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        <title>w12.lecture.note - [residual errors and regression errors in Regression] </title>
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        <description>Regression

regression (회귀분석)
아래에서 다른 것은 이해하기 쉽지만 t.r 값과 t.b값의 (상관관계계수값과 (r), 리그레션의 계수값 (b&#039;s coefficient값)) significance level을 테스트할 때 쓰이는 se.res, se.b값을 구하는 것이 조금 복잡할 수 있다. 개념적으로</description>
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        <dc:creator>hkimscil (hkimscil@undisclosed.example.com)</dc:creator>
        <title>schedule - [Week 11:] </title>
        <link>http://www.commres.net/c/mrm/2026/schedule?rev=1779665638&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>data</title>
        <link>http://www.commres.net/r/data?rev=1779661699&amp;do=diff</link>
        <description>: Motivating example
Suppose you are a researcher studying the effects of student background on academic achievement. The lab recently finished collecting and uploading the dataset (worland5.csv) of students each with 9 observed variables: Motivation, Harmony, Stability, Negative Parental Psychology, SES, Verbal IQ, Reading, Arithmetic and Spelling. The principal investigator hypothesizes three latent constructs Adjustment, Risk, Achievement measured with its corresponding to the following codeb…</description>
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        <title>schedule - [Week12] </title>
        <link>http://www.commres.net/c/ms/2026/schedule?rev=1779658653&amp;do=diff</link>
        <description>통계에 대한 기초적인 이해
가설과 가설검증

	*  가설의 종류와 그 종류에 따른 통계분석법
		*  z-test
		*  t-test
		*  ANOVA
		*  Factorial ANOVA
		*  correlation
		*  regression
		*  multiple regression
		*  factor analysis</description>
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        <title>multiple_regression_lecture_note_for_r</title>
        <link>http://www.commres.net/c/ms/multiple_regression_lecture_note_for_r?rev=1779598440&amp;do=diff</link>
        <description>Multiple regression with pr, spr, zero-order r


# multiple regression: a simple e.g.
#
#
rm(list=ls())
d &lt;- read.csv(&quot;http://commres.net/wiki/_media/regression01-bankaccount.csv&quot;) 
d

colnames(d) &lt;- c(&quot;y&quot;, &quot;x1&quot;, &quot;x2&quot;)
d
# attach(d)
lm.y.x1 &lt;- lm(y ~ x1, data=d)
summary(lm.y.x1)

lm.y.x2 &lt;- lm(y ~ x2, data=d)
summary(lm.y.x2)

lm.y.x1x2 &lt;- lm(y ~ x1+x2, data=d)
summary(lm.y.x1x2)


lm.y.x1x2$coefficient
# y.hat = 6.399103 + (0.011841)*x1 + (−0.544727)*x2 
a &lt;- lm.y.x1x2$coefficient[1]
b1 &lt;- lm.y…</description>
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