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Econ103_spring11_lec4

# Econ103_spring11_lec4 - ECON 103 Lecture 4 Simple...

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ECON 103, Lecture 4: Simple Regression I Maria Casanova April 7 (version 0) Maria Casanova Lecture 4

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1. Introduction Regression Analysis : The study of the relationship between one variable ( dependent variable ) and one or more other variables ( independent, or explanatory, variables ). Simple Regression: Linear regression model with one regressor Multiple Regression: Linear regression model with more than one regressors Maria Casanova Lecture 4
1. Introduction What do we use regression analysis for? To estimate the mean or average value of the dependent variable, given the values of the independent variables. What is the average income for people with a high school diploma? What is the average income for people with a college degree? To test a hypothesis implied by economic theory If we increase the price will the quantity demanded fall? To predict, or forecast, the mean value of the dependent variable given the independent variables. What will happen to GDP if we change the interest rate? Maria Casanova Lecture 4

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1. Introduction We will go through the steps of regression analysis with a specific example in mind. General question of interest: What is the relationship between class size and education outcomes? Specific question: What is the quantitative effect of increasing (or reducing) class size by one student on standardized test scores for 5 th graders? Maria Casanova Lecture 4
2. Data Consider data from a random sample of n = 420 California school districts. Variables recorded for each district: Average standardized test score in that district Student-teacher ratio (STR) in that district (that is, average class size) When looking at the relationship between 2 variables, it is always good to start by looking at a scatterplot . Maria Casanova Lecture 4

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2. Data Maria Casanova Lecture 4
3. Regression Line Recall that we are interested in the effect of a one unit increase in STR on test scores.

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