A ダミー変数, also known as an indicator variable, is a binary variable that takes on the value of 0 or 1 to indicate the presence or absence of a categorical effect that may be expected to shift the outcome of a regression model. Dummy variables are commonly used in 統計的モデリング and econometrics to allow for the inclusion of categorical data in regression analyses, which typically require numerical input.
For example, if we want to analyze the impact of gender (male or female) on salary, we can create a dummy variable where 0 represents ‘male’ and 1 represents ‘female’. This allows us to incorporate gender as a factor in the regression model without losing the information that categorical variables hold. By using dummy variables, we can estimate the influence of different categories on the dependent variable while controlling for other variables.
複数のダミー変数を利用する場合、すべてのカテゴリをモデルに含めるとダミー変数の罠（ダミー変数トラップ）に陥るのを避けることが重要です。これは、多重共線性を引き起こし、独立変数間の高い相関を生じさせる可能性があります。代わりに、一つのカテゴリを除外して基準グループとします。例えば、3つのカテゴリ（A、B、C）がある場合、通常はAとBのダミー変数を含め、Cを基準カテゴリとします。
In summary, dummy variables facilitate the incorporation of categorical data into regression models, enhancing the model’s predictive power and allowing for a more nuanced understanding of relationships between variables.