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(Requires Appendix Material)Consider the Following Population Regression Function Model with Two

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(Requires Appendix material)Consider the following population regression function model with two explanatory variables: Yi=β^0+β^1X1i+β^2X2iY _ { i } = \hat { \beta } _ { 0 } + \hat { \beta } _ { 1 } X _ { 1 i } + \hat { \beta } _ { 2 } X _ { 2 i } It is easy but tedious to show that SE( β^2\hat { \beta } _ { 2 } )is given by the following formula: σβ1^2=1n[11ρx1,x22]σu2σ2X1\sigma_{\hat{\beta_{1}}}^{2}=\frac{1}{n}\left[\frac{1}{1-\rho_{x_{1}, x_{2}}^{2}}\right] \frac{\sigma_{u}^{2}}{\sigma^{2} \mathrm{X}_{1}}
Sketch how SE( β^2\hat { \beta } _ { 2 } )increases with the correlation between X1iX _ { 1 i } and X2iX _ { 2 i }

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