Documents about Randomized Complete Block Design
RCBD_SPSS_setup
Iowa State, STAT 402
Excerpt: ... SPSS set up for the Analysis of a Randomized Complete Block Design ...
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Disc2sol
UC Davis, STATS 106
Excerpt: ... Discussion #1(solution) 15.3 12 experimental units 1,., 12. Each 3 of them were randomly assigned to each treatment (T1, T2, T3, T4). e.g T1 : 1,5,7; T2: 3,6,10; T3: 4,8,9; T4:1,11,12 15.8 a. Mixed experimental and observation study b. Factor, facto ...
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lecture10-1
Uni. Worcester, MA 590
Excerpt: ... Lecture 10 Administrative 1. Lab 2 due today MA 2612 - Applied Statistics II D 2004 2. Quiz 3 tomorrow (Chapter 9) Today 1. Review RCBD from Tuesday 2. An example of the analysis of data from an RCBD The Randomized Complete Block Design To this point in our discussion of comparing the means of more than two populations, we have assumed a completely randomized design (CRD). This is an experimental design in which treatments are assigned to experimental units completely at random. In this design, every experimental unit has an equal chance to receive any one of the treatments. ized complete block design (RCBD). This is an experimental design in which the experimental units are divided into blocks and, separately within each block, treatments are assigned at random to the experimental units within that block. In this design, every experimental unit within the same block has an equal chance to receive any one of the treatments. In MA 2611 (PNC Chapter 3), you also learned about the random- 2 What i ...
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ch14
Texas A&M, STAT 652
Excerpt: ... rom one of the treatment populations Replication: once the treatment is assigned to an experimental unit, a single replication of the treatment has occurred Measurement unit: the physical entity on which a measurement is taken Definitions Experimental error: the variation in the responses among experimental units, which are assigned the same treatment and are observed under the "same" experimental conditions Controlling Experimental Error Experimental procedures Selecting experimental and measurement units Reducing the variance of experimental error through blocking Using covariates to reduce variability Randomization of Treatments to Experimental Units Randomized complete block design : experimental units are not homogeneous. They are placed in groups where units within a group are more alike than units in different groups. Completely randomized designs Randomization Procedure for a Randomized Complete Block Design 1. Group the experimental unit ...
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Lect17
Penn State, STAT 460
Excerpt: ... response variable. Other names for experimental / observational units are "subjects," "participants," "cases," "plots," and "guinea pigs." Assigning Units to Treatments We use random assignment in order to make valid causal inferences about effects. In a completely randomized design, all factors are assigned randomly. In a randomized block design, one of the factors is not assigned randomly but represents preexisting "blocks" of units. The others are assigned randomly within each "block." Completely Randomized Two-Factor Design All treatments are randomized in the same way Randomized Complete Block Design Each block is randomized separately Blocking Group similar subjects into "blocks" and randomized treatment applications into those. A blocking factor is one which accounts for some variability Eg. Age, gender, location, apparatus, etc. It is included in the model to make the ANOVA work better. Completely Randomized Design Plots are randomly assigned, independent of each other ...
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Paper
Iowa State, STAT 402
Excerpt: ... trength Conditions: Factor 1: Method A, B, and C (whole plot day) Factor 2: Temperature 200, 225, 250, and 275 (sub plot samples of batches) Nuisance Factor: Day (Block) Experimental material: Batch of pulp Note: All three methods are assigned at random within a day so the whole plot experiment is a randomized complete block design . All four temperatures are assigned at random to a batch that has been subdivided so the sub plot experiment is a randomized complete block design . Data: Day 1 B 34 41 38 42 Day 2 B 31 36 42 40 Day 3 B 35 40 39 44 Method Temperature: 200 225 250 275 A 30 35 37 36 C 29 26 33 36 A 28 32 40 41 C 31 30 32 40 A 31 37 41 40 C 32 34 39 45 * From Montgomery (1991), Design and Analysis of Experiments, 3rd Edition, pg. 468. 1 45 45 40 Strength LS Means 35 30 25 200 225 250 275 40 Strength LS Means 35 30 25 A B Method C Temperature 45 A B C 40 Strength LS Means 35 30 25 200 225 250 275 Temperature 2 ...
