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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
    * . . . 
  * 위를 위해서 꼭 이해해야 할 것들
    * Variance
    * Standard Deviation
    * Standard Error (Standard Deviation of Sample Means)
    * Hypothesis Testing
 
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R Cookbook
[[:b:r cookbook:getting_started|Chapter 1 Getting Started and Getting Help]]
[[:b:r cookbook:basics|Chapter 2 Some Basics]]
[[:b:r cookbook:navigating|Chapter 3 Navigating the Software]]
[[:b:r cookbook:input output|Chapter 4 Input and Output]]
[[:b:r cookbook:data structures|Chapter 5 Data Structures]]
[[:b:r cookbook:data transformations|Chapter 6 Data Transformations]]
[[:b:r cookbook:strings and dates|Chapter 7 Strings and Dates]]
[[:b:r cookbook:probability|Chapter 8 Probability]]
[[:b:r cookbook:general statistics|Chapter 9 General Statistics]]
[[:b:r cookbook:graphics|Chapter 10 Graphics]]
[[:b:r cookbook:linear regression and ANOVA|Chapter 11 Linear Regression and ANOVA]]
[[:b:r cookbook:useful tricks|Chapter 12 Useful Tricks]]
[[:b:r cookbook:beyond basic numerics and statistics|Chapter 13 Beyond Basic Numerics and Statistics]]
[[:b:r cookbook:times series analysis|Chapter 14 Time Series Analysis]]

[[../2025|이전 페이지]]

  * Week 01: <del>March 3</del>, 5 
  * Week 02: March 10, 12
  * Week 03: March 17, 19
  * Week 04: March 24, 26
  * Week 05: March 31, April 2 (<fc #ff0000>첫번째 퀴즈</fc>)
  * Week 06: April 7, 9
  * Week 07: April 14, 16
  * Week 08: April 21, 23
  * Week 09: April 28, 30
  * Week 10: <del>May 5</del>, 7
  * Week 11: May 12, 14
  * Week 12: May 19, 21
  * Week 13: May 26, 28
  * Week 14: June 2, 4
  * Week 15: June 9, 11
  * Week 16: June 16, 18



  * Week 01: March 5, 10 
  * Week 02: March 12, 17
  * Week 03: March 19, 24
  * Week 04: March 26, 31
  * Week 05: April 2 (<fc #ff0000>첫번째 퀴즈</fc>), 7
  * Week 06: April 9, 14
  * Week 07: April 16, 21
  * Week 08: April 23, 28
  * Week 09: April 30, <del>May 5</del>
  * Week 10: May 7, 12
  * Week 11: May 14, 19
  * Week 12: May 21, 26
  * Week 13: May 28, June 2
  * Week 14: June 4, 9
  * Week 15: June 11, 16
  * Week 16: June 18, 23


====== Week01 ======
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Course Introduction --> [[../2025|이전 페이지]]

===== ideas and concepts  =====
동영상 (R 관련)
  * [[https://youtu.be/6ExajWI_r2w]] 수업소개
  * [[https://youtu.be/KYQFY8c2ePI]] R 과 R studio 인스톨
  * [[https://youtu.be/qCeTcvWBDNY]] R studio 기초 설명
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Introduction to R and others
  - Downloading and Installing R
    - [[:the_r_project_for_statistical_computing]]
    - [[:r]], [[:r:getting started]]
  - Starting R
  - Entering Commands
  - Exiting from R
  - Interrupting R
  - Viewing the Supplied Documentation
  - Getting Help on a Function
  - Searching the Supplied Documentation
  - Getting Help on a Package
  - Searching the Web for Help
  - Finding Relevant Functions and Packages
  - Searching the Mailing Lists
  - Submitting Questions to the Mailing Lists
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동영상 (통계관련 샘플링에 대한 설명)
  *[[https://youtu.be/1hJm0O-RY4Q]] Sampling 과 관련된 아이디어와 용어 설명

