105. The coefficient of determination of the least squares regression model for the bivariate data below is r^2 = _______ 0.950 0.748 0.937 0.902

What is the maximum and mininumber of the range regarding the explanatory variable for which the regression model can make valid predictions?

The value α that represents the probability of type I error is often referred to as the __________________________ of the test.

stion 4 of 14 > The scatterplot given compares data on the fuel consumption y of a car at various speeds x. Fuel consumption is measured in liters of gasoline per 100 kilometers driven, and speed is measured in kilometers per hour. A statistical software package gives the least-squares regression line ŷ = 11.058 +0.01466x. 10.0 - Use the residual plot to determine if this linear model is 75 appropriate. O No. There is an obvious positive- negative - positive 5.0 - pattern in the residual plot so a linear model is not appropriate for these data. O Yes. There is an obvious positive – negative – positive pattern in the residual plot so-a linear model is appropriate for these data: O No. The residuals do not have equal variability in the residual plot so a linear model is not appropriate for 2.5 - -2.5 -5.0 - 20 40 60 80 100 120 140 160 these data. Speed (km/h) O No. The residuals are not equally positive or negative in the residual plot so a linear model is not appropriate for these data.…

A researcher conducts a multiple regression with Y as the dependent variable and X1, X2, X3 and X4 as explanatory variables. Using the regression output below, fully describe this model and discuss important parts of the output. What is the predicted value of Y if X1 = 3, X2 = 15, X3 = 7 and X4 = 0.003? %3D SUMMARY OUTPUT Regression Staistics Muliple R R Square Adjusted R Square Standard Emor Observations 0.7236 0.5236 0.5159 5.3928 252 ANOVA Significance F 1. 10662E-38 SS MS Regression Residual 1973 9392 29.0820 67.8749 7895.7567 7183.2599 4 247 Total 251 15079.0166 Upper 95% 33.4049 Coefficients Standard Eror t Stat Pvalue 2.2273 0.026830873 Lower 95% 7.9594 2.0508 Intercept X1 17.7278 1.5583 0.2750 5.6662 4.05265E-08 1.0166 2.0999 X2 1.8376 0.1997 9.1999 1.4442 -74708 -3721 4324 1.55861E-17 2.2310 X3 55100 -5.5348 7.94036E-08 -3.5492 X4 -3.1079 1887 8435 -0.0016 0.998687788 3715 2166

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