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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
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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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
Transcribed Image Text: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
Transcribed Image Text: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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