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

ideas and concepts

Introduction to R and others

1. Downloading and Installing R
   1. the_r_project_for_statistical_computing
   2. r, getting started
2. Starting R
3. Entering Commands
4. Exiting from R
5. Interrupting R
6. Viewing the Supplied Documentation
7. Getting Help on a Function
8. Searching the Supplied Documentation
9. Getting Help on a Package
10. Searching the Web for Help
11. Finding Relevant Functions and Packages
12. Searching the Mailing Lists
13. Submitting Questions to the Mailing Lists

using theories and making hypotheses

Assignment

Concepts and ideas

Chater 2. Descriptive Statistics

• Measures of 'central tendency'
• Measures of 'spread'
• Describing a set of data: in conclusion
• Comparing two sets of data with descriptive statistics
• Some important information about numbers

—-
using theories and making hypotheses

• Theories
  • to build science
  • to guide as a frame (what to look, how to think, and how to look at)
  • to explain phenomena
  • to predict phenomena (to provide a context for predictions)
  • Empirically relevant (testing) and always tentative (deductive cycle)
    • via research (hypothesis testing)
    • hence, not fixed
  • generalized statement regarding a connection between A and B (idea, concept, construct, phenomena, etc)
  • Levels ?
    • Micro . . . frustration and aggression
    • Meso . . . online communities and disinhibition
    • Macro . . . ethnicity (or socio-econ class) and family bond
  • role of T
    • 
• Research Questions (or Problems)
  • Two ideas guided by theories
  • Questions on their relationships
  • Conceptualization
• Hypothesis
  • Educated guess (via theories)
  • Difference
  • Association
  • Variables (vs. ideas, concepts, and constructs)
    • Operationalization
    • Types of Variables
      • IV
      • DV
      • Control variable
      • Mediating (Intervening) variable

Assignment

Concepts and ideas

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

Assignment

Find two research articles that have listed hypotheses (social science research article would be good option). For each article:

1. 각 가설을 적고
2. 독립변인과 종속변인 그리고 intervening (moderator) 변인 등이 무엇인지 설명하시오.
3. 각 변인이 어떻게 측정되었는지 설명하시오.
4. 각 가설이 어떤 종류인지 설명하시오. (차이, 연관의 가설)
5. 가설검증을 위해서 어떤 테스트방법을 취했는지 찾아서 기록하시오.

due date: 다음 주 수요일 자정까지 완성하시오 (2018/09/26 11:59).

Sep. 25: Harvest Evening (23, 24, 25, 26)

Class Activity

• 가설 만들어 보기
  • how to write hypothesis at behavioral science writing.
  • One sample hypothesis Hypothesis at www.socialresearchmethods.net

z-test:

• r 에서 qnorm(proportion) pnorm(z-score) function 이해 필요
• z_score 참조

types of error
t-test

• r 에서, qt(proportion, df), pt(t-score, df) function 이해 필요
• probability 참조

Concepts and ideas

Assignment

Concepts and ideas

Data Structures

1. Introduction
2. Appending Data to a Vector
3. Inserting Data into a Vector
4. Understanding the Recycling Rule
5. Creating a Factor (Categorical Variable)
6. Combining Multiple Vectors into One Vector and a Factor
7. Creating a List
8. Selecting List Elements by Position
9. Selecting List Elements by Name
10. Building a Name/Value Association List
11. Removing an Element from a List
12. Flatten a List into a Vector
13. Removing NULL Elements from a List
14. Removing List Elements Using a Condition
15. Initializing a Matrix
16. Performing Matrix Operations
17. Giving Descriptive Names to the Rows and Columns of a Matrix
18. Selecting One Row or Column from a Matrix
19. Initializing a Data Frame from Column Data
20. Initializing a Data Frame from Row Data
21. Appending Rows to a Data Frame
22. Preallocating a Data Frame
23. Selecting Data Frame Columns by Position
24. Selecting Data Frame Columns by Name
25. Selecting Rows and Columns More Easily
26. Changing the Names of Data Frame Columns
27. Editing a Data Frame
28. Removing NAs from a Data Frame
29. Excluding Columns by Name
30. Combining Two Data Frames
31. Merging Data Frames by Common Column
32. Accessing Data Frame Contents More Easily
33. Converting One Atomic Value into Another
34. Converting One Structured Data Type into Another

Assignment

Concepts and ideas

ANOVA
factorial anova

Announcement

• First quiz on Week 07, Tuesday class (Oct. 16)
  • RANGE: Week 01 - 03 materials + lecture content + textbook
    • hypothesis, variables, types of variables, operationalization
    • z-test, mean . . . .
    • Textbook:
      • chapter 2, 3, 4, 5
  • NEXT quiz will be held on Oct. 23 during the mid term schedule.
  • The 2nd quiz will cover 1st quiz + Week 05-07 materials.