기본용어
기술통계 ([[:descriptive statistics]])
추론통계 ([[:inferential statistics]])
아래의 개념은 [[:sampling|샘플링]] 문서를 먼저 볼것

  * 전집 ([[:population]])
  * 표본 ([[:sample]])
  * 모수치 ([[:sampling#parameter_statistics|parameter]])
  * 통계치 ([[:sampling#parameter_statistics|statistics]])

  * sampling methods
    * probability 
    * non-probability

가설 ([[:hypothesis]])
  * 차이와 연관 (difference and association) 
변인 ([[:variables]])
  * [[:types of variables]]
  * [[:level of measurement]]



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===== Assignment =====
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====== Week02 ======
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===== Concepts and ideas =====
[[:Sampling]]
[[:Hypothesis|가설]]
[[https://youtu.be/k1sdZtdeDu0|지난 동영상 리캡 및 가설에 대한 소개]]
[[https://youtu.be/gLWjVDl2_6o|가설에 대한 소개 및 설명]] 
[[https://youtu.be/Q9cradIrY2M|가설이 만들어지는 이유]]
[[https://youtu.be/hvTnKaX6wSg|가설의 예]]
[[https://youtu.be/eno5USKD34U|변인의 종류와 변인측정의수준]] 

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Some [[:b:r cookbook:basics|basics]]
  - Introduction
  - Printing Something
  - Setting Variables
  - Listing Variables
  - Deleting Variables
  - Creating a Vector
  - Computing Basic Statistics
  - Creating Sequences
  - Comparing Vectors
  - Selecting Vector Elements
  - Performing Vector Arithmetic
  - Getting Operator Precedence Right
  - Defining a Function
  - Typing Less and Accomplishing More
  - Avoiding Some Common Mistakes
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----
from the previous lecture (research question and hypothesis)
  * [[:Research Question]]s (or Problems)
    * Two ideas guided by theories
    * Questions on their relationships
    * Conceptualization
  * [[:Hypothesis]]
    * Educated guess (via theories)
    * types of hypotheses
      * Difference 
      * Association
    * e.g., {{:the_third_person_effect_sprial_of_silence.pdf|양승찬의 논문}} <- 읽을 것

    * [[:Variables]] (vs. ideas, concepts, and constructs)
      * [[:Operationalization]]
      * [[:Types of Variables]]
        * [[:Independent Variable|IV]] 
        * [[:Dependent Variable|DV]]
        * Control variable
        * Mediating (Intervening) variable
      * [[:Level of Measurement]]
[[Lecture note week 02]]
===== Assignment =====
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====== Week03 ======
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3주차 온라인 강의 동영상은 4주에 걸쳐서 보시기 바랍니다. 즉, 4주 중에 따로 동영상 올리지 않습니다. 

  * [[https://www.youtube.com/watch?v=nluWkZZ8zM8| MS the 3rd Week 012: the Basic (R cookbook)]] 32:00
  * [[https://www.youtube.com/watch?v=IEr7MM4vpEU| MS the 3rd Week 013: Navigating the R]] 12:31
  * [[https://www.youtube.com/watch?v=TPSApVNCM_c| MS the 3rd Week 014: Mean, Median, Mode (Howell, Ch. 4 Part)]] 16:17
  * https://youtu.be/JvpOJPCBQkQ : R cookbook: data structure
-----
  * [[https://youtu.be/_ynGzFFmm7U]] Howell Ch 4. Variance 01: Introduction (DS, error, and SS)
  * [[https://youtu.be/HugtyhU7Im8]] Howell Ch. 4. Variance 02: Variance for sample and n-1
  * [[https://youtu.be/RE6DSk1DcJI]] 왜 분산에는 n-1을 사용하는가? (직관적인 이해)
  * [[https://youtu.be/PrPoOCW3v1s]] n-1 증명  
  * [[https://youtu.be/Ssznnbdj5Lg]] Degrees of freedom
  * [[https://youtu.be/valhVpf-haY]] Standard deviation
-----
Howell, Ch. 4 내용 중 [[:Variance]]와 (분산) [[:Standard deviation]]은 (표준편차는) 이후 통계 검증방법을 이해하는데 기초가 되는 중요한 내용이니 꼭 숙지하시기 바랍니다.

