[Side Project After Work] Big Data Analysis Certification Practical Exam (Type 1, 2, 3)
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Practical Problem Type 3 Frequently Asked Questions: When to use C()?
โ 1. ANOVA / Two-way ANOVA / One-way ANOVA
โ For categorical factors, C() is the standard practice
Yes:
model = ols("y ~ C(group)", data=df).fit()
anova_lm(model)ANOVA is originally an analysis that compares "differences in means between groups" โ factors are categorical.
Therefore, even if the problem doesn't explicitly state "categorical" in words,
Since the factor itself is a group variable, C() is the default.
In other words,
โ Even if it's in numbers โ C()
โ Even if it's in text โ C()
โ2. Regression Analysis (OLS)
โกOnly variables explicitly specified as categorical in the problem should use C()
Yes:
ols("y ~ x1 + region", data=df)
Just because it's in numbers doesn't mean it should automatically be treated as categorical data - that's wrong.
Treat numeric variables as continuous unless the problem specifically states they are "categorical variables"
โ3. Logistic Regression (logit)
โกSame principle as ols
Yes:
logit("target ~ x1 + job_type", data=df)
logit only needs C() when the problem explicitly states "categorical".
Otherwise, never automatically add C().