summary R Square .568 Adjusted R Square .500 Std. Error of the Estimate 10.9902 Model R .754a a. Predictors: (Constant), X7, X2, X4, X1, X6, X3, X5 ANOVA Sum of Squares 6998.009 Mean Square 999.716 Model Df F Sig. 000b 1 Regression 8.277 Residual 5314.503 44 120.784 Total 12312.512 51 a. Dependent Variable: Y b. Predictors: (Constant), X7, X2, X4, X1, X6, X3, X5 Coefficients Unstandardized Coefficients Standardized Coefficients Collinearity Statistics VIF O www.youtube.com · 10m Std. Error 9.495 Model B 60.554 Beta Sig. .000 Tolerance 1 (Constant) 6.377 PSL Transfer News | Kaizer Chiefs X1 .001 .001 .164 1.472 .148 .788 1.269 Recommended: Madi Media Foof X2 .087 .048 200 1.809 .077 .801 1.249 000 040 Enn 4000

Variables Entered/Removeda Model Variables Entered Variables Removed Method Enter 1 X7, X2, X4, X1, X6, X3, X5b a. Dependent Variable: Y b. All requested variables entered Model Summary R Square .568 Adjusted R Square .500 Std. Error of the Estimate 10.9902 Model R .754a a. Predictors: (Constant), X7, X2, X4, X1, X6, X3, X5 ANOVA Sum of Squares 6998.009 Mean Square 999.716 Model Df F Sig. 000b 1 Regression 8.277 Residual 5314.503 44 120.784 Total 12312.512 51 a. Dependent Variable: Y b. Predictors: (Constant), X7, X2, X4, X1, X6, X3, X5 Coefficients Unstandardized Coefficients Standardized Coefficients Collinearity Statistics VIF O www.youtube.com · 10m Std. Error 9.495 Model B 60.554 Beta Sig. .000 Tolerance 1 (Constant) 6.377 PSL Transfer News | Kaizer Chiefs X1 .001 .001 .164 1.472 .148 .788 1.269 Recommended: Madi Media Foof X2 .087 .048 200 1.809 .077 .801 1.249 000 040 Enn 4000

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

Vertices Of An Ellipse

In the study of the conic section of the geometry, An ellipse is a plane curve surrounding two focal points, or foci where the set of all locus of points in the plane has their sum of the distances to the two foci is constant. On the ellipse curve, it is the intersection point of the main axis. In an ellipse graph, the center of the axis is the midpoint of the line segment connecting the foci. The major axis is the line section that runs through the ellipse's foci, while the minor axis runs through the middle and is perpendicular to the major axis.. The ellipse has two endpoints on the major axis which are known as the vertices (in plural), vertex in the singular. The equation of the major axis is 2a, the equation of the minor is 2b and the distance between the major axis and minor axis is 2c. The semi-major axis has a length of a and the semi-minor axis has a length of b. The ellipse's vertices are determined by the elliptical orbit's equation and graph.

Question

Suppose multiple linear regression model is fitted with seven independent variable and revealed the following results.
Variables Entered/Removeda
Method
Enter
Model
Variables Entered
Variables Removed
X7, X2, X4, X1, х6, хз, х5b
a. Dependent Variable: Y
b. All requested variables entered
Model Summary
Std. Error of the
Adjusted R
Square
.500
Model
R
R Square
Estimate
10.9902
1
754a
568
a. Predictors: (Constant), X7, X2, X4, X1, X6, X3, X5
ANOVAa
Model
Sum of Squares
6998.009
Df
Mean Square
999.716
F
Sig.
1
Regression
8.277
.000b
7
Residual
5314.503
44
120.784
Total
12312.512
51
a. Dependent Variable: Y
b. Predictors: (Constant), X7, X2, X4, X1, X8, X3, X5
Coefficientsa
Unstandardized
Standardized
Collinearity Statistics
VIF
Coefficients
Coefficients
2 www.youtube.com · 10m
Model
B
Std. Error
Beta
t
Sig.
Tolerance
1 (Constant)
60.554
9.495
6.377
.000
PSL Transfer News | Kaizer Chiefs New
X1
.001
.001
.164
1.472
.148
.788
1.269
Recommended: Madi Media Football
X2
.087
.048
200
1.809
.077
.801
1.249
000
000
n40
1.000
P Type here to search

VIT
1 (Constant)
60.554
9.495
6.377
.000
X1
.001
.001
.164
1.472
.148
.788
1.269
X2
.087
.048
200
1.809
.077
.801
1.249
X3
.009
.009
.133
.948
.349
.500
1.999
X4
-.043
.017
-.290
-2.466
.018
.710
1.408
X5
.047
.012
.559
3.905
.000
.478
2.090
X6
209
.130
.188
1.607
.115
.719
1.391
X7
005
.006
.116
875
387
555
1.800
a. Dependent Variable: Y
a) Is it necessary to use the t-test to test for the significance of each coefficient? Justify your answer.
b) What can you say about the correlation coefficient value in the summary model table?
Question (c) and (d) are based on backward elimination method application:
c) Which variable would be removed first? Justify your answer.
d) After your findings in part (c), is it possible to remove another variable from the same results? Justify your answer.
e)
i) In the output, what does the statistics 'VIF' and Tolerance' assess?
ii) How do they differ from each other?
iii) Explain whether you are concerned about these values?
f) If a simple regression analysis is performed with X3 as the only independent variable, the Sum of Squares Error (SSE) e www.
above multiple regression model with seven independent variables. Test if this simple regression model is signifi PSL Trans
Recomme
For the toolbar. press ALT+F10 (PC) or ALT+FN+F10 (Mac).
BI U S Paragraph
v Arial
Q 6 2 = = = = E E x' X 8
14px

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