===== Concepts and ideas =====

<WRAP box>[[:b:r cookbook:navigating|Navigating]] software
  - Introduction
  - Getting and Setting the Working Directory
  - Saving Your Workspace
  - Viewing Your Command History
  - Saving the Result of the Previous Command
  - Displaying the Search Path
  - Accessing the Functions in a Package
  - Accessing Built-in Datasets
  - Viewing the List of Installed Packages
  - Installing Packages from CRAN
  - Setting a Default CRAN Mirror
  - Suppressing the Startup Message
  - Running a Script
  - Running a Batch Script
  - Getting and Setting Environment Variables
  - Locating the R Home Directory
  - Customizing R
</WRAP>
[[: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]]
  * sa [[:sampling distribution in r]]
[[:Central Limit Theorem]]

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===== Assignment =====




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====== Week04 ======
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===== Class Activity =====
[[lecture note week 04]]

Lecture materials for this week

===== Concepts and ideas =====
<WRAP box>
[[:b:r cookbook:input_output|Input and output]]
  - Introduction
  - Entering Data from the Keyboard
  - Printing Fewer Digits (or More Digits)
  - Redirecting Output to a File
  - Listing Files
  - Dealing with “Cannot Open File” in Windows
  - Reading Fixed-Width Records
  - Reading Tabular Data Files
  - Reading from CSV Files
  - Writing to CSV Files
  - Reading Tabular or CSV Data from the Web
  - Reading Data from HTML Tables
  - Reading Files with a Complex Structure
  - Reading from MySQL Databases
  - Saving and Transporting Objects
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<code>
# +-1SD = 68% 
# +-2SD = 95% 
# +-3SD = 99% 라고 했지만 
# pnorm(2) = ? 
pnorm(2)
pnorm(2) - pnorm(-2)
pnorm(90,70,10)
pnorm(90,70,10) - pnorm(50,70,10)
pnorm(3) - pnorm(-3)
qnorm(0.975)
qnorm(0.025)
qnorm(0.995)
qnorm(0.005)
pnorm(3) - pnorm(-3)

# sa, http://commres.net/wiki/sampling_distribution_in_r?#n_4
m.ca <- 70
sd.ca <- 10
rnorm(n, m.ca, sd.ca)

set.seed(1001)
iter <- 10000
n <- 4
rnorm(n, m.ca, sd.ca)

means <- rep (NA, iter)
for(i in 1:iter){
  means[i] = mean(rnorm(n, m.ca, sd.ca))
}

# according to the prefessor
m.means.should.be <- m.ca
v.means.should.be <- sd.ca^2/n
sd.means.should.be <- sqrt(v.means.should.be)
m.means.should.be
v.means.should.be
sd.means.should.be

m.means <- mean(means)
v.means <- var(means)
m.means
v.means
sd.means <- sd(means)
sd.means


se <- sd.means.should.be
se2 <- se*2
m.means - se2
m.means + se2

s1 <- rnorm(n, m.ca, sd.ca)
mean(s1)

</code>
===== Assignment =====

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====== Week05 ======
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[[lecture note week 05]]
===== Announcement Quiz 01 =====
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퀴즈 있습니다. 
퀴즈 범위는 
  * 5주차까지 언급된 모든 동영상
  * R 과 관련해서는 동영상 내용만 포함합니다. 
  * pnorm, qnorm, rnorm, dnorm 등에 대해서는 계속 나옵니다. 
  * 사용법을 잘 익혀 두세요.
문서
  * [[:Sampling]] 
  * [[:Hypothesis]]
  * [[:Variables]]
    * [[:Types of Variables]]
    * [[:Level of Measurement]]
  * [[:Operationalization]]
  * [[:Conceptualization]]
  * [[:Mean]], [[:Median]], [[:Mode]]
  * [[:Variance]], [[:Standard Deviation]]
  * [[:Sampling Distribution]]
  * [[:Central Limit Theorem]]
  * [[:Sampling Distribution in R]]

