Home » Social Sciences » Management » The Analytics Edge (Spring 2017) (M-I-T) » Unit 3: Logistic Regression (M-I-T) » 3.4 Election Forecasting: Predicting the Winner Before any Votes are Cast (Recitation) (M-I-T)

3.4 Election Forecasting: Predicting the Winner Before any Votes are Cast (Recitation) (M-I-T)

S#	Lecture	Course	Institute	Instructor	Discipline
1	3.4.1 Welcome to Recitation 3 (M-I-T)	3.4 Election Forecasting: Predicting the Winner Before any Votes are Cast (Recitation) (M-I-T)	MIT	John Siberholz	Social Sciences
2	3.4.2 Video 1: Election Prediction (M-I-T)	3.4 Election Forecasting: Predicting the Winner Before any Votes are Cast (Recitation) (M-I-T)	MIT	John Siberholz	Social Sciences
3	3.4.3 Video 2: Dealing with Missing Data (M-I-T)	3.4 Election Forecasting: Predicting the Winner Before any Votes are Cast (Recitation) (M-I-T)	MIT	John Siberholz	Social Sciences
4	3.4.4 Video 3: A Sophisticated Baseline Method (M-I-T)	3.4 Election Forecasting: Predicting the Winner Before any Votes are Cast (Recitation) (M-I-T)	MIT	John Siberholz	Social Sciences
5	3.4.5 Video 4: Logistic Regression Models (M-I-T)	3.4 Election Forecasting: Predicting the Winner Before any Votes are Cast (Recitation) (M-I-T)	MIT	John Siberholz	Social Sciences
6	3.4.6 Video 5: Test Set Predictions (M-I-T)	3.4 Election Forecasting: Predicting the Winner Before any Votes are Cast (Recitation) (M-I-T)	MIT	John Siberholz	Social Sciences

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