## Interpreting least squares without sampling assumptions |

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3840 Regression Equations approximately the proportion ave(bk Beaton calculations catchers chi-squared distribution column computed by dividing cov(bk Covariance Decision Rule diagonal element diagonal matrix differ in sign different signs dividing the number e'We Educational Testing Service estimate F table goodness-of-fit hypothesis testing identity matrix Insert Figures Insert Table interpretation inverse ktb signed permutation kurt(bkj kurtosis linear model nonzero element normal curve normal distribution notation Note Nth order vector odd powers Ordinary Least Squares Permuted Residuals Permuting the Weighted population parameters possible signed permutations Proof pseudo-data vector random sample randomization test Regression Analysis regressors resigned and permuted response variable result Robust/resistant regression sampling assumptions sampling theory shown in Figure signed permutation matrices signed permutation scheme skew(bkj standard error subset summary Theorem Tukey unweighted var(bkj variance of residuals vector of fitted vector of residuals Weighted least squares weighted regression Weighted Residuals Weighted values X'WX Xb Nth order zero Ziqii