  * 시험문제는 4지선다 혹은 단답식 답입니다. 
  * 문제는 모두 50문제 정도입니다.
</WRAP>
동영상 시청

  * https://youtu.be/Qaxj6LZ-iL0 : sampling distribution
  * https://youtu.be/0RZJbZtzs6s : sampling distribution e.g. in R
  * https://youtu.be/AbeIQvJJ5Vw : mean and variance (standard deviation) in sampling distribution (샘플평균들의 집합에서의 평균과 분산 (표준편차))
  * https://youtu.be/zFdbt2XoeM4 : CLT (central limit theorem) and standard error 중심극한정리와 표준오차
  * https://youtu.be/Udp-4MLAlvc : Testing hypothesis based on CLT principle CLT에 근거를 둔 가설의 검증 


  * [[:sampling distribution in r]] 
===== Concepts and ideas =====
[[:b:r cookbook:Data Structures]]
  - Introduction
  - Appending Data to a Vector
  - Inserting Data into a Vector
  - Understanding the Recycling Rule
  - Creating a Factor (Categorical Variable)
  - Combining Multiple Vectors into One Vector and a Factor
  - Creating a List
  - Selecting List Elements by Position
  - Selecting List Elements by Name
  - Building a Name/Value Association List
  - Removing an Element from a List
  - Flatten a List into a Vector
  - Removing NULL Elements from a List
  - Removing List Elements Using a Condition
  - Initializing a Matrix
  - Performing Matrix Operations
  - Giving Descriptive Names to the Rows and Columns of a Matrix
  - Selecting One Row or Column from a Matrix
  - Initializing a Data Frame from Column Data
  - Initializing a Data Frame from Row Data
  - Appending Rows to a Data Frame
  - Preallocating a Data Frame
  - Selecting Data Frame Columns by Position
  - Selecting Data Frame Columns by Name
  - Selecting Rows and Columns More Easily
  - Changing the Names of Data Frame Columns
  - Editing a Data Frame
  - Removing NAs from a Data Frame
  - Excluding Columns by Name
  - Combining Two Data Frames
  - Merging Data Frames by Common Column
  - Accessing Data Frame Contents More Easily
  - Converting One Atomic Value into Another
  - Converting One Structured Data Type into Another
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<WRAP half column>
{{:c:ms:2023:pasted:20230329-102748.jpeg}}
아래 두번째 그림은 population의 평균이 102 일 때
400명을 (1600명이 아니라) 샘플로 취했을 때의 
샘플평균들의 집합을 그린것입니다. 
{{:c:ms:2023:pasted:20230329-102811.jpeg}}

<code>
#####
mu.pop <- 100
sd.pop <- 10

set.seed(101)
treated.group <- rnorm(16, 112, 10)
treated.group
m.tg <- mean(treated.group)
m.tg

# install.packages("BSDA")
# library(BSDA)
z.test(treated.group, mu=mu.pop, sigma.x=sd.pop) 

mu.pop <- 100
sd.pop <- 10

set.seed(100)
treated.group.2 <- rnorm(16, 102, 10)
treated.group.2
m.treated.group.2 <- mean(treated.group.2)
m.treated.group.2

# install.packages("BSDA")
# library(BSDA)
z.test(treated.group.2, mu=mu.pop, sigma.x=sd.pop) 

set.seed(100)
treated.group.2 <- rnorm(1600, 102, 10)
treated.group.2
m.treated.group.2 <- mean(treated.group.2)
m.treated.group.2

