class: center, middle, inverse, title-slide # Midterm Review Questions ## ECON 480 • Econometrics • Fall 2021 ### Ryan Safner
Assistant Professor of Economics
safner@hood.edu
ryansafner/metricsF21
metricsF21.classes.ryansafner.com
--- # Question 1 What does **endogenous** mean, in words? What about statistically? --- # Question 2 If a regression is biased (from endogeneity), what can we learn about the bias? --- # Question 3 What does heteroskedasticity mean? Does heteroskedasticity **bias** `\(\hat{\beta_1}\)`? --- # Question 4 Is this data likely heteroskedastic or homoskedastic? <img src="midterm-review_files/figure-html/unnamed-chunk-1-1.png" style="display: block; margin: auto;" /> --- # Question 5 - What three things impact the variation of `\(\hat{\beta_1}\)`? How? --- # Question 6 What are the four assumptions we make about the error term? Which is most important? --- # Question 7 `$$Wages_i=\beta_0+\beta_1Education+u_i$$` a. What is in `\(u_i\)`? b. Is `\(\hat{\beta_1}\)` likely biased? --- # Question 8 What does `\(R^2\)` measure? What does it mean? How do we calculate it? --- # Question 9 What does `\(\sigma_u\)` (SER) measure? What does it mean? --- # Question 10 Interpret all of these numbers (except Adjusted R-squared and the last line): ``` ## ## Call: ## lm(formula = y ~ x, data = het_data) ## ## Residuals: ## Min 1Q Median 3Q Max ## -27.047 -2.690 0.232 2.509 18.752 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) -0.09958 0.23493 -0.424 0.672 ## x 0.20116 0.14049 1.432 0.153 ## ## Residual standard error: 5.253 on 498 degrees of freedom ## Multiple R-squared: 0.0041, Adjusted R-squared: 0.002101 ## F-statistic: 2.05 on 1 and 498 DF, p-value: 0.1528 ``` --- # Question 11 Interpret all of these numbers:
(1)
(Intercept)
-0.100
(0.235)
x
0.201
(0.140)
N
500
R-Squared
0.004
SER
5.253
*** p < 0.001; ** p < 0.01; * p < 0.05.
--- # Question 12 Suppose `\(Y\)` is normally distributed with a mean of 10 and a standard error of 5. What is the probability that `\(Y\)` is between 5 and 15? --- # Question 13 Explain what a `\(Z\)`-score means. --- # Question 14 Explain what a `\(p\)`-value means. :) --- # Question 15 We run the following hypothesis test at `\(\alpha=0.05\)`: `$$\begin{align*} H_0: \, & \beta_1=0\\ H_1: \, & \beta_1 \neq 0 \\ \end{align*}$$` Is this test one-sided or two-sided? We find the `\(p\)`-value is 0.02. What is our conclusion? Be specific and precise in your wording! --- # Question 16 Suppose we ran that hypothesis test on our finding. What can we conclude? .pull-left[ .smallest[ ``` ## ## Call: ## lm(formula = y ~ x, data = het_data) ## ## Residuals: ## Min 1Q Median 3Q Max ## -27.047 -2.690 0.232 2.509 18.752 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) -0.09958 0.23493 -0.424 0.672 ## x 0.20116 0.14049 1.432 0.153 ## ## Residual standard error: 5.253 on 498 degrees of freedom ## Multiple R-squared: 0.0041, Adjusted R-squared: 0.002101 ## F-statistic: 2.05 on 1 and 498 DF, p-value: 0.1528 ``` ] ] .pull-right[ .smallest[
(1)
(Intercept)
-0.100
(0.235)
x
0.201
(0.140)
N
500
R-Squared
0.004
SER
5.253
*** p < 0.001; ** p < 0.01; * p < 0.05.
] ]