The Least Squares Assumptions for Causal Inference KEY CONCEPT 4.3 Y; = Bo + B1X; + ui, i = 1,...

The Least Squares Assumptions for Causal Inference KEY CONCEPT 4.3 Y; = Bo + B1X; + ui, i = 1,...,n, where B1 is the causal eYour instructor decided to run an experiment where he/she thinks the marks scored during an exam (0 = Y; = 100) can be a func

The Least Squares Assumptions for Causal Inference KEY CONCEPT 4.3 Y; = Bo + B1X; + ui, i = 1,...,n, where B1 is the causal effect on Y of X, and: 1. The error term u; has conditional mean 0 given Xị: E(u; X;) = 0; 2. (X;, Y;), i = 1,...,n, are independent and identically distributed (i.i.d.) draws from their joint distribution; and 3. Large outliers are unlikely: X; and Y; have nonzero finite fourth moments. Your instructor decided to run an experiment where he/she thinks the marks scored during an exam (0 = Y; = 100) can be a function of time pressure during exam. He/she assigns different timing to each student randomly by flipping a coin whether the student will get 90 minutes of 120 minutes to write the exam (Xi = 90 or 120). He/she ran the following regression model: Yi = Bo + B1X1 + Uị a) Explain why E (uị|X;) = 0 for this regression. b) Are the other assumptions from pg. 129 (Key concepts 4.3) satisfied in this regression? Why?

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