# install.packages("BSDA")
# library(BSDA)
z.test(treated.group.2, mu=mu.pop, sigma.x=sd.pop) 


</code>

===== Assignment =====

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====== Week06 ======
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===== Concepts and ideas =====
이번 주 동영상 
  * https://youtu.be/hX0mbKm6M4s : z-test (z 테스트)
  * https://youtu.be/06xTY1cVtb8 : z score (표준점수) 
  * https://youtu.be/aG8X6EUu7xI : probability in R (R에서의 확률분포함수들)


또한 R에서 데이터를 (테이블 혹은 어레이) 이용하여 function을 적용하는 것에 대해서 잘 익혀두시기 바랍니다. 이는 R cookbook의 아래 내용에 해당이 됩니다 (특히 sapply, tapply, by 등)  
<WRAP box>
[[:b:r cookbook:Data Transformations]]
  - Introduction
  - Splitting a Vector into Groups
  - Applying a Function to Each List Element
  - Applying a Function to Every Row
  - Applying a Function to Every Column
  - Applying a Function to Groups of Data
  - Applying a Function to Groups of Rows
  - Applying a Function to Parallel Vectors or Lists
Strings and Dates
</WRAP>
</WRAP>

<WRAP half column>
[[./schedule/week06 t-test and anova note]]
===== Announcement =====
===== Assignment =====
</WRAP>

====== Week07 ======
<WRAP half column>
===== Concepts and ideas =====
[[:Hypothesis testing]]
[[:z-test]]
  * r 에서 qnorm(proportion) pnorm(z-score) function 이해 필요 
  * [[:z_score]] 참조
[[:types of error]]

7주차 동영상 
  * t-test
    * https://youtu.be/Eje8lR8EXPc t-test: Intro
    * https://youtu.be/BL9TZbDUVWg t-test: One sample t-test
    * https://youtu.be/E7QUCYRcbM0 t-test: Independent samples t-test; repeated measure t-test 일부
    * https://youtu.be/CV-DY9xdxtc t-test: Repeated measure t-test 계속
  * 관련 문서: [[:t-test]]
  * [[:r:t-test|t-test in r]]
  * r 에서, qt(proportion, df), pt(t-score, df) function 이해 필요 
  * [[:r/probability?s[]=qnorm]] 참조
  * [[:t_distribution_table]] 참조

[[:b:r_cookbook:probability|Probability calculation in R]] <- Probability in R cookbook (텍스트북)


<WRAP box>
[[:b:r cookbook:Probability]]
  - Introduction
  - Counting the Number of Combinations
  - Generating Combinations
  - Generating Random Numbers
  - Generating Reproducible Random Numbers
  - Generating a Random Sample
  - Generating Random Sequences
  - Randomly Permuting a Vector
  - Calculating Probabilities for Discrete Distributions
  - Calculating Probabilities for Continuous Distributions
  - Converting Probabilities to Quantiles
  - Plotting a Density Function
</WRAP>

</WRAP>
<WRAP half column>
[[./w07 anova note]]
[[./w07.2 factorial anova]] or two way anova
===== Assignment =====
----
  * 가설 만들어 보기
    * [[http://behavioralsciencewriting.blogspot.kr/2011/09/how-to-write-hypothesis.html|how to write hypothesis]] at behavioral science writing. 
    * One sample hypothesis [[http://www.socialresearchmethods.net/kb/hypothes.php|Hypothesis]] at www.socialresearchmethods.net
===== 8주차 퀴즈 =====
8주차 정기시험기간 중에 2차 퀴즈
  * 4월26일, 09:00 ~ (A, B교시)
  * 범위
    * 처음부터 One-way ANOVA test with post hoc test 까지 (R square에 대한 설명포함)
    * 제 9주차 내용이지만 수업시간에 다룬 것만 시험에 나옵니다. 
    * 동영상은 7주차까지 보셔야 합니다
      * [[:Sampling]]
      * [[:Hypothesis]]
      * [[:Variables]]
        * [[:Types of Variables]]
        * [[:Level of Measurement]]
      * [[:Mean]] [[:Median]] [[:Mode]]
      * [[:Variance]], [[:Standard Deviation]]
      * [[:Sampling Distribution]]
      * [[:Central Limit Theorem]], [[:Hypothesis Testing]]
      * [[:z-test]]
      * [[:t-test]]
      * [[:ANOVA]]
      * [[:post hoc test]]
 

