(i) Use OLS to estimate a linear probability model to determine the likelihood that a young male was arrested during 1986: arr86=?0+?1pcnv+?2avgsen+?3tottime+?4ptime86+?5qemp86+uarr86=?0+?1pcnv+?2avgsen+?3tottime+?4ptime86+?5qemp86+u, where narr86narr86 is the binary variable. If a man was arrested during 1986, arr86=1arr86=1, otherwise arr86=0arr86=0. The estimated equation is as follows: arr86ˆ=arr86^= 0.4434 +(-0.1597) pcnvpcnv + 0.0061 avgsenavgsen + (-0.0016) tottimetottime + (-0.0226) ptime86ptime86 + (-0.0431) qemp86qemp86 . Obtain the fitted values from the regression. The smallest fitted value is approximately 0.428 and the largest fitted value is approximately 1 . (ii) Estimate the following equation by weighted least squares using weights 1hiˆ?1hi^, where hiˆ=arr86iˆ(1?arr86iˆ)hi^=arr86i^?1?arr86i^. (Hint: Run the regression without an intercept and note that ?0ˆ?0^ is a coefficient of 1hiˆ?1hi^.) The estimated equation is as follows: arr86*ˆ=arr86*^= int*int* + pcnv*pcnv* + avgsen*avgsen* + tottime*tottime* + ptime86*ptime86* + qemp86*qemp86* .