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c:ma:2019:schedule [2019/10/04 09:02] – [Week05 (Oct 2, 4)] hkimscilc:ma:2019:schedule [2019/12/13 13:23] (current) – [Week16 (June 18, 20)] hkimscil
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 [[:r:probability]] [[:r:probability]]
 [[:r:General Statistics]] [[:r:General Statistics]]
 +
 +==== t.test: mtcars ====
 +<code>
 +> mdata <- split(mtcars$mpg, mtcars$am)
 +> mdata
 +$`0`
 + [1] 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
 +[13] 10.4 14.7 21.5 15.5 15.2 13.3 19.2
 +
 +$`1`
 + [1] 21.0 21.0 22.8 32.4 30.4 33.9 27.3 26.0 30.4 15.8 19.7 15.0
 +[13] 21.4
 +
 +> stack(mdata)
 +   values ind
 +1    21.4   0
 +2    18.7   0
 +3    18.1   0
 +4    14.3   0
 +5    24.4   0
 +6    22.8   0
 +7    19.2   0
 +8    17.8   0
 +9    16.4   0
 +10   17.3   0
 +11   15.2   0
 +12   10.4   0
 +13   10.4   0
 +14   14.7   0
 +15   21.5   0
 +16   15.5   0
 +17   15.2   0
 +18   13.3   0
 +19   19.2   0
 +20   21.0   1
 +21   21.0   1
 +22   22.8   1
 +23   32.4   1
 +24   30.4   1
 +25   33.9   1
 +26   27.3   1
 +27   26.0   1
 +28   30.4   1
 +29   15.8   1
 +30   19.7   1
 +31   15.0   1
 +32   21.4   1
 +> mdata
 +$`0`
 + [1] 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
 +[13] 10.4 14.7 21.5 15.5 15.2 13.3 19.2
 +
 +$`1`
 + [1] 21.0 21.0 22.8 32.4 30.4 33.9 27.3 26.0 30.4 15.8 19.7 15.0
 +[13] 21.4
 +
 +> t.test(mpg~am, data=mtcars)
 +
 + Welch Two Sample t-test
 +
 +data:  mpg by am
 +t = -3.7671, df = 18.332, p-value = 0.001374
 +alternative hypothesis: true difference in means is not equal to 0
 +95 percent confidence interval:
 + -11.280194  -3.209684
 +sample estimates:
 +mean in group 0 mean in group 1 
 +       17.14737        24.39231 
 +
 +> t.test(mpg~am, data=mtcars, var.equal=T)
 +
 + Two Sample t-test
 +
 +data:  mpg by am
 +t = -4.1061, df = 30, p-value = 0.000285
 +alternative hypothesis: true difference in means is not equal to 0
 +95 percent confidence interval:
 + -10.84837  -3.64151
 +sample estimates:
 +mean in group 0 mean in group 1 
 +       17.14737        24.39231 
 +
 +> m1 <- mdata[[1]]
 +> m2 <- mdata[[2]]
 +> m1
 + [1] 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
 +[13] 10.4 14.7 21.5 15.5 15.2 13.3 19.2
 +> m2
 + [1] 21.0 21.0 22.8 32.4 30.4 33.9 27.3 26.0 30.4 15.8 19.7 15.0
 +[13] 21.4
 +> m1.var <- var(m1)
 +> m2.var <- var(m2)
 +> m1.n <- length(m1)
 +> m2.n <- length(m2)
 +> m1.df <- length(m1)-1
 +> m2.df <- length(m2)-1
 +> m1.ss <- m1.var*m1.df
 +> m2.ss <- m2.var*m2.df
 +> m1.ss
 +[1] 264.5874
 +> m2.ss
 +[1] 456.3092
 +> m12.ss <- m1.ss+m2.ss
 +> m12.ss
 +[1] 720.8966
 +> m12.df <- m1.df+m2.df
 +> pv <- m12.ss/m12.df
 +> pv
 +[1] 24.02989
 +> pv/m1.n
 +[1] 1.264731
 +> pv/m2.n
 +[1] 1.848453
 +> m.se <- sqrt((pv/m1.n)+(pv/m2.n))
 +> m.se
 +[1] 1.764422
 +> m1.m <- mean(m1)
 +> m2.m <- mean(m2)
 +> m.tvalue <- (m1.m-m2.m)/m.se
 +> m.tvalue
 +[1] -4.106127
 +</code>
 +
 +<code>
 +> t.test(mpg~am, data=mtcars, var.equal=T)
 +
 + Two Sample t-test
 +
 +data:  mpg by am
 +t = -4.1061, df = 30, p-value = 0.000285
 +alternative hypothesis: true difference in means is not equal to 0
 +95 percent confidence interval:
 + -10.84837  -3.64151
 +sample estimates:
 +mean in group 0 mean in group 1 
 +       17.14737        24.39231 
 +
 +</code>
 +==== anova: mtcars ====
 +<code>
 +stats4each = function(x,y) {
 +   meani <- tapply(x,y,mean)
 +   vari <- tapply(x,y,var)
 +   ni <- tapply(x,y,length)
 +   dfi <- tapply(x,y,length)-1
 +   ssi <- tapply(x,y,var)*(tapply(x,y,length)-1)
 +   out <- rbind(meani,vari,ni,dfi,ssi)
 +   
 +   return(out)  
 +}
 +
 +library(MASS)
 +
 +tempd <- iris
 +x <- tempd$Species
 +y <- tempd$Sepal.Width
 +
 +tempd <- mtcars
 +x <- tempd$gear
 +y <- tempd$mpg
 +
 +tempd <- mtcars
 +x <- tempd$am
 +y <- tempd$mpg
 +
 +
 +x <- factor(x)
 +dfbetween <- nlevels(x)-1
 +
 +stats <- stats4each(y, x)
 +stats 
 +
 +sswithin <- sum(stats[5,])
 +sstotal <- var(y)*(length(y)-1)
 +ssbetween <- sstotal-sswithin
 +
 +round(sswithin,2)
 +round(ssbetween,2)
 +round(sstotal,2)
 +
 +dfwithin <- sum(stats[4,])
 +dftotal <- length(y)-1
 +
 +dfwithin
 +dfbetween
 +dftotal
 +
 +mswithin <- sswithin / dfwithin
 +msbetween <- ssbetween / dfbetween
 +mstotal <- sstotal / dftotal
 +
 +round(mswithin,2)
 +round(msbetween,2)
 +round(mstotal,2)
 +
 +fval <- round(msbetween/mswithin,2)
 +fval
 +siglevel <- pf(q=fval, df1=dfbetween, df2=dfwithin, lower.tail=FALSE)
 +siglevel
 +
 +mod <- aov(y~x, data=tempd)
 +summary(mod)
 +
 +</code>
 +
 +==== cor ====
 +<code>
 +attach(mtcars)
 +cor(mpg, hp)
 +
 +mycor <- cov(mpg,hp)/(sd(mpg)*sd(hp))
 +mycor
 +
 +sp <- cov(mpg,hp)*(length(mtcars$hp)-1)
 +ssx <- var(mpg)*(length(mtcars$mpg)-1)
 +ssy <- var(hp)*(length(mtcars$hp)-1)
 +
 +mycor2 <- sp/sqrt(ssx*ssy)
 +mycor2
 +
 +mycor2 == mycor
 +mycor == cor(mpg,hp)
 +mycor2 == cor(mpg,hp)
 +
 +</code>
 +
 +
  
