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# Regression

### Terms

TermDefinition
Level of Confidence 1) is the amount of Type I error implied by a test 2) 100 minus the level of significance.
Steps in the t test 1) Set up null and alternative hypotheses. 2) Choose a level of significance and therefore a critical t value. (d.f. = n-K-1) 3) Run regression and get an calculated t-score. 4) Compare t score with critical t value.
Gauss-Markov Theorem Given Classical Assumptions I through VI, the OLS estimator of βk, is the minimum variance estimator, from among the set of all linear unbiased estimators of βk, for k = 0, 1, . . ., K. That is, OLS is BLUE
BLUE BEST LINEAR UNBIASED ESTIMATOR
Robustness a goal of econometrics; the degree to which the regression model can produce the similarly qualitative results across multiple data sets
Ordinary Least Squares Regression technique that minimizes the sum of the square of the residuals (minimize the sum of squared difference between actual data points and the estimated line)
Schoastic Error Term added to regression equation to introduce all of the variation in the dependent variable that cannot be explained by the independent variables
Residual The difference between the observed value and the estimated function value. Difference between observed Y and estimated regression line
Error Term The deviation of the observed value from the (unobservable) true function value. Difference between observed Y and true regression line.
T-test Tests for the significance of explanatory variables.
t-value Coefficient/SE
Critical t-value is calculated based on three items: 1) Degrees of freedom = ? 2) One-sided or two-sided test? 3) Significance level (generally 5% or 1%)?
Standard Error Depends on: 1) Goodness of fit of equation (SEE, or Standard Error of the Equation) 2) Sample size – larger samples make SEE smaller 3) How widespread X’s are from their mean
TSS Total sum of squares (TSS) = Explained sum of squares (ESS) + Residual sum of squares (RSS). Squared variations of Y around its mean.
ESS Amount of the squared deviation of from its mean that is explained by the estimated regression line.
RSS Amount of the squared deviation of from its mean that is unexplained by the estimated regression line; OLS minimizes RSS
Created by: kristel387