Stat-403/Stat-650 : Intermediate Sampling and Experimental Design ...
Stat-403/Stat-650 : Intermediate Sampling and Experimental Design ...
Stat-403/Stat-650 : Intermediate Sampling and Experimental Design ...
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Index of Pamphlet Topics<strong>Experimental</strong> <strong>Design</strong>17: What is the design?34: When are blocks pseudo-replicates?44: What do we look for in a working plan?<strong>Experimental</strong> unit – see Treatment unitF-Distribution15: Using SAS to obtain probability values for F-,t-, <strong>and</strong> χ 2 statistics37: A general description of hypothesis testing <strong>and</strong>power analysis52: Post-hoc power analyses for ANOVA F-testsF-Max Test25: ANOVA: The within sums of squares as anaverage varianceF-Test18: Multiple regression: selecting the best subject27: When the t-test <strong>and</strong> the F-test are equivalent28: Simple Regression with replication: testing forlack of fit31: ANCOVA: The linear models behind the F-tests45: Calculating contrast F-tests when SAS will not46: GLM: Comparing regression linesFactor Relationship Diagram55: Displaying factor relationships in experimentsFactorial <strong>Design</strong>14: ANOVA: Factorial designs with a separatecontrol17: What is the design?48: ANOVA: Why a fixed effect is tested by itsinteraction with a r<strong>and</strong>om effect53: Balanced incomplete block (BIB) study designs54: Incomplete block designs: Connected designscan be analysed55: Displaying factor relationships in experiments57: Interpreting main effects when a two-wayinteraction is present59: ANOVA: Coefficients for contrasts <strong>and</strong> meansof incomplete factorial designs60: MANOVA: Profile Analysis – an exampleusing SAS43Homogeneity of Variance25: ANOVA: The within sums of squares as anaverage varianceHypothesis Testing30: Interpretation of probability p-values37: A general description of hypothesis testing <strong>and</strong>power analysis48: ANOVA: Why a fixed effect is tested by itsinteraction with a r<strong>and</strong>om effectIndicator (Dummy) Variables56: The use of indicator variables in non-linearregressionLack of Fit28: Simple regression with replication: testing forlack of fitLinear Combination16: ANOVA: Contrasts viewed as t-testsLinear Models28: Simple regression with replication for lack of fit31: ANCOVA: The linear models behind the F-tests46: GLM: Comparing regression linesLog-linear model36: Contingency tables <strong>and</strong> log-linear modelsLogistic Regression7: Logistic regression analysis: model statementsin PROC CATMODLSD (Least Significant Difference)13: ANCOVA: comparing adjusted means57: Interpreting main effects when a two-wayinteraction is presentMallow’s CP18: Multiple regression: selecting the best subset3
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