Assignment

Concepts and ideas

correlation
regression

Probability

1. Introduction
2. Counting the Number of Combinations
3. Generating Combinations
4. Generating Random Numbers
5. Generating Reproducible Random Numbers
6. Generating a Random Sample
7. Generating Random Sequences
8. Randomly Permuting a Vector
9. Calculating Probabilities for Discrete Distributions
10. Calculating Probabilities for Continuous Distributions
11. Converting Probabilities to Quantiles
12. Plotting a Density Function

Assignment

개인과제

Concepts and ideas

General Statistics
t-test
ANOVA
Factorial ANOVA
repeated measure anova
correlation and regression and multiple regression

• Before regression, SS actually is sum of (error squared of guessing estimates).
• sum of error square = 오차의 제곱의 합 = SS (오차라는 단어 없이 사용되는 용어)
• For this, read carefully 표준오차 잔여변량 (standard error residual) in Regression document.

1. Introduction
2. Summarizing Your Data
3. Calculating Relative Frequencies
4. Tabulating Factors and Creating Contingency Tables
5. Testing Categorical Variables for Independence
6. Calculating Quantiles (and Quartiles) of a Dataset
7. Inverting a Quantile
8. Converting Data to Z-Scores
9. Testing the Mean of a Sample (t Test)
10. Forming a Confidence Interval for a Mean
11. Forming a Confidence Interval for a Median
12. Testing a Sample Proportion
13. Forming a Confidence Interval for a Proportion
14. Testing for Normality
15. Testing for Runs
16. Comparing the Means of Two Samples
17. Comparing the Locations of Two Samples Nonparametrically
18. Testing a Correlation for Significance
19. Testing Groups for Equal Proportions
20. Performing Pairwise Comparisons Between Group Means
21. Testing Two Samples for the Same Distribution

Assignment

Concepts and ideas

multiple regression continued.

• multiple regression examples

sequential regression

• for more details we need to look at eg. 3 in linear regression in r space.

using dummy variables

Assignment

Concepts and ideas

getting started
basics
navigating in r
input output in r
data structures
data transformations

Graphics

1. Introduction
2. Creating a Scatter Plot
3. Adding a Title and Labels
4. Adding a Grid
5. Creating a Scatter Plot of Multiple Groups
6. Adding a Legend
7. Plotting the Regression Line of a Scatter Plot
8. Plotting All Variables Against All Other Variables
9. Creating One Scatter Plot for Each Factor Level
10. Creating a Bar Chart
11. Adding Confidence Intervals to a Bar Chart
12. Coloring a Bar Chart
13. Plotting a Line from x and y Points
14. Changing the Type, Width, or Color of a Line
15. Plotting Multiple Datasets
16. Adding Vertical or Horizontal Lines
17. Creating a Box Plot
18. Creating One Box Plot for Each Factor Level
19. Creating a Histogram
20. Adding a Density Estimate to a Histogram
21. Creating a Discrete Histogram
22. Creating a Normal Quantile-Quantile (Q-Q) Plot
23. Creating Other Quantile-Quantile Plots
24. Plotting a Variable in Multiple Colors
25. Graphing a Function
26. Pausing Between Plots
27. Displaying Several Figures on One Page
28. Opening Additional Graphics Windows
29. Writing Your Plot to a File
30. Changing Graphical Parameters

Assignment

Announcement

Quiz 03: Nov. 23

Concepts and ideas

chi-square test
probability
general statistics

Graphics

Assignment

Concepts and ideas

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)

• 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

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)

• 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.”

suburbs <- read.csv("http://commres.net/wiki/_export/code/r/data_transformations?codeblock=15", head=T, sep="	")

• 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.

attach(Cars93)
aggregate(MPG.city ~ Origin, Cars93, mean)

• 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

pnorm(84, mean=72, sd=15.2, lower.tail=FALSE)

• What is the value of the below?

pnorm(1)

• How would you get 68, 95, 99% from pnorm
  • use ?pnorm and see the default option

• generate 10 random numbers with runif function

year <- c(1900:2016)     # years in vector year
world.series <- data.frame(year)

• get 10 year samples out of world.series data with “sample” command
• how would you get the sample sample again latter?

pnorm(110, mean=100, sd=10)

• What would be the result from the above?

library(MASS)       # load the MASS package 
tbl = table(survey$Smoke, survey$Exer) 
tbl                 # the contingency table

summary(tbl)

• read the above output and interpret
• what about the below one?

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

• Can you say the types of cars are different by the Origins?

dur <- faithful$eruptions
dur

• make the above data into z-score (zdur).
• get mean of the zdur
• get sd of the zdur

set.seed(1123)
x <- rnorm(50, mean=100, sd=15)

• 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?

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 

• find the t critical value with function qt.
• explain what happens in the next code
• read (or remind) what pnorm and qnorm do.

> 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
> 

• what's wrong with the below?

t.test(x)

> mtcars

• 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

a = c(175, 168, 168, 190, 156, 181, 182, 175, 174, 179)
b = c(185, 169, 173, 173, 188, 186, 175, 174, 179, 180)

• 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 = ?

Assignment

1. Do Ex 1 part in linear regression

Concepts and ideas

ANOVA
oneway anova
twoway anova
linear regression

Linear Regression and ANOVA
http://commres.net/wiki/text_mining_example_with_korean_songs

Assignment

Group Presentation

Week16 (Dec. 18, 21)

Group Presentation
Final-term