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--- c:ms:2020:schedule [2020/06/26 15:37] – [Week16 (June 22, 25)] hkimscil
+++ c:ms:2020:schedule [2020/06/27 13:29] – hkimscil
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 ===== Concepts and ideas =====
 <WRAP box>
 Some [[:b:r cookbook:basics|basics]]
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 ===== Concepts and ideas =====
-Do the following
-<code>S1 <- c(89, 85, 85, 86, 88, 89, 86, 82, 96, 85, 93, 91,
-, 87, 94, 77, 87, 98, 85, 89, 95, 85, 93, 93,
-, 71, 97, 93, 75, 68, 98, 95, 79, 94, 98, 95)
-S2 <- c(60, 98, 94, 95, 99, 97, 100, 73, 93, 91, 98,
-, 66, 83, 77, 97, 91, 93, 71, 91, 95, 100,
-, 96, 91, 76, 100, 97, 99, 95, 97, 77, 94,
-, 88, 100, 94, 93, 86)
-S3 <- c(95, 86, 90, 90, 75, 83, 96, 85, 83, 84, 81, 98,
-, 94, 84, 89, 93, 99, 91, 77, 95, 90, 91, 87,
-, 76, 99, 99, 97, 97, 97, 77, 93, 96, 90, 87,
-, 88)
-S4 <- c(67, 93, 63, 83, 87, 97, 96, 92, 93, 96, 87, 90,
-, 90, 82, 91, 85, 93, 83, 90, 87, 99, 94, 88,
-, 72, 81, 93, 93, 94, 97, 89, 96, 95, 82, 97)
-scores <- list(S1=S1,S2=S2,S3=S3,S4=S4)</code>
-  * find means for each element in "scores" in a list format
-  * find standard deviation for each element in "scores" in a data frame format
-  * find variance  for each element in "scores" in a data frame format without using "var" function
-<code>longdata<- c(-1.850152, -1.406571, -1.0104817, -3.7170704,
-           -0.2804896, 0.9496313, 1.346517, -0.1580926, 1.6272786,
-           -2.4483321, -0.5407272, -1.708678, -0.3480616, -0.2757667,
-           -1.2177024)</code>
-  * make "longdata" to a matrix whose size is  3 by 5
-  * name columns "trial1, trial2, . . . . trial5"
-  * name rows "subject1, subject2, subject3"
-  * get means for each subject
-  * attach the above data to the matrix data and name it "longtemp."
-  * get standard deviation for each trial
-  * attach the above data to the matrix data, "longtemp."
-<code>suburbs <- read.csv("http://commres.net/wiki/_export/code/r/data_transformations?codeblock=15", head=T, sep="	")</code>
-  * get subrubs data  as the above
-  * get population means by each state (listed in the data, suburbs)
-    * use aggregate and refer to the below e.g.
-<code>attach(Cars93)
-aggregate(MPG.city ~ Origin, Cars93, mean)</code>
-  * get population sum by each county with  tapply function.
-  * tapply(number, byfactor, function)
-  * how many counties are there?
-  * Use Cars93 data, get MPG.city mean by Origin.
-__Using pnorm, qnorm__
-pnorm : get proportion out of normal distribution whose characteristics are mean and sd
-<code>pnorm(84, mean=72, sd=15.2, lower.tail=FALSE)</code>
-  * What is the value of  the below?
-<code>pnorm(1)</code>
-  * How would you get 68, 95, 99% from pnorm
-      * use ?pnorm and see the default option
-  * generate 10 random numbers with runif function
-<code>year <- c(1900:2016)     # years in vector year
-world.series <- data.frame(year)</code>
-  * get 10 year samples out of world.series data with "sample" command
-  * how would you get the sample sample again latter?
-<code>pnorm(110, mean=100, sd=10)</code>
-  * What would be the result from the above?
-<code>library(MASS)       # load the MASS package
-tbl = table(survey$Smoke, survey$Exer)
-tbl                 # the contingency table</code>
-<code>summary(tbl)
-</code>
-  * read the above output and interpret
-  * what about the below one?
-<code>chisq.test(tbl)
-</code>
-see first [[:chi-square test]]
-see [[:b:r cookbook:chi-square test]] in r document space for more
-<code> library(MASS)
- cardata <- data.frame(Cars93$Origin, Cars93$Type)
- cardata
-</code>
-  * Can you say the types of cars are different by the Origins?
-<code>dur <- faithful$eruptions
-dur</code>
-  * make the above data into z-score (zdur).
-  * get mean of the zdur
-  * get sd of the zdur
-<code>
-set.seed(1123)
-x <- rnorm(50, mean=100, sd=15)
-</code>
-  * test x against population  mean 95.
-  * test x against population  mean 99.
-  * are they different from each other?
-  * what would you do if you want to see the different result from the  second one?
-<code>a = c(65, 78, 88, 55, 48, 95, 66, 57, 79, 81)
-> t.test(a, mu=60)
-	One Sample t-test
-data:  a
-t = 2.3079, df = 9, p-value = 0.0464
-alternative hypothesis: true mean is not equal to 60
-percent confidence interval:
-.22187 82.17813
-sample estimates:
-mean of x
-.2
-</code>
-  * find the t critical value with function qt.
-  * explain what happens in the next code
-  * read (or remind) what pnorm and qnorm do.
-<code>> s <- sd(x)
-> m <- mean(x)
-> n <- length(x)
-> n
-[1] 50
-> m
-[1] 96.00386
-> s
-[1] 17.38321
-> SE <- s / sqrt(n)
-> SE
-[1] 2.458358
-> E <- qt(.975, df=n-1)*SE
-> E
-[1] 4.940254
-> m + c(-E, E)
-[1]  91.0636 100.9441
-> </code>
-  * what's wrong with the below?
-<code>t.test(x)</code>
-<code>> mtcars</code>
-  * using aggregate, get mean for each trnas. type.
-  * compare the difference of mileage between auto and manual cars.
-    * use t.test (two sample)
-    * "use var.equal=T" option
-<code>a = c(175, 168, 168, 190, 156, 181, 182, 175, 174, 179)
-b = c(185, 169, 173, 173, 188, 186, 175, 174, 179, 180)
-</code>
-  * stack them into data c
-  * convert colnames into score and trans
-  * t.test score by trans with var.equal option true.
-  * aov test
-  * see  t.test t value, t = -0.9474 and F value,  F = ?
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 </WRAP>
 <WRAP half column>
+===== Assignment =====
+그룹과제 내용수정 ----
+그룹과제 내용을 아래와 같이 수정합니다.
 </WRAP>
 ====== Week16 (June 22, 25) ======