SAS programming Statistical Analyst Certification Course

upload data files and sas programs into sas on demand for academics by extracting zip files and organizing them in a dedicated sas base codes folder.

Use Levene’s test to check homogeneity of variances in anova and apply the means statement in proc glm; the example shows p=0.09, not rejected.

Explore the randomized block design to control nuisance factors and improve treatment effect estimates. Apply SAS GLM to test reading score differences across parent education groups, blocking by SMS minutes.

In unbalanced randomized block designs, use type iii sums of squares and the lsmeans statement for one-factor analysis, and rely on unweighted means for accurate pairwise comparisons via proc glm.

Learn how to prepare input variables for predictive modeling by cleaning data, handling missing values, reducing redundancy, and ensuring preprocessing is applied to training data to prevent information leakage.

Learn to handle missing values with complete case analysis and imputation, using missing value indicators and median imputation, while considering MCAR, MAR, and NMAR patterns in SAS.

Use the Greenacre method to collapse state categories, import Excel data with proc import, compute chi-square via proc freq, and use proc means to create state proportions for clustering.

Identify a two-cluster solution using proc sql and proc tree, then create a sta_class variable to compare chi square via the Greenacre method.

Screen inputs for monotonic relevance using Spearman and Hoeffding D, flagging variables with p values over 0.5 as potentially irrelevant and noting any non-monotonic patterns for further empirical logit analysis.

Apply empirical logit to detect non-linear associations in logistic regression, using logit plots by predictor and binning via the rank procedure and proc means to reveal nonlinearity.

Quantify the association between two continuous variables with the Pearson correlation using the CORR procedure, interpreting strength, direction, and significance alongside scatter plots and descriptive statistics.

This lecture covers detecting collinearity in regression using variance inflation factor and the condition index, with collin and collinoint, and advises deleting predictors or applying ridge or principal component regression.

Learn backward elimination in proc reg to remove the least significant predictors by p-values, ending when remaining variables meet p<0.01 and observing adjusted r-squared trends.

Explore the glmselect procedure for variable selection, highlighting stepwise methods, collinearity cautions, and p-value risks; review a SAS glmselect example predicting reading scores.

Explore logistic regression with a continuous predictor, interpret odds ratios, and assess model fit with AIC, likelihood ratio tests, and confidence intervals; evaluate predictive power using ROC AUC.

Explore logistic regression with a three-level categorical predictor using effect coding parameterization in SAS, including dummy design variables, class statement handling, and interpretation via odds ratios.

Master backward elimination in multiple logistic regression with SAS PROC LOGISTIC, starting from a full model and using chi-square tests to remove least significant predictors at a 0.01 level.

Explore best subset selection for multiple logistic regression, using score-based chi-square criteria to choose models from two to six predictors, including dummy variables from a categorical predictor.

Model interaction terms in multiple logistic regression and test their significance. Apply backward elimination under model hierarchy and interpret conditional odds ratios with plots.

Compare seven-predictor and three-predictor models using the roc and roc contrast statements on validation data set, evaluating roc curves and AUC, and prefer the simpler model when differences are non-significant.