Consider the following Stata regression output (IDMC5) Source SS df MS Number of obs 935 %3D F(1, 933) 111.79 %3D Model 16340644.5 1 16340644.5 Prob > F %3D 0.0000 Residual R-squared Adj R-squared 136375524 933 146168.836 0.1070 %3D %3D 0.1060 Total 152716168 934 163507.675 Root MSE 382.32 %3D wage Coefficient Std. err. t P>|t| [95% conf. interval] educ 60.21428 5.694982 10.57 0.000 49.03783 71.39074 cons 146.9524 77.71496 1.89 0.059 -5.56393 299.4688 where wage is monthly wage in US$ and educ is years of education. What is the predicted effect of a 5 year increase of education on wage?

10.  You estimated a regression with the following output.         Source |       SS           df       MS      Number of obs   =       333 -------------+----------------------------------   F(1, 331)       =   4608.21        Model |  32636494.1         1  32636494.1   Prob > F        =    0.0000     Residual |   2344225.8       331  7082.25316   R-squared       =    0.9330 -------------+----------------------------------   Adj R-squared   =    0.9328        Total |  34980719.9       332  105363.614   Root MSE        =    84.156   ------------------------------------------------------------------------------            Y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval] -------------+----------------------------------------------------------------            X |   30.79902   .4537022    67.88   0.000     29.90652    31.69153        _cons |   20.85313    42.1964     0.49   0.621     -62.1538    103.8601…

A student used multiple regression analysis to study how family spending (y) is influenced by income (x1), family size (x2), and additions to savings(x3). The variables y, x1, and x3 are measured in thousands of dollars. The following results were obtained.   ANOVA                                   df                  SS Regression                3                   45.9634 Residual                  11                   2.6218 Total                                 Coefficients     Standard Error Intercept                0.0136 x1                          0.7992                   0.074 x2                          0.2280                   0.190 x3                         -0.5796                   0.920   Write out the estimated regression equation for the relationship between the variables.  Compute coefficient of determination. What can you say about the strength of this relationship?  Carry out a test to determine whether y is…

The regression output is reported in Table 4. Why does Stata omit hhsize3? Would it make any difference if Stata omitted hhsize3 instead of hhsize1?

Do the following plots show 1. Constant variability  2. Nearly normal residuals 3.  Independent observations for SLR (conditions for linear regression)

Please do not give solution in image format thanku