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analysispyramid
Ohio State, STAT 528
Excerpt: ... Philosophy of Applied Statistics Creating Information Forming a Rational Basis for Action Ecological Fallacy Exponential Explosion Factor Testing versus Estimation Confounding Orthogonality Interaction Central Limit Theorem Gauss Markov Theorem General Linear Model Theory Empirical Rule 3 Sigma Limits Tchebyshev's Inequality Theory of Rational Subgrouping Statistical Theory for Constant Cause Systems of Variation Shewhart Theory of Chance Cause Systems of Variation Sampling Theory Probability Theory Taguchi Approach Nested Designs Split Plot Designs Fractional Factorial Designs Latin Square Designs Incomplete Block Designs Randomized Complete Block Design s Completely Randomized Designs Simple Simulation Power Calculations Estimation Error Sample Size Analysis Multiple Comparisons Contrasts Analysis of Variance Multiple IndicatorVariables Logistic Correlation Independence Regression General Linear Models Analysis of Means Statistical Concepts Statistical Theory Design of Experiments Statistical Methods ...
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425
University of Illinois, Urbana Champaign, STAT 425
Excerpt: ... Course Schedule - Spring 2009 Statistics 425 Applied Regression and Design credit: 3 or 4 hours. Explores linear regression, least squares estimates, F-tests, analysis of residuals, regression diagnostics, transformations, model building, factorial designs, randomized complete block design s, Latin squares, split plot designs. Computer work is an integral part of the course. 3 undergraduate hours. 4 graduate hours. Prerequisite: STAT 410. CRN 48340 Type lecture Section D1G Time 09:00 AM - 09:50 AM Days MWF Location room 218 Mechanical Engineering Bldg Instructor Douglas, J 48340: 4 hours 50354 lecture D1U 09:00 AM - 09:50 AM MWF room 218 Mechanical Engineering Bldg Douglas, J 50354: 3 hours Page 1 - Statistics, Spring 2009 ...
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short
Maryville MO, KOESTERE 120908
Excerpt: ... PROPERTIES OF EXTRUDED WHITE CORN FLOUR HIGH AMYLOSE CORN STARCH PUFFS Elizabeth Koester Fu-hung Hsieh, Thesis Supervisor ABSTRACT This study was conducted to determine the effect of high-amylose corn starch in corn puffs on the extrusion parameters, product and textural properties and the glass transition temperature. The data was analyzed in a randomized complete block design (RCBD) in which the treatments were arranged in a 4 x 3 x 3 {high-amylose content (0, 20, 40 and 60%) x moisture content (20, 22, and 18 or 24%) x extruder screw speed (200, 300 and 400 rpm)} factorial arrangement of treatments with two replicants. The collected results indicate that the maximum amount of expansion occurs when the screw speed and moisture content are both decreased and the high-amylose corn starch content is increased. The puff density increased and the specific volume decreased when screw speed and starch content decreased and moisture content increased. While the original puff breaking strength and hardness were ...
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rcbd
Los Angeles Southwest College, STAT 706
Excerpt: ... Review of Randomized Complete Block Design s Respond to the following questions individually then discuss your answers as a group. You should hand in your individual response. We will discuss your group responses and then I will lecture on other topics. 1. At a local farm, USGS soil maps identified 5 different soil types. Within each irregularlyshaped plot defined by a single soil type, 6 different fertilizer/watering regimens were applied. What is the block? 2. Suppose you had an experiment with 5 blocks and 4 treatments. Explain how to randomize treatments for a RCBD. 3. Construct and run SAS code to analyze the following RCBD data. Is the test on Block significant? In general, why might a test on Block be inappropriate? Block 2 3 8.9 9.2 8.4 9.6 8.0 8.9 7.3 9.0 9.3 11.3 Treatment A B C D E 1 8.7 8.8 7.4 7.9 10.2 4 8.3 8.3 7.9 7.6 9.5 1 ...
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post29
Purdue, STAT 503
Excerpt: ... Stat 503 Lecture 29 Wed. Nov. 1, 2006 8.4 Randomized Complete Blocks Design Tries to control inherent variation in subject pool by ensuring that each treatment group is somewhat similar. First, the subject pool is divided into blocks. What are blo ...