 </WRAP> </WRAP>
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 <WRAP half column> <WRAP half column>
 ===== ideas and concepts  ===== ===== ideas and concepts  =====
 +[[:correlation]]
 +[[:regression]]
 +[[:multiple regression]]
 +  * [[:r:correlation|correlation in r]]
 +  * [[:r:multiple regression|multiple regression in r]]
 +
 +[[:Partial and semipartial correlation]]
 +[[:using dummy variables]]
 +
 +[[:Statistical Regression Methods]]
 +[[:Sequential Regression]]
 +
 +
 +  
 </WRAP> </WRAP>
 <WRAP half column> <WRAP half column>
 ===== Assignment ===== ===== Assignment =====
 +  - Public opinion in online environments ((refer to {{:public.opinion.theories.introduction.pdf}} ))
 +    * [[:Spiral of Silence]]
 +    * [[:Pluralistic Ignorance]]
 +    * [[:The Third Person Effect]]
 +    * etc. 여론형성과 관련된 사회학적 혹은 사회심리학적 이론을 찾아보고 소개하기, 예로 위의 세가지. 얼마전 사회현상을 어떻게 설명하면 좋을까에 대해서 논의정리하기? 정확한 온라인 환경에서의 여론파악을 위해서 어떤 것이 필요할까?
 +    * 혹은 다른 문제에 대해서 (. . . 조에 따른 . . .)
 +  - Hypotheses
 +    * Multiple regression hypotheses. 
 +    * Google Survey Questions
 + 
 +
 </WRAP> </WRAP>
  
