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Regressions

Regressions. Regression Here are simple uses of proc reg for standard problems: proc reg; /* simple linear regression */ model y = x; proc reg; /* weighted linear regression */ model y = x; weight w; proc reg; /* multiple regression */

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Regressions

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  1. Regressions • Regression • Here are simple uses of proc reg for standard problems: • proc reg; /* simple linear regression */ • model y = x; • proc reg; /* weighted linear regression */ • model y = x; • weight w; • proc reg; /* multiple regression */ • model y = x1 x2 x3;

  2. model y = x / noint; /* regression with no intercept */ model y = x / ss1; /* print type I sums of squares */ model y = x / p; /* print predicted values and residuals */ model y = x / r; /* option p plus residual diagnostics */ model y = x / clm; /* option p plus 95% CI for estimated mean */ model y = x / cli; /* option p plus 95% CI for predicted value */ model y = x / r cli clm; /* options can be combined */

  3. model y = x / covb; /* covariance matrix for estimates */ model y = x / collin; /* collinearity diagnostic */ model y = x / collinoint; /* collin without intercept */ The output phrase can have several keywords (which can be used together): output out=b predicted=py; /* predicted values in "py" */ output out=b p=py; /* same as predicted */ output out=b residual=ry; /* residual values in "ry" */ output out=b r=ry; /* same as residual */ output out=b stdr=sr; /* standard error of residuals "sr" */ output out=b student=sy; /* studentized residuals "sy" */

  4. proc glm; /* analysis of covariance */ class trt; /* trt = factor, x = covariate */ model y = x trt; proc glm; /* analysis of covariance */ class trt; /* with different slopes */ model y = x trt x*trt;

  5. proc glm; /* simple linear regression */ model y = x / solution; proc glm; /* weighted linear regression */ model y = x / solution; weight w; proc glm; /* multiple regression */ model y = x1 x2 x3 / solution; proc glm; /* one-way analysis of variance */ class trt; model y = trt; proc glm; /* additive two-factor anova */ class fert var; model y = fert var; proc glm; /* full two-factor anova */ class fert var; model y = fert | var; proc glm; /* analysis of covariance */ class trt; /* trt = factor, x = covariate */ model y = x trt; data testlin; set resps; x = level; proc glm; /* test for non-linearity */ class level; resp = x level;

  6. model y = trt x / solution; /* print parameter estimates and SEs */ model y = x / noint; /* no intercept (as in proc reg) */ model y = x / ss1; /* print only type I sums of squares */ model y = x / ss2; /* print only type II sums of squares */ model y = x / p; /* print predicted values and residuals */ model y = x / clm; /* option p plus 95% CI for estimated mean */ model y = x / cli; /* option p plus 95% CI for predicted value */ model y = x / cli alpha=.01; /* only .01, .05 and .10 available */

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