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INGunter2001SeededWM
Purdue, HORT 2001
Excerpt: ... Seeded Watermelon Cultivar Trials for Southwestern Indiana, 2001 Christopher C. Gunter1*, Melborn K. Lang2, Dennis Nowaskie2, Angie Thompson2 1 Horticulture Specialist at the Southwest Purdue Agricultural Program, Vincennes, IN 47591 2 Southwest Purdue Agricultural Center, Vincennes, IN 47591 Indiana remains a major watermelon producer for the Midwest. With the proliferation of new varieties, the increased competition and the need to maximize profitability/unit area, the identification of new varieties that are of high quality, high yielding and disease resistant as well as meet market expectations, is of importance to commercial growers. This trial, along with the seedless watermelon variety trial provides an objective and independent comparative assessment of new watermelons for the commercial industry. This years study included 12 seeded watermelons, with 8 named varieties, and 4 experimental lines. Methods: Twelve seeded melon cultivars were evaluated in a randomized complete block design with three r ...
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INGunter2001SeedlessWM
Purdue, HORT 2001
Excerpt: ... planted on May 15 into a randomized complete block design with three replications. Stars-N-Stripes was used a as pollinator and planted in every third row and in the guard rows. Plots were single rows 55 ft. long, centered eight ft. apart, and covered with 4 ft. black plastic mulch. Each plot had 11 plants five feet apart. The recommendations in the Midwest Vegetable Production Guide for Commercial Growers (ID-56, 2001) were followed for fertilization, weed, disease and insect control. Plots were harvested on July 31, August 7, and 14. The data was analyzed using the Statistical Analysis Software (SAS) package (SAS Institute, Cary, NC). Trickle irrigation was used as necessary to provide ample water to the field plots. Results and Conclusions: Yields and Quality. Yields ranged from 31 to 46 tons/acre with 3102 to 4554 fruit/acre harvested across all entries (Table 1). The average weight of seedless fruit was up this year to 20.5 lbs/fruit with a range of 16.8 to 24.0 lbs/fruit. Smaller weight per fruit led to ...
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IET3403_COURSECONTENTS
SPSU, IET 3403
Excerpt: ... Hypothesis Testing Inferences from Two Samples Nonparametric Statistics Linear Regression a) b) c) d) a) b) c) d) a) b) c) d) a) b) Topic Details Estimating a Population Proportion Estimating a Population Mean: Known Estimating a Population Mean: Not Known Estimating a Population Variance Testing a Claim about a Proportion Testing a Claim about a Mean: Known Testing a Claim about a Mean: Not Known Testing a Claim about Variation Inferences about Two Proportions Inferences about Two Means Inferences from Matched Pairs Comparing Variation in Two Samples Wilcoxon Signed-Ranks Test Wilcoxon Rank-Sum Test Lecture Hours 4 Textbook Chapter / Section 5.1-5.3 Computer Applications MS Excel 1 2 5 6.1-6.4 6.10-6.11 5.4-5.7 6.5-6.8 6.9 7.1-7.4 8.1-8.3 MS Excel 3 4 5 5 4 8 MS Excel MS Excel a) Simple Linear Regression Models b) Multiple Linear Regression Models a) b) c) d) One-Factor Experiments Two-Factor Experiments Randomized Complete Block Design 2p Factorial Experiments 6 Factorial Experiments / Analy ...
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LearningobjectivesforMid1ofStat370
N.C. State, ST 370
Excerpt: ... designed experiment, identify the treatments, response variables, and experimental units C2. List reasons for variability of responses (treatment effect, experimental error) C3. List sources of experimental error C4. Explain what it would mean to control for a variable C5. Explain why we would want to control for a variable C6. List two ways to deal with experimental error that may remain after controlling variables (randomization, blocking) C7. Define replication C8. Give reasons for replication in designed experiments C9. Define randomization C10. Explain why one wants to randomize in a designed experiment C11. Define a block (homogenous subset of experimental units) C12. Explain why one would want to block a subset of experimental units C13. Describe two ways to randomize (completely randomized design, randomized complete block design ) C14. Given a situation, describe how to set up a randomized complete block design C15. Given a situation, explain how to set up a completely randomized design C16. G ...
