「Regression model」の版間の差分

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==Classification of Regression models==
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{|class="wikitable"
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|-
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!colspan="2" rowspan="2" style="width:150px"|
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!colspan="2"|Independent variable (exposure)
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|-
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!style="width:250px"|Monovariable (single variable)
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!style="width:250px"|Multivariable (multiple variables)
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|-
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!rowspan="2"|Dependent variable<br>(outcome)
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!Continuous
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|
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*'''Simple linear regression'''
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::<math>Y = a + bX</math>
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|
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*'''Multivariable linear regression'''
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::<math>Y = a + b_1X_1 + b_2X_2 + b_3X_3 + \cdots</math>
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|-
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!Binary
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*'''Logistic regression'''
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::<math>\log Y = a + bX</math><br>where <math>Y</math> is odds of outcome
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|
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*'''Logistic regression'''
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::<math>\log Y = a + b_1X_1 + b_2X_2 + b_3X_3 + \cdots</math><br>where <math>Y</math> is odds of outcome
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|}

2022年12月12日 (月) 00:48時点における版

Basics & Definition
Epidemiology
Odds in statistics and Odds in a horse race
Collider bias
Data distribution
Statistical test
Regression model
Multivariate analysis
Marginal effects
Prediction and decision
Table-related commands in STATA
Missing data and imputation

Classification of Regression models

Independent variable (exposure)
Monovariable (single variable) Multivariable (multiple variables)
Dependent variable
(outcome)
Continuous
  • Simple linear regression
[math]\displaystyle{ Y = a + bX }[/math]
  • Multivariable linear regression
[math]\displaystyle{ Y = a + b_1X_1 + b_2X_2 + b_3X_3 + \cdots }[/math]
Binary
  • Logistic regression
[math]\displaystyle{ \log Y = a + bX }[/math]
where [math]\displaystyle{ Y }[/math] is odds of outcome
  • Logistic regression
[math]\displaystyle{ \log Y = a + b_1X_1 + b_2X_2 + b_3X_3 + \cdots }[/math]
where [math]\displaystyle{ Y }[/math] is odds of outcome