</WRAP>

====== Week08 ======
시험기간
보강영상 수업

====== Week09 ======
<WRAP half column>
===== Concepts and ideas =====
영상 ANOVA
  * https://youtu.be/bNK5iIjAoHI : Intro to ANOVA (F-test)
  * https://youtu.be/L9ns0vuvWJ8 : principles of ANOVA 
  * https://youtu.be/xOixsz4Qkz0 : ANOVA, calculation based on the priciple
  * https://youtu.be/kyVXFS3jts4 : post-hoc test / t-test vs. ANOVA
위키페이지 참조
  * [[:ANOVA]] 
  * [[:Factorial ANOVA]] 
  * [[:repeated measure anova]]

<WRAP box>
[[:b:r cookbook:General Statistics]]
  - Introduction
  - Summarizing Your Data
  - Calculating Relative Frequencies
  - Tabulating Factors and Creating Contingency Tables
  - Testing Categorical Variables for Independence
  - Calculating Quantiles (and Quartiles) of a Dataset
  - Inverting a Quantile
  - Converting Data to Z-Scores
  - Testing the Mean of a Sample (t Test)
  - Forming a Confidence Interval for a Mean
  - Forming a Confidence Interval for a Median
  - Testing a Sample Proportion
  - Forming a Confidence Interval for a Proportion
  - Testing for Normality
  - Testing for Runs
  - Comparing the Means of Two Samples
  - Comparing the Locations of Two Samples Nonparametrically
  - Testing a Correlation for Significance
  - Testing Groups for Equal Proportions
  - Performing Pairwise Comparisons Between Group Means
  - Testing Two Samples for the Same Distribution
</WRAP>

</WRAP>
<WRAP half column>

vene . . . go or come

intervene
  * intervenient
convene
  * convention
  * convent
  * convenient
contravene
prevent
advent
circumvent



===== Assignment =====
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====== Week10 ======
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===== Concepts and ideas =====
10주차 동영상입니다. 
  * https://youtu.be/IpuyWhk1R9g : Factorial ANOVA
  * https://youtu.be/UuJhej1eJJI : Factorial ANOVA by hand 
  * https://youtu.be/rl6zs1lK0BE : Factorial ANOVA egs. 

see [[./schedule/w10.lecture.note]]

</WRAP>
<WRAP half column>

===== Assignment =====

</WRAP>

====== Week11 ======
<WRAP half column>
===== Concepts and ideas =====
동영상 (총 5 개)
  * https://youtu.be/vwxdhllHM-8 : Repeated Measures ANOVA, Intro 
  * https://youtu.be/L_jzB650Llo : Repeated Measures ANOVA in R
----
  * https://youtu.be/Cj7mxGBrIU8 : Correlations 01
  * https://youtu.be/oYKFeuAn140 : Correlations 02
  * https://youtu.be/aHdb4j3ybX8 : Spearman (Rank ordered) Correlation 

[[./schedule/w11.lecture.note]]

[[:repeated measure ANOVA]]
[[:correlation]]
----

[[:regression]]
[[:multiple regression]] 
[[:using dummy variables]]




