ANSC/TPSS 603 Experimental Design Laboratories
Last Updated: December 5, 2012
Lab 1. Descriptive Statistics
Objectives:
1. To calculate descriptive statistics by hand
2. To calculate descriptive statistics using Excel
3. To import an Excel spreadsheet into SAS
4. To calculate descriptive statistics using SAS Analyst
Descriptive statistics to be calculated: mean, variance, standard deviation, standard error, range, confidence limits
Data sets used: prequiz data, Calculating machines, Assignment 1
Review of Statistics: Review of Statistics, Descriptive Statistics, Normal Distribution, Binomial Expansion, Segregation, Segregation with Correlated Variances and Means, Skewed Distributions, Exercise
Basic Methods in Excel: Introduction, Spreadsheets, Spreadsheet Math, Sort/Fill/Frequency, Data Conversion, Data Tables, Advanced Tools, Graphs, Advanced Graphics
Introduction to SAS for data analysis: SAS Intro, Data sets, SAS Tutorial
Lab 2. T and F tests, Completely Randomized Designs
Objectives:
1. To calculate t test and F test by hand
2. To calculate an ANOVA for a Completely Randomized Design on Excel
3. To calculate an ANOVA for a Completely Randomized Design using SAS Analyst
Data sets used: Calculating machines, Assignment 2
Tables and calculators: student t calculator, F Table, F Calculator
Completely Randomized Designs in Excel: ANOVA, CRD ANOVA, Template, Problems
ANOVA for Completely Randomized Designs in SAS: SAS ANOVA
Lab 3. Randomized Complete Block Designs, Crossover Designs, Number of Replicates
Objectives:
1. To calculate an ANOVA for a Randomized Complete Block Design on Excel
2. To calculate an ANOVA for a Randomized Complete Block Design using SAS Analyst
3. To calculate relative efficiency of RCBD compared to a CRD
4. To calculate an ANOVA for a Crossover Design in SAS
5. To calculate the number of replicates needed for a desired power and confidence level.
Data sets used: Calculating machines, Assignment 2, Assignment 3
Randomized Complete Block Designs: RCBDs, ANOVA for RCBD, Relative Efficiency, Error Variance, RCBD Template, Missing Values
SAS: SAS for RCBD and CO Designs
Spreadsheet for calculating number of replicates: NumRep
Lab 4. Latin Square Designs, Normal Equations
Objectives:
1. To calculate an ANOVA for a Latin square design on SAS
2. To calculate normal equations for treatment and block effects
Data sets used: Assignment 3, Sorghum
Latin Squares: Introduction, Latin squares
Latin Squares in SAS: SAS LS
Lab 5. Factorials, Contrasts and Mean Separation
Objectives:
1. To calculate factorial treatment effects in an ANOVA using SAS
2. To plot an interaction in SAS
3. To set up treatment contrasts and calculate SS
4. To enter contrasts in SAS
5. To run multiple range tests using SAS
Data sets used: Lambs, Assignment 5
Factorials: Introduction, Interactions, Factorial Treatments, Factorial in RCBD, Problems
Mean separation: Subdividing SS in CRD, Subdividing SS in RCBD, Unequal Replication, Sensitivity Analysis, Comparison Coefficients, Range tests in CRD
Factorials, contrasts and multiple range tests in SAS: SAS Factorials and Means
Lab 6. Split Plots and Strip Blocks
Objectives:
1. To calculate split plot experiments in SAS
2. To use the appropriate error term and hypothesis test in SAS
Data sets used: Phenylalanine, Sugar Beet SP
Split-Pots and Strip-Blocks: Introduction, Split Plot, Strip Block, Template, Problems
SAS: Split Plots
Lab 7. Split-split Plots: Combined Experiments
Objectives:
1. To calculate split split plot experiments in SAS
2. Set up ANOVA for repeated experiments
3. Analyze repeated measures in SAS
Data sets used: Sugar Beet SSP, Alfalfa, Corn, Assn6
Combined Experiments: Introduction, Combined Experiments, Repeated Measures, Bartlett’s Test, Multiple Harvest, Annual Crop, Years and Locations, Problems
Lab 8. Components of Variance; Sampling and Hierarchical Designs
Objectives:
1. Analyze mixed models in SAS
2. Identify components of variance and appropriate F tests
3. Analyze an experiment with subsampling in SAS
4. Determine the number of samples needed
5. Analyze a hierarchical design
Data Sets: Rice Panicles, Sampling Assignment, Oranges
Variance Components: Components of Variance
Sampling, Hierarchical Designs, CRD Example, CRD with Random Numbers, Sampling in RCBD, Analysis, Improving Precision, Template, Problems
Lab 9. Second Midterm Exam, Midterm 2 Data
Lab 10. Regression and Correlation
Objectives:
1. Analyze linear and quadratic regression in Excel
2. Analyze linear and quadratic regression in SAS
3. Analyze regression in replicated experiments using SAS linear models
4. Analyze curvilinear data
Data Set 1: Nitrogen
Instructions: Exercise 1, Output 1
Data Sets: Onions, San Diego, Lima Beans
Regression and Correlation, Regression, Residuals, Exponential Fit, Polynomial Fit,Sensitivity, P Values for Correlations
Lab 11. Covariance
Objectives:
1. Perform an analysis of covariance in SAS
2. Interpret covariance
Data Sets: Steers, Covariance Homework, Pigs
Lab 12. Multiple Regression
Objectives:
1. Analyze regression using SAS regression
2. Select the best model for a data set using stepwise
Data Set 1: Nitrogen
Instructions: N Regression
Homework Data:
N and P fertilization of corn, Instructions
Tree growth trial at Hamakuapoko, Maui, Instructions
Applied and soil Mo and Cu on Mo in D. Intortum, Instructions
Environment and initial macadamia nut set, Instructions
N and density effect on Chines taro, Instructions
Dressing percentage of beef, Instructions
Rib eye area (REA) of beef carcasses, Instructions
Food supplement effect on pig growth, Instructions
Ascorbic acid loss during storage of snap beans, Instructions
Progesterone concentration in cow blood, Instructions
Water quality of New York rivers, Instructions
Height of women in Hawaii, Instructions
Leg length of women in Hawaii, Instructions
Lab 13. Transformations
Objectives:
1. Create new variables in SAS
2. Transform and analyze data
3. Identify data that requires transformation
Data Set: Vitamins
Lab 14. Incomplete Block Designs
Objectives:
1. Analyze an incomplete block design
2. Anaylze an augmented design
DataSet: Corn Varieties, Tillers
Incomplete Block Designs: Introduction, Types, Balanced Lattice, Double Lattice, Triple Lattice, Double Lattice Example, Problem
Augmented Blocks: Augmented Designs, Augmented Blocks, Example
Other Designs: Fractional Factorials, Response Surfaces, Augmented Factorial
Lab 15. Designing Experiments
Objectives:
1. Plan an experimental design
2. Principal component analysis
Data set: Apples
Lab 16. Review, Final pdf, Final doc, Maize, Pigs