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 <WRAP half column> <WRAP half column>
 __**Mid-term period**__  __**Mid-term period**__ 
 +
 +===== Quiz the first one =====
 +  * Lecture materials + textbook  
 +  * Textbook: r cookbook: textbook과 관련해서는 예상되는 아웃풋, 아웃풋을 얻기위한 명령어, 명령어(function)에 사용되는 옵션이 의미하는 것 등에 대한 사지선다 혹은 단답식 질문이 나옵니다. 펑션의 옵션사용 등과 같은 정확한 것에 대해서는 질문이 나오지 않습니다. 
 +    * 예
 +      * one sample t-test를 하기 위한 명령어를 쓰시오 (x)
 +      * t.test(sample, mu=100)에서 mu는 무엇을 의미하는가? (o)
 +      * 다음 중 sapply의 아웃풋 모양으로 적당한 것은? 등등 
 +    * [[:The r project for statistical computing]]
 +    * [[:r:Getting started]]
 +    * [[:r:Basics]]
 +    * [[:r:Navigating]]
 +    * [[:r:Input output]]
 +    * [[:r:Data structures]]
 +    * [[:r:Data transformations]]
 +  * Lecture content
 +    * [[:Hypothesis]], 
 +    * [[:Research question]], 
 +    * [[:Research methods lecture note#커뮤니케이션_연구문제_제기와_가설|커뮤니케이션 연구문제 제기와 가설]] 부분만
 +    * [[:Operationalization]],
 +    * [[:Variables]], 
 +    * [[:Types of variables]]
 +    * [[:Hypothesis testing]]
 +    * [[:T-test]]
 +      * 정확한 t test 공식등은 외울 필요가 없습니다. (제공됩니다). 
 +      * 간단한 t test 계산을 요구할 수 있습니다. 
 +      * ANOVA도 마찬가지입니다. 
 +    * [[:ANOVA]]
 +
 </WRAP> </WRAP>
  
Line 248: Line 529:
 <WRAP half column> <WRAP half column>
 ===== ideas and concepts  ===== ===== ideas and concepts  =====
 +[[:correlation]]
 +[[:regression]]
 +[[:multiple regression]]
 +  * [[:r:correlation|correlation in r]]
 +  * [[:r:multiple regression|multiple regression in r]]
 +
 +[[:Partial and semipartial correlation]]
 +[[:using dummy variables]]
 +
 +[[:Statistical Regression Methods]]
 +[[:Sequential Regression]]
 +
 +===== Activity =====
 +[[c/ma/2019/Multiple Regression Exercise]]
 +
 </WRAP> </WRAP>
 <WRAP half column> <WRAP half column>
Line 256: Line 552:
 <WRAP half column> <WRAP half column>
 ===== ideas and concepts  ===== ===== ideas and concepts  =====
 +[[:factor analysis]]
 </WRAP> </WRAP>
 <WRAP half column> <WRAP half column>
Line 275: Line 572:
 <WRAP half column> <WRAP half column>
 ===== Assignment ===== ===== Assignment =====
 +[[factor analysis assignment]]
 +
 </WRAP> </WRAP>
  
Line 280: Line 579:
 <WRAP half column> <WRAP half column>
 ===== ideas and concepts  ===== ===== ideas and concepts  =====
 +[[:social network analysis]]
 +[[:r:social network analysis tutorial]]
 +[[:r:social network analysis|sna in r]] 
 +[[:sna_eg_stanford|Stanford University egs.]]
 </WRAP> </WRAP>
 <WRAP half column> <WRAP half column>
 +===== announcement  =====
 +Quiz 2 (on Friday Dec. the 6th) covers:
 +  * [[:correlation]]
 +  * [[:regression]]
 +  * [[:multiple regression]]
 +    * [[:partial and semipartial correlation]]
 +    * [[:using dummy variables]]
 +  * [[:factor analysis]]
 +Some R outputs will be used to ask the related concepts and ideas (the above). 
 +
 ===== Assignment ===== ===== Assignment =====
 </WRAP> </WRAP>
Line 292: Line 605:
 ====== Week15 (Dec 11, 13) ====== ====== Week15 (Dec 11, 13) ======
 <WRAP half column> <WRAP half column>
-Group Presentation+ 
 +[[./assignment week15]]
 </WRAP> </WRAP>
  
 ====== Week16 (June 18, 20) ====== ====== Week16 (June 18, 20) ======
 <WRAP half column> <WRAP half column>
-__**Final-term**__+__**Final-term**__ covers: 
 +correlation 
 +regression 
 +multiple regression 
 +partial and semipartial correlation 
 +using dummy variables 
 +factor analysis 
 +[[:social network analysis]] 
 +[[:r:social network analysis tutorial|sna tutorial]] 
 +[[:r:social network analysis|sna in r]]  
 +[[:sna_eg_stanford:lab06|SNA e.g. lab 06]] 
 + 
 +Some R outputs will be used to ask the related concepts and ideas (the above). 
 </WRAP> </WRAP>
  
c/ma/2019/schedule.1570147354.txt.gz · Last modified: 2019/10/04 09:02 by hkimscil

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