[[:r:getting started]]
[[:b:r cookbook:basics]]
[[:b:r cookbook:navigating]] in r
[[:b:r cookbook:input output]] in r
[[:b:r cookbook:data structures]]
[[:b:r cookbook:data transformations]]
----
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[[r:graphics|Graphics]]
  - Introduction
  - Creating a Scatter Plot
  - Adding a Title and Labels
  - Adding a Grid
  - Creating a Scatter Plot of Multiple Groups
  - Adding a Legend
  - Plotting the Regression Line of a Scatter Plot
  - Plotting All Variables Against All Other Variables
  - Creating One Scatter Plot for Each Factor Level
  - Creating a Bar Chart
  - Adding Confidence Intervals to a Bar Chart
  - Coloring a Bar Chart
  - Plotting a Line from x and y Points
  - Changing the Type, Width, or Color of a Line
  - Plotting Multiple Datasets
  - Adding Vertical or Horizontal Lines
  - Creating a Box Plot
  - Creating One Box Plot for Each Factor Level
  - Creating a Histogram
  - Adding a Density Estimate to a Histogram
  - Creating a Discrete Histogram
  - Creating a Normal Quantile-Quantile (Q-Q) Plot
  - Creating Other Quantile-Quantile Plots
  - Plotting a Variable in Multiple Colors
  - Graphing a Function
  - Pausing Between Plots
  - Displaying Several Figures on One Page
  - Opening Additional Graphics Windows
  - Writing Your Plot to a File
  - Changing Graphical Parameters
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===== Assignment =====
과제명: ms23.w11.ga.covariance.exercise
제출파일명: ms23.w11.ga.covariance.exercise.group##.odc (docx)
과제내용:
아래 데이터를 다운로드 받아서 두 변인 간의 상관관계계수를 구하시오. 
{{:income.happiness.csv}}
데이터는 수입과 행복을 측정한 것입니다. 실제 데이터를 살펴보고 R로 읽어 온 후에 R을 이용하여 아래를 구하시오. R에서의 명령어와 아웃풋을 카피/패이스트 하여 제출하시오 (fixed-font를 사용하여).
  * 각 변인의 deviation score 값을 구하여 ds.inc 와 ds.hap 에 저장하시오. 
  * 두 변인의 SP값을 (Sum of Product) 구하여 sp.dat 에 저장하시오. 
  * 두 변인의 df값을 구하여 df.dat 에 저장하시오. 
  * 두 변인간 covariance값을 r의 cov 명령어를 이용하여 구하여 cov.dat값에 저장하시오. 
  * sp.dat / df.dat 값을 구하여 cov.cal 값에 저장하시오.
  * cov.cal 과 cov.dat 값이 같은지 비교하시오. (힌트: ''=='' 연산자를 이용하여 확인하시오) 
  * 각 변인의 standard deviation 값을 구하여 sd.inc, sd.hap에 저장하시오
  * 우리가 배운 correlation값을 구하는 공식에 따라서 r 값을 구해서 r.cal 에 저장하시오.
  * R의 cor 명령어를 이용하여 correlation coefficient값을 구하여 r.dat 에 저장하시오.
  * r.cal 과 r.dat 을 비교하시오.

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====== Week12 ======
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May 22 (월), 24 (수)
[[./schedule/w12.lecture.note]]

===== Announcement =====

===== Concepts and ideas =====
동영상 Regression
  - https://youtu.be/68gho4ubOjs : Regression 1. Intro
  - https://youtu.be/qXSRgSh1rw0 : Regression 2. e.g. 1
  - https://youtu.be/I8wt2W7-Iio : Regression 3. e.g. 2

[[:chi-square test]]
[[:b:r cookbook:probability]]
[[:b:r cookbook:general statistics]]

Graphics
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===== Assignment =====

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====== Week13 ======
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<WRAP box>
May 29 (월), 31 (수)
<del>May 31 (수)</del> 
6월 5일 (월) 세번째 퀴즈
퀴즈 범위는 
처음부터 multiple regression까지


