Summary: Modelling Statistical

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• 1.1 Introduction

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• What is the difference between correlation and causation?

• Correlation: when x increases, y changes by a consistent amount
• Causation: indicates one event as the result of the occurence of the other event.
• 1.2 The simple regression model

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• What is the error term?

Error term u, collects all unobserved influences
• How to interpret the conditional mean independence?

Knowing the value of x, provides no information about u.
• What are fitted/predicted values?

^y, ^b,0 ^b1 etc
• How to deal with outliers?

1. Recorded incorrectly? correct them/remove them
2. Else leave them since they affect estimates so much.
• How to interpret a log(y)

Change is in b1*100%
• What are unbiased estimators?

If the conditional mean independence holds, b^0 = b0,
• 1.2.1 Multiple regression

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• Why do multiple regression models give a beter chance at uncovering causal relationships?

Explicitly hold other factors constant
• Name two examples of multicollinearity

1. log(x) + log(x^2) = log(x) + 2log(x)

should become
(b2+2b3) * log(x)

1. Full dummy variable model:

Leave out one of the dummy variables/ leave out the constant b0
• When can a multiple regression model explain a causal interpretation?

When all variables are exogenous: exogeneity.
PLEASE KNOW!!! There are just 62 flashcards and notes available for this material. This summary might not be complete. Please search similar or other summaries.