ModelRR SquareAdjusted R SquareStd. Error of the Estimate1.328a.108-.190.0714662a. Predictors: (Constant), BI ANOVAaModelSum of SquaresdfMean SquareFSig.1Regression.0021.002.362.590bResidual.0153.005 Total.0174 a. Dependent Variable: Logarithm of SRb. Predictors: (Constant), BI CoefficientsaModelUnstandardized CoefficientsStandardized CoefficientstSig.BStd. ErrorBeta1(Constant)6.157.118 52.393.000BI-.013.022-.328-.601.590a. Dependent Variable: Logarithm of SR Required analys and interpret simple linear regression model

Given that, the dependent Variable is Logarithm of SR and independent variable is BI. So the…

Answered: Required analys and interpret simple…

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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Q19
Solve last 2 parts
Dependent Variable: Plant Growth Source Corrected Model Intercept Watering frequency Sunlight Watering frequency * Sunlight Tests of Between-Subjects Effects Error Total Type III Sum of Squares 13.921 250.260 1.550 2.870 9.500 df 11.068 275.250 Corrected Total 24.990 a. R Squared= .557 (Adjusted R Squared= .491) 3 1 1 1 1 20 24 23 Mean Square 4.640 250.260 1.550 2.870 9.500 ,553 F 8.385 452,210 2.802 5.187 17.167 SPSS output reads that the p-value for the main effect of sunlight is [ Select] , meaning that the null hypothesis is [Select] .000 .110 .034 .001 Sig. .001 .000 110 .034 .001
Source SS DF MS Test Statistic Regression 3 1094.23 Error ? 648.43 Total 11063.85 15 Step 2 of 9: Calculate the Sum of Squared Error. Round your answer to two decimal places, if necessary. -caculate the degress of freedom among error -caculate the value of the test stastic -how much of the varation in the dependent variable is explained by the regression
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You may need to use the appropriate technology to answer this question. Following is a portion of the computer output for a regression analysis relating y = maintenance expense (dollars per month) to x = usage (hours per week) of a particular brand of computer termina Analysis of Variance SOURCE Regression Error Total Predictor Constant X DF Adj SS 1 1575.76 8 349.14 9 1924.90 Regression Equation Y = 6.1092 +0.8951 X O Ho: B₁ * 0 H₂: B₁ = 0 Coef SE Coef 0.9361 0.1490 (a) Write the estimated regression equation. ý =| 6.1092+ 0.8951r O Ho: B₁ 20 H₂: B₁ <0 |0 Ho: Boo Hà Bo=0 |0 Ho: Bo=0 = 0 Ha: Bo #0 6.1092 0.8951 Ho: B₁ = 0 H₂: B₁ * 0 (b) Use a t test to determine whether monthly maintenance expense (dollars per month) is related to usage (hours per week) at the 0.05 level of significance. State the null and alternative hypotheses. Adj MS 1575.76 43.64 Find the value of the test statistic. (Round your answer to two decimal places.) 36.11 X
Engineer/Stats/Economics. Entry Level Salary (of 500 MBAS) was regressed on X1: MBA CGPA, X2: Undergrad CGPA and whether the student was Dummy 1 = 1 for Engineer; Dummy 2 = 1 for Stats. The output from a Statistical package is given below. Answer the questions that follow. Variable Intercept X1 X2 Dummy 1 Dummy 2 Parameter Estimates Coefficient Std Error t Value P Value 1.41 3.20 2.12 3.10 2.00 3.21 6.82 5.25 2.60 3.93 2 1 O www.w 2.28 2.13 2.48 0.84 1.97 0.02 0.03 0.01 0.40 0.05 What is the advantage of Statistics over Economics? Std Estimates VIF 90% Conf Limits 0.89 0.70 1.88 6.55 0.35 11.00 4.94 0.20 1.45 -4.37 0.10 4.20 -0.57 5.53 12.08 8.74 7.70 7.22 Explain your inference from the QQ Plot List the variables accepted at alpha - 10%? Explain which independent variable is most collinear with the other independent. variables? Which variable is influencing the dependent variable the maximum? I
Model Summary Adjusted R Square Std. Error of Model R R Square the Estimate 1 .772 .596 .516 215.1509 a. Predictors: (Constant), Porosity ANOVA Sum of Model Squares df Mean Square F Sig. Regression 341943.282 1 341943.282 7.387 .042b Residual 231449.575 46289.915 Total 573392.857 a. Dependent Variable: Runoff b. Predictors: (Constant), Porosity Coefficients Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig. (Constant) 1863.761 237.546 7.846 .001 Porosity -80.729 29.703 -.772 -2.718 .042 a. Dependent Variable: Runoff A geomorphologist is building a model to understand variation in the amount of runoff observed in various streams within a large drainage basin. Linear regression is used to determine whether runoff is inversely related to the porosity of the soil. The variables here are thus runoff and porosity. The SPSS output from the analysis is shown above. One of the cases has a runoff value of 900 and a porosity value of 8. Calculate the value of the…
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Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.328^a	.108	-.190	.0714662
a. Predictors: (Constant), BI

ANOVA^a
Model	Sum of Squares	df	Mean Square	F	Sig.
1	Regression	.002	1	.002	.362	.590^b
Residual	.015	3	.005
Total	.017	4
a. Dependent Variable: Logarithm of SR
b. Predictors: (Constant), BI

Coefficients^a
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.
B	Std. Error	Beta
1	(Constant)	6.157	.118		52.393	.000
BI	-.013	.022	-.328	-.601	.590
a. Dependent Variable: Logarithm of SR