c:ma:2019:multiple_regression_exercise
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| c:ma:2019:multiple_regression_exercise [2019/11/08 10:42] – [Ex. 2] hkimscil | c:ma:2019:multiple_regression_exercise [2021/11/11 10:14] (current) – [Ex. 2] hkimscil | ||
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| Line 6: | Line 6: | ||
| * Use ''? | * Use ''? | ||
| * Use '' | * Use '' | ||
| + | * 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 | ||
| - | | + | see [[: |
| - | * Make a null model (with no variables) then, build up the model with additional IVs until you find a fitted model. | + | see also [[: |
| - | * Can we use '' | + | |
| - | * Interpret the result | + | |
| + | * <del>Make a null model (with no variables) then, build up the model with additional IVs until you find a fitted model.</ | ||
| + | * <del>Can we use '' | ||
| + | * <del>Interpret the result</ | ||
| + | |||
| + | < | ||
| - | > step(lm.full, direction=" | + | [[./ |
| ===== Ex. 2 ===== | ===== Ex. 2 ===== | ||
| Line 18: | Line 32: | ||
| * load the tidyverse | * load the tidyverse | ||
| * '' | * '' | ||
| - | * '' | + | |
| - | * Use a dataset | + | * load the car and the carData |
| - | * describe the data set | + | |
| + | * '' | ||
| + | * explain what it is | ||
| + | | ||
| ---- | ---- | ||
| * Regress sex variable on salary variable | * Regress sex variable on salary variable | ||
| * Write the regression model | * Write the regression model | ||
| - | * Discuss the difference | + | * Discuss the difference |
| * Use rank variable for the same purpose | * Use rank variable for the same purpose | ||
| - | * -- | + | * Write the regression model |
| - | * Use yrs.service + rank + discipline | + | * Regress |
| - | * on salary | + | |
| * How do you interpret the result? | * 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 | 위의 Salaries 데이터사용이 안 될 때 | ||
| Line 41: | Line 65: | ||
| 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 " | 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 " | ||
| < | < | ||
| - | > head(Salaries) | ||
| - | rank discipline yrs.since.phd yrs.service | ||
| - | 1 Prof B 19 18 Male 139750 | ||
| - | 2 Prof B 20 16 Male 173200 | ||
| - | 3 AsstProf | ||
| - | 4 Prof B 45 39 Male 115000 | ||
| - | 5 Prof B 40 41 Male 141500 | ||
| - | 6 AssocProf | ||
| - | </ | ||
| - | |||
| - | |||
| - | < | ||
| - | > summary(lm.sal.sex) | ||
| - | |||
| - | Call: | ||
| - | lm(formula = salary ~ sex, data = Salaries) | ||
| - | |||
| - | Residuals: | ||
| - | | ||
| - | -57290 -23502 | ||
| - | |||
| - | Coefficients: | ||
| - | Estimate Std. Error | ||
| - | (Intercept) | ||
| - | sexMale | ||
| - | t value Pr(> | ||
| - | (Intercept) | ||
| - | sexMale | ||
| - | --- | ||
| - | Signif. codes: | ||
| - | 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ | ||
| - | 0.05 ‘.’ 0.1 ‘ ’ 1 | ||
| - | Residual standard error: 30030 on 395 degrees of freedom | ||
| - | Multiple R-squared: | ||
| - | F-statistic: | ||
| - | </ | ||
| ====== Discussion ====== | ====== Discussion ====== | ||
| Common topics | Common topics | ||
c/ma/2019/multiple_regression_exercise.1573177353.txt.gz · Last modified: 2019/11/08 10:42 by hkimscil