c:ma:2019:multiple_regression_exercise
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c:ma:2019:multiple_regression_exercise [2019/11/08 10:51] – [Ex. 2] hkimscil | c:ma:2019:multiple_regression_exercise [2021/11/11 10:14] (current) – [Ex. 2] hkimscil | ||
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* 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 ===== | ||
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* 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: | ||
- | </ | ||
- | |||
- | < | ||
- | > summary(lm.sal.rank) | ||
- | |||
- | Call: | ||
- | lm(formula = salary ~ rank, data = Salaries) | ||
- | |||
- | Residuals: | ||
- | | ||
- | -68972 -16376 | ||
- | |||
- | Coefficients: | ||
- | Estimate Std. Error | ||
- | (Intercept) | ||
- | rankAssocProf | ||
- | rankProf | ||
- | t value Pr(> | ||
- | (Intercept) | ||
- | rankAssocProf | ||
- | rankProf | ||
- | --- | ||
- | Signif. codes: | ||
- | 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ | ||
- | 0.05 ‘.’ 0.1 ‘ ’ 1 | ||
- | |||
- | Residual standard error: 23630 on 394 degrees of freedom | ||
- | Multiple R-squared: | ||
- | F-statistic: | ||
- | |||
- | > </ | ||
- | |||
- | < | ||
- | |||
- | Call: | ||
- | lm(formula = salary ~ yrs.service + rank + discipline + sex, | ||
- | data = Salaries) | ||
- | |||
- | Residuals: | ||
- | | ||
- | -64202 -14255 | ||
- | |||
- | Coefficients: | ||
- | Estimate Std. Error t value Pr(> | ||
- | (Intercept) | ||
- | yrs.service | ||
- | rankAssocProf 14560.40 | ||
- | rankProf | ||
- | disciplineB | ||
- | sexMale | ||
- | --- | ||
- | Signif. codes: | ||
- | |||
- | Residual standard error: 22650 on 391 degrees of freedom | ||
- | Multiple R-squared: | ||
- | F-statistic: | ||
- | > </ | ||
====== Discussion ====== | ====== Discussion ====== | ||
Common topics | Common topics |
c/ma/2019/multiple_regression_exercise.1573177879.txt.gz · Last modified: 2019/11/08 10:51 by hkimscil