A researcher wants to build a multiple linear regression model in order to predict height of child (inch) based on the height of the parents (inch) and sex of the child (coded as 0=female , 1= male)1. Given that the father is 60 inches tall and mother is 70 inches tall, use the regression equation to predict the height of the son. 2. Interpret the regression coefficient of the dummy variable. * Q3-Q5 conduct a global test of hypothesis to determine whether the regression model is significant. *(a=0.05)3.state the null hypothesis in symbolic form4. What is the p-value5. State the decision and conclusionQ6-Q8 * At 5% level of significant, test a hypothesis to determine whether height of father is significant explanatory variable. 6. state the null hypothesis in symbolic form.7. What is the p value8. State the decision and conclusion9. Which of the independent variable (s) is (are) significant at 5% level of significance.

Dependent Variable: CHILD_HEIGHT Number of Observations Used 20 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model 3 157.90196 52.63399 8.29 0.0015 Error 16 101.56754 6.34797 Corrected Total 19 259.46950 Root MSE 2.51952 R-Square 0.6086 Parameter Estimates Parameter Standard Variable DF Estimate Error t Value Pr > It] Intercept 1 25.58789 21.94242 1.17 0.2606 MOTHER HEIGHT 1 0.37705 0.30794 1.22 0.2385 FATHER HEIGHT 1 0. 19548 0.19009 1.03 0.3191 SEX 1 4. 14772 1.33432 3.11 0.0068 SAS output A2 : Regression Dependent Variable: SEX Number of Observations Used 20 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model 3 2.77704 0.92568 6.66 0.0040 Error 16 2.22296 0.13893 Corrected Total 19 5.00000 Root MSE 0.37274 R-Square 0.5554 Parameter Estimates Parameter Standard Variable DF Estimate Error t Value Pr > It| Intercept -7.49583 2.81453 -2.66 0.0170 MOTHER HEIGHT 1 0.03559 0.04681 0.76 0.4581 FATHER HEIGHT 1 -0.00195 0.02903 -0.07 0.9474 CHILD HEIGHT 1 0.09078 0.02920 3.11 0.0068