Linear Regression with One Regressor


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1 Linear Regression with One Regressor Michael Ash Lecture 8
2 Linear Regression with One Regressor a.k.a. Bivariate Regression An important distinction 1. The Model Effect of a oneunit change in X on the mean of Y. Role of randomness and idiosyncratic variation. Mean caveats apply. 2. Estimating the Model Computing sample coefficients with Ordinary Least Squares Hypothesis testing
3 The Linear Regression Model Slope, or response β ClassSize = Change in TestScore Change in ClassSize = TestScore ClassSize The Greek capital letter delta stands for change in. The Greek lowercase letter beta β is the symbol for how Y (TestScore) responds to a change in X (ClassSize). In this case, β is measured in test points per student. Other examples: how murder rates (Y ) respond to poverty (X ); how highway deaths (Y ) respond to drunkdriving penalties (X ); how earnings (Y ) respond to years of schooling (X ). Consider the β in each case.
4 Using a known β Suppose we know that β = 0.6 test points per student. (Adding one student to the class reduces the class test score by 0.6 points.) What is the effect of reducing class size by two students? Rearranging the definition of β, and then putting in the particular example. TestScore = β ClassSize ClassSize = ( 0.6) ( 2) = +1.2 In words: Reducing class size by two students will raise test scores by 1.2 points.
5 Building a Model TestScore = β 0 + β ClassSize ClassSize is a statement about relationship that holds on average across the population of districts. TestScore = β 0 + β ClassSize ClassSize + other factors is a statement that is true for any district. β 0 + β ClassSize ClassSize is the average effect of class size. other factors includes teacher quality, textbook quality, community income or wealth, native English speakers, testing variation, luck.
6 Linear Regression Model with a Single Regressor Y i = β 0 + β 1 X i + u i Y is the dependent variable, or outcome variable, or lefthand variable. (No one says regressand with a straight face.) X is the independent variable, or regressor, or explanatory variable, or righthand variable. β 0 + β 1 X is the population regression line, or the expected value (mean) of Y given X, or E(Y X ) = β 0 + β 1 X
7 Linear Regression Model with a Single Regressor Y i = β 0 + β 1 X i + u i β 1 and β 0 are the coefficents, or parameters of the regression line. β1 is the slope, the change in Y associated with a unit change in X. β0 is the intercept, the expected value of Y when X = 0. (Sometimes X = 0 doesn t make any sense.) β 0 raises or lowers the regression line.) u i is the error term or residual, which includes all of the unique, or idiosyncratic features of observation i, including randomness, measurement error, and luck that affect its outcome Y i.
8 Determinism and Randomness Appreciate determinism E(Y i X i ) = β 0 + β 1 X i Appreciate randomness u i Figure 4.1 Better or worse than predicted Determinism and randomness
9 Estimating the Coefficients of the Linear Regression Model Draw a best line through a scatterplot. Figure 4.2 Choosing ˆβ 0 and ˆβ 1 defines a line. What is the best line? Recall that the sample mean Y minimizes n (Y i m) 2 i=1
10 Estimating the Coefficients of the Linear Regression Model Now instead of getting to choose m, we get to choose b 0 and b 1 to minimize n (Y i b 0 b 1 X i ) 2 i=1 Let s focus on the key term: (Y i b 0 b 1 X i ) = (Y i (b 0 + b 1 X i )) = (Y i E(Y i X i ))
11 Estimating the Coefficients of the Linear Regression Model (Y i (b 0 + b 1 X i )) The key term expresses how far the actual value of Y i is from the expected value of Y i given X i according to the proposed line. We want to choose b 0 and b 1 to keep these gaps down. The values of b 0 and b 1 that keep n (Y i b 0 b 1 X i ) 2 i=1 as low as possible are called the ordinary least squares estimators of β 0 and β 1. The estimators are named ˆβ 0 and ˆβ 1.
12 The Ordinary Least Squares Estimators From Figure 4.2 to Figure 4.3 Choose b 0 and b 1 to minimize n (Y i b 0 b 1 X i ) 2 i=1 1. Could do this by trial and error. (Choose many alternative pairs b 0 and b 1 and see which gives the smallest sum of squared errors.) 2. Calculus creates simple formulas: ˆβ 1 = n i=1 (X i X)(Y i Y ) n i=1 (X i X ) 2 = s XY s 2 X ˆβ 0 = Y ˆβ 1 X 3. These are an average concept (again!) with all the good properties of sample averages.
13 Some OLS terminology OLS Regression Line Predicted Value of Y i given X i Ŷ = ˆβ 0 + ˆβ 1 X Ŷ i = ˆβ 0 + ˆβ 1 X i Predicted residual of the i th observation û i = Y i Ŷi
14 Test Scores and StudentTeacher Ratio Unit of observation: a California school district (n = 420) Variables: district average test score, district studentteacher ratio OLS Results ˆβ 0 = ˆβ 1 = 2.28 E(TestScore) = β 0 + β STR STR TestScore = STR The slope is 2.28: an increase in the studentteacher ratio (STR) by one student per class is, on average, associated with a decline in the districtwide test score by 2.28 points on the test.
15 Are the results big? Consider reducing STR by two students What would we expect to happen in a district with median studenttoteacher ratio and median test score? (No such district necessarily exists, but it s a useful reference point.) Table 4.1 Reduction of 2 students: from 19.7 (50th percentile) to 17.7 (c. 10th percentile) Expected change in test scores: = +4.6 Expected change in test scores: from (50th percentile) to (c. 60th percentile)? Worth it? Consider reducing STR by five students Beware outofsample predictions
16 Advantages of OLS Estimators Widely used method in social sciences, policy, and administration. Desirable Properties of OLS 1. ˆβ consistent and unbiased estimator of β 2. ˆβ is approximately normally distributed in large samples 3. With additional assumptions, ˆβ may be the smallest variance estimator of β.
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