How should interpret assumptions of the technique used?Based on the tables and charts presented below, interpret the results and report your answer with an academic style. You should interpret assumptions of the technique used. 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 PearsonCorrelationModel SummaryRR Square.883 .779Model1.778a. Predictors: (Constant), Hores Power of the Engine,Number of cylindersb. Dependent Variable: GallonsPer100MilesModel 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

From the above graph we see that, the data fits well in the regression model. There are 3 variables.…

del Normal P-P Plot of Regression Standardized Residual Dependent Variable CallensPer100Mes Expected Cam Prob La Regression Residual Observed Cum Prob Sum of Squares A ANOVA df 848.363 2 240.325 389 1088.688 391 Regression Standardized Residual F 424.182 686.598 .618 Mean Square Total a. Dependent Variable: GallonsPer100Miles h. Predictors (Constant), Hores Power of the Engine, Number of cylinders Sig. .000 Dependent **** *** Regression Standardized Predicted Value

del Normal P-P Plot of Regression Standardized Residual Dependent Variable CallensPer100Mes Expected Cam Prob La Regression Residual Observed Cum Prob Sum of Squares A ANOVA df 848.363 2 240.325 389 1088.688 391 Regression Standardized Residual F 424.182 686.598 .618 Mean Square Total a. Dependent Variable: GallonsPer100Miles h. Predictors (Constant), Hores Power of the Engine, Number of cylinders Sig. .000 Dependent ** * Regression Standardized Predicted Value

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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