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        <dc:creator>hkimscil (hkimscil@undisclosed.example.com)</dc:creator>
        <title>note</title>
        <link>http://commres.net/c/mrm/2026/schedule/week_09/note?rev=1777515590&amp;do=diff</link>
        <description>Statistical Test

	*  DV: 숫자
		*  IV: 종류 한개일 경우
			*  특성이 두가지: (4 종류의) t-test t.test()
			*  특성이 두가지 이상: F-test (anova) aov()

		*  IVs: 종류이면서 2개
			*  factorial anova aov()

		*  IV: 숫자 (종류)
			*</description>
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        <title>schedule - [Week 9:] </title>
        <link>http://commres.net/c/mrm/2026/schedule?rev=1777512063&amp;do=diff</link>
        <description>이전 페이지

---- exploring media theories

	*  Week 01: March 2, 5
	*  Week 02: March 9, 12
	*  Week 03: March 16, 19
	*  Week 04: March 23, 26 (the 1st quiz)

---- choosing media theories

	*  Week 05: March 30, April 2 
	*  Week 06: April 6, 9 

---- developing and revising hypotheses</description>
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        <title>w07_factorial_anova_note</title>
        <link>http://commres.net/c/ms/2026/w07_factorial_anova_note?rev=1777420369&amp;do=diff</link>
        <description>see factorial anova

	*  
	*  

sa factorial anova

see repeated measure anova
sa repeated measure anova</description>
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        <title>factorial_anova - [output interpretation] </title>
        <link>http://commres.net/r/factorial_anova?rev=1777383149&amp;do=diff</link>
        <description>Two-way ANOVA

Factorial ANOVA 라고도 부른다.


rm(list=ls(all=TRUE)) 

#################################################
# two-way anova
# subject = factor(paste(&#039;sub&#039;, 1:30, sep=&#039;&#039;))
#################################################

n.a.group &lt;- 3 # a treatment 숫자 e.g: drug a1, a2, a3
n.b.group &lt;- 2 # b 그룹 숫자 e.g.: exercise no(b1), yes(b2)
n.sub &lt;- 30 # 총 샘플 숫자
n.sub/n.a.group

# 데이터 생성
set.seed(9)
a &lt;- gl(n.a.group, 
        n.sub/n.a.group, 
        n.sub, 
        labels=c(&#039;drg1&#039;, &#039;drg2&#039;, &#039;drg3&#039;…</description>
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        <title>interaction_effects_in_regression_analysis</title>
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        <description>interaction effects in regression analysis

	*  Interpreting interaction coefficient in R (Part1 lm) UPDATED
	*  How can I explain a continuous by continuous interaction?
	*  Interaction effects between continuous variables PDF
	*  see also ANCOVA

E.g. 1 One category and one continuous

Data 만들기 


x&lt;-runif(50,0,10)
f1&lt;-gl(n=2,k=25,labels=c(&quot;Low&quot;,&quot;High&quot;))
modmat&lt;-model.matrix(~x*f1,data.frame(f1=f1,x=x))
coeff&lt;-c(1,3,-2,1.5)
y&lt;-rnorm(n=50,mean=modmat%*%coeff,sd=0.5)
dat&lt;-data.frame(y=y,f1=f1,x=…</description>
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        <dc:date>2026-04-27T04:51:58+00:00</dc:date>
        <dc:creator>hkimscil (hkimscil@undisclosed.example.com)</dc:creator>
        <title>repeated_measures_anova - [exer1] </title>
        <link>http://commres.net/r/repeated_measures_anova?rev=1777265518&amp;do=diff</link>
        <description>Repeated measures ANOVA

e.g. 1


demo1  &lt;- read.csv(&quot;https://stats.idre.ucla.edu/stat/data/demo1.csv&quot;)
## Convert variables to factor
demo1 &lt;- within(demo1, {
  group &lt;- factor(group)
  time &lt;- factor(time)
  id &lt;- factor(id)
})

demo1

par(cex = .6)

with(demo1, interaction.plot(time, group, pulse,
  ylim = c(5, 20), lty= c(1, 12), lwd = 3,
  ylab = &quot;mean of pulse&quot;, xlab = &quot;time&quot;, trace.label = &quot;group&quot;))

demo1.aov &lt;- aov(pulse ~ group * time + Error(id), data = demo1)
summary(demo1.aov)</description>
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        <dc:creator>hkimscil (hkimscil@undisclosed.example.com)</dc:creator>
        <title>repeated_measure_anova - [demo 2] </title>
        <link>http://commres.net/repeated_measure_anova?rev=1777264374&amp;do=diff</link>
        <description>See also, ANOVA, Factorial ANOVA, paired sample t-test repeated_measures_anova

Repeated Measure ANOVA

Introduction

	*  one-way ANOVA for related, not-independent groups
	*  extension of the dependent t-test (one group t-test, repeated measure t-test)
	*  also, it is called “within-subjects ANOVA” or “ANOVA for correlated samples$\text{independent ANOVA: } F = \displaystyle \frac{MS_{between}}{MS_{within}} = \frac{MS_{between}}{MS_{error}}$$\text{rep measures ANOVA: } F = \displaystyle \frac{M…</description>
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