Variable Mean Std Dev ZES 26.0009000 16.1864141 170 0000900 17.2162714 Dependent Variable: y Number of Observations Read BE Number of Observations Used Analysis of Variance Sum of Mean Source DF Squares Square F Value |Pr - F Model 2505.28397 2505.28397 49.15 0001 Error 458.71603 50.96845 Corrected Total 0 2964.00000 Root MSE 7.13922 R-Square |0.8452 Dependent Mean 170.00000 Adj R-Sq 0.8280 Coeff Var 4.19954 Parameter Estimates Parameter Standard Variable DF Estimate Error t Value |Pr - It Intercept 144.57557 4.21712 34.28 <.0001 to predict math scores using writing as independent variable perform a regression analysis for males and females separately. the weight systolic blood pressure of randomly selected in age group are shown sas code. assume that any fixed value bp is normally distributed. find line relating weight. test hypothesis there no linear relationship between bp. you may use o=".05)" ci slope line.3. company manufactures spare parts experiences fluctuations lot sizes due supply demand. interested examining number man-hours worked size. taken dependent where each y combination represents one production run. summary sample data given below answer following questions. mean variance correlation r .9978 equation least square these coefficient determination r2. give an interpretation context problem. fitted man hours when size set at researchers assessing average cigarettes smoked day x among male smokers. on this output shows day. report prediction interpret slope. standard deviations correlation. its value. r2 null smokers indepen dent p-value. construct confidence interval interpret. consider hsb do followings: compare wrtg females. draw scatter plot overlay two plots>