1. Introduction
2. Descriptive Statistics
3. Standard Score
4. Intro to hypothesis testing
5. Sampling
6. HT with one sample
7. Selecting samples for comparison
8. HT with two samples
9. Significance, error and power
10. Intro to the analysis of variance
11. One factor independent measure ANOVA
12. Multiple comparisons
13. One factor repeated measure ANOVA
14. Interaction of factors in the ANOVA
15. Calculating two factor ANOVA
16.
17.
18. One factor ANOVA for ranked data
19. Chi-square
20. Linear correlation and regression
21. Multiple correlation and regression
22. Complex analyses and computers
23. An introduction to the general linear model
Introduction to R and others
using theories and making hypotheses
Some basics
Chater 2. Descriptive Statistics
—-
using theories and making hypotheses
Navigating software
Mean
Mode
Median
Variance
Standard Deviation
+-1 sd = 68% = +-1 sd
+-2 sd = 95% = +-1.96 sd
+-3 sd = 99% (99.7%) = +-3 sd
표준점수 (unit with a standard deviation) = z score
Sampling distribution via random sampling
Central Limit Theorem
Hypothesis testing
z-test
Find two research articles that have listed hypotheses (social science research article would be good option). For each article:
due date: 다음 주 수요일 자정까지 완성하시오 (2018/09/26 11:59).
Sep. 25: Harvest Evening (23, 24, 25, 26)
Strings and Dates
Mid-term period
Range:
General Statistics
t-test
ANOVA
Factorial ANOVA
repeated measure anova
correlation and regression and multiple regression
getting started
basics
navigating in r
input output in r
data structures
data transformations
Quiz 03: Nov. 23
chi-square test
probability
general statistics
Graphics
Do the following
S1 <- c(89, 85, 85, 86, 88, 89, 86, 82, 96, 85, 93, 91, 98, 87, 94, 77, 87, 98, 85, 89, 95, 85, 93, 93, 97, 71, 97, 93, 75, 68, 98, 95, 79, 94, 98, 95) S2 <- c(60, 98, 94, 95, 99, 97, 100, 73, 93, 91, 98, 86, 66, 83, 77, 97, 91, 93, 71, 91, 95, 100, 72, 96, 91, 76, 100, 97, 99, 95, 97, 77, 94, 99, 88, 100, 94, 93, 86) S3 <- c(95, 86, 90, 90, 75, 83, 96, 85, 83, 84, 81, 98, 77, 94, 84, 89, 93, 99, 91, 77, 95, 90, 91, 87, 85, 76, 99, 99, 97, 97, 97, 77, 93, 96, 90, 87, 97, 88) S4 <- c(67, 93, 63, 83, 87, 97, 96, 92, 93, 96, 87, 90, 94, 90, 82, 91, 85, 93, 83, 90, 87, 99, 94, 88, 90, 72, 81, 93, 93, 94, 97, 89, 96, 95, 82, 97) scores <- list(S1=S1,S2=S2,S3=S3,S4=S4)
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)
suburbs <- read.csv("http://commres.net/wiki/_export/code/r/data_transformations?codeblock=15", head=T, sep=" ")
attach(Cars93) aggregate(MPG.city ~ Origin, Cars93, mean)
Using pnorm, qnorm
pnorm : get proportion out of normal distribution whose characteristics are mean and sd
pnorm(84, mean=72, sd=15.2, lower.tail=FALSE)
pnorm(1)
year <- c(1900:2016) # years in vector year world.series <- data.frame(year)
pnorm(110, mean=100, sd=10)
library(MASS) # load the MASS package tbl = table(survey$Smoke, survey$Exer) tbl # the contingency table
summary(tbl)
chisq.test(tbl)
see first chi-square test
see chi-square test in r document space for more
library(MASS) cardata <- data.frame(Cars93$Origin, Cars93$Type) cardata
dur <- faithful$eruptions dur
set.seed(1123) x <- rnorm(50, mean=100, sd=15)
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 95 percent confidence interval: 60.22187 82.17813 sample estimates: mean of x 71.2
> 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 >
t.test(x)
> mtcars
a = c(175, 168, 168, 190, 156, 181, 182, 175, 174, 179) b = c(185, 169, 173, 173, 188, 186, 175, 174, 179, 180)
ANOVA
oneway anova
twoway anova
linear regression
multiple regression
partial and semipartial correlation
statistical regression methods
sequential_regression
Linear Regression and ANOVA
http://commres.net/wiki/text_mining_example_with_korean_songs
Final quiz
Part I (필기시험): NO open book.
Part II (r 실기시험): 교재와 R help만 허용
Final-term