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The pickage 'faraway contains the aasion data st recording 'Four material Wcre fed into #r testing machine althe Amount of Wt rccordexl _ Use aov() perfot...

Question

The pickage 'faraway contains the aasion data st recording 'Four material Wcre fed into #r testing machine althe Amount of Wt rccordexl _ Use aov() perfotmn Wly ANOVA fr Wo bv `material' Statt with compratin plot of tle 'material' goups ad g0 all the way through pata Iwoc comparisls Hackl, MIc | cluking of aysumptions_ State sour hypot hcncn #II ccuclusicus.

The pickage 'faraway contains the aasion data st recording 'Four material Wcre fed into #r testing machine althe Amount of Wt rccordexl _ Use aov() perfotmn Wly ANOVA fr Wo bv `material' Statt with compratin plot of tle 'material' goups ad g0 all the way through pata Iwoc comparisls Hackl, MIc | cluking of aysumptions_ State sour hypot hcncn #II ccuclusicus.



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Fill in the ANOVA table. $$\begin{array}{lcccc} \text { Source of } & \text { Sum of } & \text { Degrees of } & \text { Mean } & \text { F-Test } \\ \text { Variation } & \text { Squares } & \text { Freedom } & \text { Squares } & \text { Statistic } \\ \hline \text { Treatment } & 387 & 2 & & \\ \hline \text { Error } & 8042 & 27 & & \\ \hline \text { Total } & & & & \end{array}$$

Part one. Okay, we at new sub to hat to the original equation. An estimated by old L s. The coefficient on new to hat is minus point Oh, 57 with 80 statistic of minus one point. Oh, eight. So what's the difference in the estimates of the return to education is large. It is not statistically significant. Part two. We now add near C two as an ivy along with near C four. The two stage least square estimate of beta one is now 0.157 with a standard error of point. Oh, 53 Okay, so the estimate is even larger. Yeah. Part three. Let you said I had to be the two stage least square residuals. Mm. We regret these on all exogenous variables, including near C two and near C four. The Choice square statistic, which is calculated by ticking and the number of observation multiply with our square from the regression is the product of 3000 and 10 observations and 0.4 which is the our square of the regression. You get 1.2. Yeah. The P value of this called square sta district is mhm 0.5 0.55 Yeah, so the over identifying restriction is not rejected. Mhm. Yeah,


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