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4. Consider a multiple linear regression model Y5 = :30 + 515511 + 32332 + {333-3 + 34309(m4l + 5i- We have the following statistics for the regression Call: lm(fnrmula = 3r " :1 + x2 + x3 + long4)) Coefficients: Estimate Std. Error t value Pr(>|t|} (Intercept) 154.1928 194.9062 0.791 0.432938 :1 -4.2280 2.0301 -2.083 0.042873 * :2 -6.1353 2.1936 -2.797 0.007508 HI :3 0.4719 0.1285 3.672 0.000626 \"HI: :4 26.?552 9.33?4 2.865 0.006259 I\" Signif. codes: 0 'HHII' 0.001 \"N" 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 64.89 on 46 degrees of freedom Multiple R-squered: 0.5105,.Fidjusted Rsquared: 0.4679 Fstatistic: 11.99 on 4 and 46 DF. p-value: 9.331e-07 The diagnostic statistics for three cases were the following: case3 183.499 0.084 (a) (4 points) Interpret ,82, i.e. hold 11:1, :33 and :34 constant, if 1:2 increase 1 unit, what is the change of Y? Similarly, properly interpret ,64. (b) (9 points) Compute 1: for each of these cases, test outliers at level a = 0.05 and draw your conclusion.[t45,g_g?5 = 2.014) (c) (4 points) Compute Cook's distance D; for each of these casa, and rank them from highest to lowest in terms of their inuence