A.) Why the variables might be related at all B.) Why the relationships might be

Question

A.) Why the variables might be related at all

B.) Why the relationships might be linear

C.) The interpretation of the estimated slope and intercept

D.) The statistical significance of the model estimates

/CRITERIA-PIN 1.05, /MOORIGIN EOUI (.10) DEPENDENT car Regression Variables Entered/Removed Variables Entered Variables Removed Nethod Population m b. AI requesled vanatles ertered. Model Summary Model R Bquan Squara 744 4953 a. Predi:tors (Constant, Population ofUS (000 ANOVA Squares 0302 729 425.089 13728 316 Residual 38 90.160 Total a. DependemVa iable: Cars in use miliors b. Predistors (Constant. Population ofU5 (000 Coefficients Sandareized Ccemcierts Bera Unstandardeed Coemeients 5td. Errer iq. Constant Pooulaionorus 12.394 274 056 1C.6S0 a. Dependent Va iable: Cars in use miliors

Explanation / Answer

A.)

Here, we are trying to predict the cars in use in the United States based on the population which is a right combination of dependent-independent variables. Cars in use would depend on the population of the United States

B.)

Relationship is linear because the cars in use might increase/decrease with the changes in population

C.)

Intercept: Cars in use might decrease by -13.762 even when there is no change in population (i.e. excluding all other factors)

Slope: Cars in use would increase by 0.001 keeping all other factors constant

D.)

Slope is significant as the significance level is 0.0 which is less than 0.05

E.)

Overall model is significant as the significance level is 0.0 which is less than 0.05

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