Stat-403/Stat-650 : Intermediate Sampling and Experimental Design ...

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Index of Pamphlet TopicsRegression21: What are degrees of freedom?27: When the t-test and the F-test are equivalent28: Simple regression with replication: testing forlack of fit29: Simple regression: Confidence intervals for apredicted X-value31: ANCOVA: The linear models behind the F-tests46: GLM Comparing regression lines56: The use of indicator variables in non-linearregression58: On the presentation of statistical results: asynthesisRepeated Measures32: Analysing a split-plot in time with the properrepeated measures ANOVA39: A repeated measures example58: On the presentation of statistical results: asynthesis60: MANOVA: Profile Analysis – an exampleusing SASReplication2: The importance of replication in analysis ofvariance5: Understanding replication and pseudoreplication17: What is the design?28: Simple regression with replication: testing forlack of fit48: ANOVA: Why a fixed effect is tested by itsinteraction with a random effectResidual Sums of Squares31: ANCOVA: The linear models behind the F-testsSample Size11: Sample sizes: for one mean41: Power analysis and sample size determinationfor contingency table tests49: Power analysis and sample sizes for completelyrandomized designs with subsampling50: Power analysis and sample sizes for randomizedblock designs with subsamplingSampling11: Sample sizes: for one mean43: Standard error formulas for cluster sampling(unequal cluster sizes)44: What do we look for in a working plan?Sampling Units17: What is the design?SAS Programs1: Producing ASCI files with SAS9: Reading category variables with statisticssoftware15: Using SAS to obtain probability values for F-,t-, and χ 2 statistics29: Simple Regression: Confidence intervals for apredicted X-value33: Box plots35: The computation of tree shadow lengths36: Contingency tables and log-linear models41: Power analysis and sample size determinationfor contingency table tests43: Standard error formulas for cluster sampling(unequal cluster sizes)47: SAS: Adding observations when class variables(e.g. species list) are missing51: Programs for power analysis/sample sizecalculations for CR and RB designs withsubsampling59: ANOVA: Coefficients for contrasts and meansof incomplete factorial designs60: MANOVA: Profile Analysis – an exampleusing SASSAS: CATMOD7: Logistic regression analysis: model statementsin PROC CATMOD36: Contingency tables and log-linear modelsSAS: Data Step20: Rearranging raw data files using SAS24: Reading WATFILE file into SAS47: SAS: Adding observations when class variables(e.g. species list) are missing455
SAS: GLM3: ANOVA using SAS: specifying error terms4: ANOVA using SAS: How to pool error terms6: Using plot means for ANOVA32: Analysing a split-plot in time with the properrepeated measures ANOVA40: Finding the expected mean squares and theproper error terms with SAS45: Calculating contrast F-tests when SAS will not46: GLM Comparing regression lines57: Interpreting main effects when a two-wayinteraction is present59: ANOVA: Coefficients for contrasts and meansof incomplete factorial designs60: MANOVA: Profile Analysis – an exampleusing SASSAS: Graph38: Plotting error bars with SAS/Graph42: Labelling curves in SAS/GRAPHSAS: MIXED59: ANOVA: Coefficients for contrasts and meansof incomplete factorial designsSAS: NLIN39: A repeated measures example56: The use of indicator variables in non-linearregressionSAS: REG8: Standard errors for predicted values frommultiple regression18: Multiple regression: selecting the best subsetShadow Lengths35: The computation of Tree Shadow LengthsSplit-Plot Design6: Using plot means for ANOVA17: What is the design?32: Analysing a split-plot in time with the properrepeated measures ANOVA34: When are blocks pseudo-replicates?55: Displaying factor relationships in experimentsStandard Errors8: Standard errors for predicted values frommultiple regression11: Sample sizes: for one mean43: Standard error formulas for cluster sampling(unequal cluster sizes)58: On the presentation of statistical results: asynthesis59: ANOVA: Coefficients for contrasts and meansof incomplete factorial designsSYSTAT9: Reading category variables with statisticssoftwaret-distribution15: Using SAS to obtain probability values for F-,t-, and χ 2 statisticst-test16: ANOVA: Contrasts viewed as t-tests27: When the t-test and the F-test are equivalentTreatment Unit2: The importance of replication in analysis ofvariance5: Understanding replication and pseudoreplication17: What is the design?34: When are blocks pseudo-replicates?55: Displaying factor relationships in experimentsType I & II errors30: Interpretation of probability p-values37: A general description of hypothesis testing andpower analysisWATFILE24: Reading WATFILE file into SASWithin Sums of Squares25: ANOVA: The within sums of squares as anaverage variance28: Simple regression with replication: testing forlack of fit646
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