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Lecture

# Top-10.docx

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Department
Economics
Course Code
ECON 120B
Professor
Carroll Foster

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Ec 120B ­­ ECONOMETRICS B                                                                   LECTURE NOTES Foster, UCSD                                                                                April 10, 2014                 TOPIC 10.  INTRO TO CORRELATION & REGRESSION A. Covariance, Correlation, and Statistical Inference 1. Review of Population Covariance and Correlation:   a) X and Y are jointly distributed random variables. 1) Marginal distributions f(x) and f(y) have parameters μ  axd σ , x  ynd σ .y 2) Joint distribution f(x, y) has parameters: • Covariance σ  xy Cov(X, Y) = E[X–μ )(Y–x )];  y∞  t(3) = 3.182 in right tail.  M and G are not  0 .025 statistically independent. 4. Interpretation and Abuses of Correlation:   a) Correlation is a measure of direction and strength of the linear relationship between two jointly  distributed random variables. 1) Graph sample pairs xi i in a scatter plot.  The closer the points are to a straight line, the stronger  the (linear) correlation between X and Y (and the cxyser r  to ±1). • |r| > 0.8   ▯ strong relationship•    |r|
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