Based on the output generated by SPSS, answer the following questions: i. Identify the dependent and independent variables based on the information given in the dataset. iii. What can you conclude from the correlation coefficient (r)? iv. Specify the equation of the regression line. V. Provide a practical interpretation of the estimates for B and in the regression equation. (If a practical interpretation is not possible, please explain why). vi. Identify whether x and y have a linear relationship (inferences about the slope) using the t-test and p-value approaches at 95% confidence level. Specifically, for the t-test approach: a. State the statistical hypotheses that tests the existence of linear relationship between x and y. b. Draw the rejection region using a bell curve and specify the rejection rule. C. Based on the calculated t-test statistics in SPSS and the rejection region/rule in Regression Variables Entered/Removed Variables Variables Model Entered Removed Method 1 Car Weight (kg), xb Enter a. Dependent Variable: CO2 emission (grams per mile), y b. All requested variables entered. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .456 .208 .180 5.365 a. Predictors: (Constant), Car Weight (kg), x Model Regression Residual Total ANOVA Sum of Squares df Mean Square F Sig. 211.960 1 211.960 7.363 .011b 806.040 28 28.787 1018.000 29 a. Dependent Variable: CO2 emission (grams per mile), y b. Predictors: (Constant), Car Weight (kg), x GGraph 115- 110- 105- CO2 emission (grams per mile), y 100- 95- 90- 0 0 0 00 750 1000 750 0 8 00 1250 Car Weight (kg), x 1500 1750 Coefficients Standardized Unstandardized Coefficients Coefficients 95.0% Confidence Interval for B Model B Std. Error Betal t Sig. Lower Bound Upper Bound 1 (Constant) 85.856 5.304 16.188 .000 74.992 96.720 Car Weight (kg), x .011 .004 .456 2.713 .011 .003 .020 a. Dependent Variable: CO2 emission (grams per mile), y Regression Variables Entered/Removed Variables Variables Model Entered Removed Method 1 Car Weight (kg), xb Enter a. Dependent Variable: CO2 emission (grams per mile), y b. All requested variables entered. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 456 .208 180 5.365 a. Predictors: (Constant), Car Weight (kg), x ANOVA Sum of Model Squares df Mean Square F Sig. Regression 211.960 1 211.960 7.363 011b Residual 806.040 28 28.787 Total 1018.000 29 a. Dependent Variable: CO2 emission (grams per mile), y b. Predictors: (Constant), Car Weight (kg), x Unstandardized Coefficients Coefficients Standardized Coefficients 90.0% Confidence Interval for B Model B Std. Error Beta t Sig. Lower Bound Upper Bound 1 (Constant) 85.856 5.304 16.188 .000 76.833 94.878 Car Weight (kg), x .011 .004 456 2.713 .011 .004 .018 a. Dependent Variable: CO2 emission (grams per mile), y Regression Variables Entered/Removed Variables Variables Model Entered Removed Method 1 Car Weight (kg), xb Enter a. Dependent Variable: CO2 emission (grams per mile), y b. All requested variables entered. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .456 .208 .180 5.365 a. Predictors: (Constant), Car Weight (kg), x Model 1 Regression Residual Total ANOVA Sum of Squares df Mean Square F Sig. 211.960 1 211.960 7.363 .011b 806.040 28 28.787 1018.000 29 a. Dependent Variable: CO2 emission (grams per mile), y b. Predictors: (Constant), Car Weight (kg), x Coefficients Standardized Unstandardized Coefficients Coefficients B Model Std. Error Beta t Sig. 1 (Constant) 85.856 5.304 16.188 .000 Car Weight (kg), x .011 .004 .456 2.713 .011 a. Dependent Variable: CO2 emission (grams per mile), y