퀴즈 범위는 
stats part
  * [[:sampling distribution]]
  * [[:central limit theorem]]
  * [[:hypothesis testing]]
  * [[:z-test]]
  * [[:types of error]]
  * [[:t-test]]
  * [[:ANOVA]]
  * [[:Factorial ANOVA]]
  * [[:Repeated Measure ANOVA]]
  * [[:correlation]]
  * [[:Regression]]
  * [[:Multiple Regression]]
  * [[:Interpretation of Multiple Regression]]
  * [[:Partial and semipartial correlation]]

r part
  * [[:b/r_cookbook/data_structures|Data structure in r]]
  * [[:b:r cookbook:data transformations|Data transformation in r]]
  * [[:b:r_cookbook:general statistics]] in r
r은 주로 아웃풋에 대한 질문이 있을 예정입니다. 오픈 북이니 모든 명령어 등을 외울 필요는 없습니다. 
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===== Concepts and ideas =====
영상
  * https://youtu.be/LOEinkXaskA : Multiple Regression 01 Intro. 
  * https://youtu.be/v6LswXPvEWY : Multiple Regression 03 Interpreting ivs
  * https://youtu.be/tc6wb7fBmiY : Week13 Multiple Regression 02 Dummy variables 
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===== Assignment =====
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====== Week14 ======
<WRAP half column>
June 5(월), 7(수)
<del>영상보기 
  * https://youtu.be/AXMtT5cYpZ4 Factor Analysis
</del>
===== Concepts and ideas =====
<del>[[:using dummy variables|Including Dummy variables]]</del>
[[:r:dummy variable]] with R
[[:interaction effects in regression analysis]]
[[:sequential regression]]
[[:beta coefficients]]
[[:mediation analysis]]






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<WRAP half column>
===== Assignment =====

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====== Week15 ======
<WRAP half column>
June 12, 14
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===== Assignment =====
그룹 assignment: ms.23.ga.w15.multiple.regression.groupID
그룹의 아래의 두 개 중 하나를 택하여 수행하시오. 6월 19일까지 완성 (ABB)
  - 일
    * data: elemapi2.csv
    * available at http://commres.net/wiki/_media/r/elemapi2.csv
    * api00 을 종속변인으로 하고 관련이 있을 것 같고 흥미로운 변인들을 독립변인으로 하여 (최소한 3개 이상, 그 중 하나는 종류변인이어야 합니다) multiple regression을 디자인 하고 이를 수행한 후 경과와 결과를 보고하시오
    * api00을 종속변인으로 하고 두 개의 독립변인으로 avg_ed와 mealcat 두 개를 골라서 interaction을 포함한 regression을 수행한 후 경과와 결과를 보고하시오.  
   - 이 
    * data: College (in ISLR package in R) 
      * data에 대한 정보
      * ''library(ISLR)'' 후 ''?College''
    * 종속변인 하나를 골라서 다른 독립변인들을(최소 3개 이상) 가지고 Regression을 수행한 후 경과와 결과를 보고하시오.
    * 위에서의 종속변인과 두개의 독립변인을 골라서 (숫자+숫자 혹은 숫자+종류) interaction 효과를 포함하는 Regression을 한 후에 경과와 결과를 보고하시오




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====== Week16 ======
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June 19, 21 (퀴즈일자에만 퀴즈를 보고 수업은 없음)

__**Final-term**__
  * 마지막 퀴즈
  * 범위는 다음과 같습니다. 
  * Statistics
    * [[:sampling distribution]]
    * [[:central limit theorem]]
    * [[:hypothesis testing]]
    * [[:z-test]]
    * [[:types of error]]
    * [[:t-test]]
    * [[:ANOVA]]
    * [[:Factorial ANOVA]]
    * [[:Repeated Measure ANOVA]]
    * [[:correlation]]
    * [[:Regression]]
    * [[:Multiple Regression]]
      * [[:beta coefficients]]
      * [[:r:dummy variable]]
      * [[:interaction effects in regression analysis]] 
      * [[:interaction effects in regression analysis]]
      * [[:sequential regression]]
      * [[:beta coefficients]]

  * R 관련 문제는 아웃풋을 이해하는지에 치중을 하시면 됩니다. 실제 명령어 사용 등에 대한 문제는 나오지 않습니다. 

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