ANSC/TPSS 603 Experimental Design Laboratories

Last Updated: March 15, 2014

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, Second Midterm in Word, 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