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lecture22
Texas Tech, ANSC 5403
Excerpt: ... AnSc 5403 Biometry Lecture Notes 22 I. The randomized complete block design Two-way classification A. So far, our study of the AOV has involved the simplest of experimental designs, the completely randomized or completely random design (CRD) 1. The only complexity we have introduced at this point is the factorial arrangement of treatments within the CRD B. What if we use a CRD and we find that the variation among experimental units is so large relative to the treatment effects that we are unable to detect a significant difference with the F test? 1. Consider the following data for average daily gain (ADG) by 12 pens of cattle fed three treatment diets: Trt 1 3.4 3.59 3.65 3.85 x = 3.6225 Trt 2 3.32 3.49 3.52 3.7 x = 3.5075 Trt 3 3.25 3.42 3.55 3.67 x = 3.4725 2. If we do the AOV for this CRD, we would have the following results: Source Total Treatment Residual df 11 2 9 SS MS F P-value 0.322292 0.049267 0.024633 0.812014 0.474013 0.273025 0.030336 - a. Thus, we would conclude from this AOV that treat ...
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finalexamtopics
University of Florida , STA 4211
Excerpt: ... What is the purpose of the Tukey Test for Additivity? What does the Tukey model assume? Why are blocks used in a study? What is the model used for analyzing the data from a randomized complete block design and how is the analysis performed? What is the purpose of Analysis of Covariance? What is a covariate and what are the restrictions that we place on them? What is the ANCOVA model and how are appropriate inferences carried out? How is a two factor study analyzed when the sample sizes are not equal? How is the model constructed? What are type I and type III sums of squares in SAS? What is the structure of a three factor factorial design? What are higher order interactions? In what sequence are the terms tested to produce the most appropriate model? What is a random factor? How do the models change and the analyses change when you have random factors, fixed factors or mixed models? What is the structure for a randomized complete block design where blocks are a random factor? What analyses are ...
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rcbd
Wisconsin, STAT 850
Excerpt: ... Statistics 850 Clayton February 19, 2004 Randomized Complete Block Design Model Yij = + i + j + where i = 1, . . . , k j = 1, . . . , b eij N (0, 2 ) ij indexes treatment indexes block corresponds to plot error ANOVA Table Source Blocks Treatment Error Total df b-1 k-1 (k - 1)(b - 1) kb - 1 SS k j (Yj - Y )2 i - Y )2 b i (Y 2 j (Yij - Yi - Yj + Y ) 2 (Yij - Y ) i j i MS MSBlk MSTrt MSErr E(MS) 2 + k 2 + b 2 2 j /(b - 1) 2 i i /(k - 1) j Notes: The word "complete" in the name " randomized complete block design " means that every treatment appears at least once in each block. This is different from the meaning in "completely randomized design" where the word "completely" refers to the fact that the design is randomized as much as possible, subject to the constraint that each treatment must show up a prescribed number of times. In proc glm we fit this model with a term like model y = trt blk; . RCB with Subsampling Model Yijl = + i + j + where i = 1, . . . , k j = 1, ...
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anovarcb
Utah State, STAT 3000
Excerpt: ... # # The following R code and comments are for Randomized Complete Block Design ANOVA in R # # Download the dataset called 'wheat.csv' from the course website. # In R, change to your preferred working directory and execute the following commands: wheat.df=read.csv("wheat.csv") wheat.df summary(aov(yield~field+type,data=wheat.df) # # Notice that the above commands reads in the data, views it, and then performs the ANOVA RCB analysis and # reports an F-table on the results. In this case, the blocking variable was the field and the factor was # the type of treatment applied. Tests have fairly large p-values suggesting that neither field nor treatment # have an effect on the crop yield. See chapter 11.2 in the textbook for additional details (specifically, # pages 517 and 522 for the data and hypothesis testing protocol, respectively. # # # Note that you can also easily construct several plots using R (similar to fig. 11.27 in the text): # matplot(t(matrix(w ...
