Recipe 1: Example of Descriptive Statistics
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Recipes for the Design of Experiments: Recipe Outline
as of August 28, 2014, superceding the version of August 24. Always use the most recent version.
Heading
Name
School (RPI or ISMMS)
Date and Version
1. Setting
System under test
Choose one of the large datasets listed on the Realtime Board (e.g., babynames or nasaweather)
Make sure you have > 1000 data What is the problem that you were given?
Factors and Levels
??? ### Continuous variables (if any) ??? ### Response variables Define ### The Data: How is it organized and what does it look like? Describe ### Randomization Describe
# effectsize=c(0.005, 0.001, 0.01, 0.05, 0.10, 0.25, 0.50, 0.75, 1.0, 2.0, 2,5, 5.0)
m=50
n=50
sd=5
mu=180
# nu=175
nu=0.25 * sd + mu
#
x=rnorm(m,mu,sd)
y=rnorm(n,nu,sd)
t.test(x,y,var.equal=TRUE)##
## Two Sample t-test
##
## data: x and y
## t = -2.815, df = 98, p-value = 0.005891
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.7939 -0.8299
## sample estimates:
## mean of x mean of y
## 179.6 182.42. (Experimental) Design
How will the experiment be organized and conducted to test the hypothesis?
Describe ### What is the rationale for this design? Describe ### Randomize: What is the Randomization Scheme? Describe ### Replicate: Are there replicates and/or repeated measures? Answer ### Block: Did you use blocking in the design? Answer
3. (Statistical) Analysis
(Exploratory Data Analysis) Graphics and descriptive summary
You can also embed plots, for example:
hist(x,breaks=10,prob=TRUE,xlim=c(150,200))
lines(density(x),col="blue")
lines(density(y),col="red")
### Testing Describe
t.test(x,y,var.equal=TRUE)##
## Two Sample t-test
##
## data: x and y
## t = -2.815, df = 98, p-value = 0.005891
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.7939 -0.8299
## sample estimates:
## mean of x mean of y
## 179.6 182.4Estimation (of Parameters)
Describe
You will need a table of results here
Diagnostics/Model Adequacy Checking
Describe
# qqplot