Please interpret clearly and how to do if based on the tables and charts presented below, interpret the results and report your answer with an academic style. How should interpret assumptions of the technique used. PearsonCorrelationModel SummaryModel1.778a. Predictors: (Constant), Hores Power of the Engine,Number of cylindersb. Dependent Variable: GallonsPer100MilesRR Square.883 .779Model Dimension Eigenvalue1 12.917Sig (1-tailed)Model23a. Dependent Variable: GallonsPer 100MiesCollinearity. Diagnostics067016ContarNumber of cindersHores Power of theEnginea Dependent variable CalenAdjusted RSquareConditionNumber of Hores PowerIndex (Constant) cylinders of the Engine1.000 01001485GallonaPer100MenNumber of cylindersHores Power of theEngine6.596 8513.68014CorrelationsGallonsPer100MilesNumber of cylindersHors Power of theEngineGallonsPer100MilesNumber of cylindersHores Power of theEngine2804062.185GalonsPer100MUnstandardand Coefficients1 Std. Ervar134141921.000840854*** 88.000392392Std. Error ofthe Estimate78600Variance Proportions392Number ofcylindersStandardedCafficerska0003971401.00034300000039239239212.094415 9.374504 11.343HoresPower ofthe EngineCoefficients8548431.00000000030239230295.5 Confidence aloSlow Round pe lund -ade370000003173211808542491 140 4292.563814500Pot T221271219 14582014 ModelNormal P-P Plot of Regression Standardized ResidualDependent Variable CallonsPer100MesRegressionResidualObserved Cum ProbSum ofSquaresANOVAdf848.3632240.3253891088.688 391F424.182 686.598.618Mean SquareTotalDependent Variable, GallonsPer100Milesh. Predictors (Constant), Hores Power of the Engine, Number of cylindersSig..000Dependent Variable CallionsPer100MilesRegression Standardized Predicted Vale

There are 4 variables. ‘Gallons per 1000 miles’ is dependent variable (Y) and ‘Horse power of…

1 2 Model 1 .778 a. Predictors: (Constant), Hores Power of the Engine, Number of cylinders b. Dependent Variable: GallonsPer100Miles Model Dimension Eigenvalue Pearson Correlation R R Square .883⁰ .779 Sig (1-tailed) Model Model Summary 3 a Dependent Variable: GallonsPer100Mies Collinearity Diagnostics Contard Number of cinders Hores Power of the Engine a Dependent arable Calen Adjusted R Square GallonsPer100Me Number of cylinders Hores Power of the Engine GallonsPer100M Condition Number of Hones Power Index (Constant) cylinders of the Engine 2917 1.000 01 067 6.596 85 016 13.680 14 Number of cylinders Hores Power of the Engine GallonsPer100M Number of cylinders Hores Power of the Engine Correlations 280 A06 2.185 GalonsPer 100M Unstandarded Cofficients 1 1.000 840 804 134 14 192 *** 88. 000 392 392 Variance Proportions 392 Std. Ervar la Std. Error of the Estimate 78600 Standard Coffe Hores Number of Power of cylinders the Engine 415 & 885 00 140 1.000 343 000 000 9.374 304 11.383 392 302 392 Coefficients 1 S 2014 47 BES 854 843 1.000 000 000 302 302 302 00 14 85 95.05 Confidence alo Lower Band 317 321 1.800 Convites pe land bade 542 491 2.561 429 854 500 340 E Collar un I Ft T 221 29 145 271 219 149

1 2 Model 1 .778 a. Predictors: (Constant), Hores Power of the Engine, Number of cylinders b. Dependent Variable: GallonsPer100Miles Model Dimension Eigenvalue Pearson Correlation R R Square .883⁰ .779 Sig (1-tailed) Model Model Summary 3 a Dependent Variable: GallonsPer100Mies Collinearity Diagnostics Contard Number of cinders Hores Power of the Engine a Dependent arable Calen Adjusted R Square GallonsPer100Me Number of cylinders Hores Power of the Engine GallonsPer100M Condition Number of Hones Power Index (Constant) cylinders of the Engine 2917 1.000 01 067 6.596 85 016 13.680 14 Number of cylinders Hores Power of the Engine GallonsPer100M Number of cylinders Hores Power of the Engine Correlations 280 A06 2.185 GalonsPer 100M Unstandarded Cofficients 1 1.000 840 804 134 14 192 *** 88. 000 392 392 Variance Proportions 392 Std. Ervar la Std. Error of the Estimate 78600 Standard Coffe Hores Number of Power of cylinders the Engine 415 & 885 00 140 1.000 343 000 000 9.374 304 11.383 392 302 392 Coefficients 1 S 2014 47 BES 854 843 1.000 000 000 302 302 302 00 14 85 95.05 Confidence alo Lower Band 317 321 1.800 Convites pe land bade 542 491 2.561 429 854 500 340 E Collar un I Ft T 221 29 145 271 219 149

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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Question

Please interpret clearly and how to do if based on the tables and charts presented below, interpret the results and report your answer with an academic style. How should interpret assumptions of the technique used.

Pearson
Correlation
Model Summary
Model
1
.778
a. Predictors: (Constant), Hores Power of the Engine,
Number of cylinders
b. Dependent Variable: GallonsPer100Miles
R
R Square
.883 .779
Model Dimension Eigenvalue
1 1
2.917
Sig (1-tailed)
Model
2
3
a. Dependent Variable: GallonsPer 100Mies
Collinearity. Diagnostics
067
016
Contar
Number of cinders
Hores Power of the
Engine
a Dependent variable Calen
Adjusted R
Square
Condition
Number of Hores Power
Index (Constant) cylinders of the Engine
1.000 01
00
14
85
GallonaPer100Men
Number of cylinders
Hores Power of the
Engine
6.596 85
13.680
14
Correlations
GallonsPer100Miles
Number of cylinders
Hors Power of the
Engine
GallonsPer100Miles
Number of cylinders
Hores Power of the
Engine
280
406
2.185
GalonsPer
100M
Unstandardand Coefficients
1 Std. Ervar
134
14
192
1.000
840
854
*** 88.
000
392
392
Std. Error of
the Estimate
78600
Variance Proportions
392
Number of
cylinders
Standarded
Cafficers
ka
00
03
97
140
1.000
343
000
000
392
392
392
1
2.094
415 9.374
504 11.343
Hores
Power of
the Engine
Coefficients
854
843
1.000
000
000
302
392
302
95.5 Confidence alo
Slow Round pe lund -ade
37
000
000
317
321
1808
542
491 140 429
2.563
814
500
Pot T
221
271
219 1458
2014

Model
Normal P-P Plot of Regression Standardized Residual
Dependent Variable CallonsPer100Mes
Regression
Residual
Observed Cum Prob
Sum of
Squares
ANOVA
df
848.363
2
240.325
389
1088.688 391
F
424.182 686.598
.618
Mean Square
Total
Dependent Variable, GallonsPer100Miles
h. Predictors (Constant), Hores Power of the Engine, Number of cylinders
Sig.
.000
Dependent Variable CallionsPer100Miles
Regression Standardized Predicted Vale

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