Publications by Laboratory Exercise No. 5
Stat 141 Split-Plot Experiments
Basic ideas a two-factor experiment wherein levels of one of the factors require large plot size for execution and also show large differences in their effects the experiment will consist of a set of large plots called main plots in which levels for the main plot factor are assigned each main plot is divided into subplots to which the second ...
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Stat 141 Experiments with Nested Factors
Nested (hierarchical) factors Recall that in a factorial experiment, the levels of factor A are “crossed” with the levels of factor B In some two-factor experiments the level of one factor , say B, is not “cross” or “cross classified” with the other factor, say A, but is “NESTED” with it. Nesting may occur in the treatment struc...
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Stat 141 Key Answer Lab Exercise 4
Problem No. 1 An experiment station conducted a randomized complete block experiment in order to make comparisons among three varieties of sweet potato. Six blocks were used. The yields (kg) are shown in the following table. Test if there exist significant differences in the mean yield among the three varieties. Test if blocking is effective....
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Stat 141 Post hoc test for significant interaction
Remarks Recall that when the interaction effect is significant, the effect of one factor is dependent on the other factor When the interaction effect is significant, the main effects are no longer sensible Thus, in the presence of significant interaction effect, we do post hoc test of the levels of one factor at each (fixed) level of the other...
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Stat 141 Factorial ANOVA Using R
Two-factor factorial ANOVA (Fixed Model) fact2.crd <- read.csv("twofactorial.crd.csv") head(fact2.crd) ## Pesticide Variety Yield ## 1 P1 V1 49 ## 2 P1 V1 39 ## 3 P1 V2 55 ## 4 P1 V2 41 ## 5 P1 V3 66 ## 6 P1 V3 68 out1 <- aov(Yield ~ Pesticide*Variety...
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Stat 141 Expected Mean Squares
Steps in Deriving Expected Mean Squares Prepare a two way table with the terms of the model as the row labels. Write the subscripts in the model as column headings. Over each subscript, write the number of levels of the factors associated with that subscript and whether the factor is fixed (F) or random (R). Replicates are always treated as ra...
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Stat 141 Analysis of RBD Experiments Using R
Loading required packages library(agricolae) library(ExpDes) ## ## Attaching package: 'ExpDes' ## The following objects are masked from 'package:agricolae': ## ## lastC, order.group, tapply.stat Reading and preparing the data #RCBD without sub-sample rcbd <- read.csv("rcbd.csv") rcbd$Trt <- factor(rcbd$TRT) rcbd$Block <- factor(rcbd$...
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Stat 141 Multiple Comparisons Using R
Import data chap4demo <- read_excel("chap4demo.xlsx") chap4demo$Trt <- factor(chap4demo$fertilizer) head(chap4demo) ## # A tibble: 6 × 4 ## trt fertilizer yield Trt ## <chr> <dbl> <dbl> <fct> ## 1 T1 0 4.89 0 ## 2 T1 0 4.79 0 ## 3 T1 0 4.65 0 ## 4 T1 0 4.47 0 ...
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ANSWER KEY Lab Exercise No. 1 (Stat 141)
Problem No. 1 For the given experiment, identify the following: factor(s): drug levels of each factor(s): \(D_1, D_2\) treatments: \(D_1, D_2\) blocks: hospitals appropriate linear model (fixed, random or mixed): fixed treatment structure (one-way or two-way): one-way design structure (CRD, RCBD, LSD): RCBD experimental units: wards measurement...
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Data Visualization using ggplot2
Plotting One Variable Introduction To visualize one variable, the type of graphs to be used depends on the type of the variable: For categorical variable or grouping variables. You can visualize the count of categories using a bar plot or using a pie chart to show the proportion of each category. Dot charts can be another alternative. For con...
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