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MATH 226 - 8.29.2007
Bucknell, MATH 226
Excerpt: ... y Dipping Spray Dipping Spray Dipping Spray Dipping Spray Dipping Spray Dipping Primer Primer 1 Primer 1 Primer 2 Primer 2 Primer 3 Primer 3 Primer 1 Primer 1 Primer 2 Primer 2 Primer 3 Primer 3 Primer 1 Primer 1 Primer 2 Primer 2 Primer 3 Primer 3 Pieces Rand() .9435 .9083 .1466 .5147 .4058 .7338 .0439 .3393 .9954 .2003 .7980 .9518 .2209 .3694 .0078 .9351 .1080 .0062 Pieces Rand(18) 10 16 18 6 5 3 1 14 8 12 4 17 13 2 7 11 9 15 Order 3 15 7 6 1 2 16 4 11 9 13 8 14 12 5 17 10 18 1,w 1,d 2,w 2,d 3,w 3,d 4,w 4,d 1 1 5 3 2 8 7 6 4 o Randomized Complete Block Design (RCBD) Block on one of the variables Position Dependent randomized each block of the field 2 3 4 5 6 ...
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765-522
Rutgers, 765 522
Excerpt: ... 16:765:522 Applied Plant Science Statistics (3 credits) Normally Offered: Fall of odd-numbered years Instructor: Dr. Durner Pre-requisites and other registration restrictions: None Format: Two 80-minute lectures Description: Statistical analysis for plant science research using the SAS system. Topics 1. Introduction & Review of the basics a. means, variances, the normal distribution, etc. b. t-tests, confidence intervals and tests of hypothesis. c. F distributions, associated tests and ANOVAS. Elements of Experimentation SAS - An Overview Field Plot Techniques. a. basic experimental design principles b. replication, randomization, & local control c. choice of design. Single Factor Experiments a. Completely Random Design b. Randomized Complete Block Design c. Latin Square Two Factor Experiments a. Interactions b. Factorial Experiments c. Complete Block Design d. Split Plot e. Strip Plot 7. Three or more factor experiments a. Split split plots b. Strip split plots c. Alternatives 8. Estimation of treatment ...
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STA 6166 Fall 2006 Tentative Schedule
University of Florida , STA 6166
Excerpt: ... Tentative Schedule STA 6166 Fall 06: Lectures Topics 1-2 3-5 6-7 8-10 11-13 14-16 17-18 19-20 22-23 24-26 27-28 29-31 32-34 35-36 37-39 40 Introduction, Data Collection/Summaries, Populations/Samples Probability, Random Variables, Graphical Representation Sampling and Sampling Distributions, Estimating a Mean Statistical Test for a Mean Comparing Two Population Means and Medians Introduction to F, 2 Distributions, Inference on Variances Introduction to Analysis of Variance and Experimental Design 1-Way ANOVA: Assumptions, Rank-Based Tests, Post-hoc tests Randomized Complete Block Design Latin Square Design, 2-Factor ANOVA Categorical Data Analysis: Estimating and Comparing Proportions Contingency Tables, 2-Tests, Odds Ratios Introduction to Linear Regression Correlation and ANOVA intro to Multiple Regression Multiple Linear Regression Logistic Regression Sections 1.1-3.9 4.1-4.10 4.11-4.13,5.1-5.3 5.4-5.7 6.1-6.6 7.1-7.4 8.1-8.3 8.4-8.6 9.1-9.2 9.3-9.6 10.1-10.3 10.5-10.6 11.1-11.5 11.7, 12.1-12.2 12.1-12.5 1 ...
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Chpt 7 Analysis of Variance for a Randomized Bl...
Alabama, ST 610
Excerpt: ... Chapter 7 Analysis of Variance for a Randomized Block Design 7.1 7.2 7.3 7.4 7.5 ANOVA with Fixed Effects . 7-3 ANOVA with Random Effects. 7-8 Does it Matter: Fixed or Mixed? . 7-19 Exercises. 7-24 Chapter Summary . 7-27 7-2 Chapter 7 Analysis of Variance for a Randomized Block Design 7.1 ANOVA with Fixed Effects 7-3 7.1 ANOVA with Fixed Effects Objectives Define blocking effects. Define a randomized complete block design . Analyze a randomized complete block design using the GLM procedur ...
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