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


t and F tests

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

Crossover Designs

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


Split Split Plot

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

Sampling in Latin Square


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


Covariance, Problems


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