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--- c:ma:2019:multiple_regression_exercise [2019/11/06 22:51] – [Class A] hkimscil
+++ c:ma:2019:multiple_regression_exercise [2021/11/11 01:14] (current) – [Ex. 2] hkimscil
@@ Line 1: / Line 1: @@
-====== Class A ======
+====== Class Activities ======
+===== Ex. 1 =====
   * Install packages ISLR
   * use a dataset, Carseats
@@ Line 6: / Line 6: @@
   * Use ''?Carseats'' command for the explanation of the dataset
   * Use ''str'' function to see the characteristic of each variable. Make it sure that ''SelvesLoc'' variable should be factor, not int or anything.
+  * 변인설명을 토대로 가설만들기
+    * 종속변인 = Sales
+    * 독립변인 = 숫자변인 1 + 종류변인 1  (조별 선택)
+    * Multiple regression without interactin
+    * Multiple regression with interaction
+  * 가설 만들기
+    * 종속변인 Sales
+    * 독립변인 여러개 (interaction 없이)
+    * Modeling 해 볼 것
-  * Make a full model (with all variables) then reduce down the model until you find it fitted.
+see [[:hierarchical regression]]
-  * Make a null model (with no variables) then, build up the model with additional IVs until you find a fitted model.
+see also [[:statistical regression methods]] <- 많이 쓰이지 않음
-  * Can we use ''step'' or ''stepAIC'' (MASS package needed) function?
-  * Interpret the result
-> step(lm.full, direction="back")
+  * <del>Make a full model (with all variables) then reduce down the model until you find it fitted.</del>
+  * <del>Make a null model (with no variables) then, build up the model with additional IVs until you find a fitted model.</del>
+  * <del>Can we use ''step'' or ''stepAIC'' (MASS package needed) function?</del>
+  * <del>Interpret the result</del>
-----
+<del>> step(lm.full, direction="back")</del>
+[[./eg_script]]
+===== Ex. 2 =====
   * Install packages tidyverse
   * load the tidyverse
-  * ''install.packages("car")''
+  * ''%%install.packages("car")%%''
-  * ''data("Salaries", package = "car")''
+  * ''%%install.packages("carData")%%''
-  * Use a dataset Salaries
+  * load the car and the carData
-  * describe the data set
+  * ''%%data("Salaries", package = "carData")%%''
+  * ''%%?Salaries%%''
+    * explain what it is
+    * describe the data set
 ----
   * Regress sex variable on salary variable
   * Write the regression model
-  * Discuss the difference
+  * Discuss the difference male and female (sex)
   * Use rank variable for the same purpose
-  * --
+  * Write the regression model
-  * Use yrs.service + rank + discipline + sex
+  * Regress rank + sex on salary
-  * on salary
+  * How do you interpret the result?
+  * And regress rank + sex + rank:sex on salary
+  * How do you interpret this result?
+  * Do factorial ANOVA test with rank and sex on salary
   * How do you interpret the result?
-----
+  * Test regression model of your own choice
+  * Interpret the result
+-----
 위의 Salaries 데이터사용이 안 될 때
   * download to R from here {{:salaries.csv}}
-    * use to import the data set. <code>Salaries <- read.csv("http://commres.net/wiki/_media/salaries.csv")</code>
+    * use to import the data set. ''%%Salaries <- read.csv("http://commres.net/wiki/_media/salaries.csv")%%''
   * for information about Salaries (it may not be loaded),
-    * use ''??Salaries'' to describe the data set.
+    * use ''%%??Salaries%%'' to describe the data set.
+-----
+Please copy and paste the proper r command and output to a txt file (use notepad or some other text editing program). You could use MS Word, but, please make it sure that you use type-setting fonts such as "Courier New." The below output, as an example, includes the r command ''%%head(Salaries)%%'' and the output.
+<code>
 ====== Discussion ======