Here’s question 15 of our exam: 15. Consider the following procedure. • Set n = 100 and draw n continuous values x_i uniformly distributed between 0 and 10. Then simulate data from the model y_i = a + bx_i + error_i, for i = 1,…,n, with a = 2, b = 3, and independent errors […]
Author: Andrew
Question 14 of our Applied Regression final exam (and solution to question 13)
Here’s question 14 of our exam: 14. You are predicting whether a student passes a class given pre-test score. The fitted model is, Pr(Pass) = logit^−1(a_j + 0.1x), for a student in classroom j whose pre-test score is x. The pre-test scores range from 0 to 50. The a_j’s are estimated to have a normal […]
Naomi Wolf and David Brooks
Palko makes a good point: Parul Sehgal has a devastating review of the latest from Naomi Wolf, but while Sehgal is being justly praised for her sharp and relentless treatment of her subject, she stops short before she gets to the most disturbing and important implication of the story. There’s an excellent case made here […]
Question 13 of our Applied Regression final exam (and solution to question 12)
Here’s question 13 of our exam: 13. You fit a model of the form: y ∼ x + u full + (1 | group). The estimated coefficients are 2.5, 0.7, and 0.5 respectively for the intercept, x, and u full, with group and individual residual standard deviations estimated as 2.0 and 3.0 respectively. Write the […]
Question 12 of our Applied Regression final exam (and solution to question 11)
Here’s question 12 of our exam: 12. In the regression above, suppose you replaced height in inches by height in centimeters. What would then be the intercept and slope of the regression? (One inch is 2.54 centimeters.) And the solution to question 11: 11. We defined a new variable based on weight (in pounds): heavy […]
How statistics is used to crush (scientific) dissent.
Lakeland writes: When we interpret powerful as political power, I think it’s clear that Classical Statistics has the most political power, that is, the power to get people to believe things and change policy or alter funding decisions etc… Today Bayes is questioned at every turn, and ridiculed for being “subjective” with a focus on […]
Question 11 of our Applied Regression final exam (and solution to question 10)
Here’s question 11 of our exam: 11. We defined a new variable based on weight (in pounds): heavy 200 and then ran a logistic regression, predicting “heavy” from height (in inches): glm(formula = heavy ~ height, family = binomial(link = “logit”)) coef.est coef.se (Intercept) -21.51 1.60 height 0.28 0.02 — n = 1984, k = […]
Question 10 of our Applied Regression final exam (and solution to question 9)
Here’s question 10 of our exam: 10. For the above example, we then created indicator variables, age18_29, age30_44, age45_64, and age65up, for four age categories. We then fit a new regression: lm(formula = weight ~ age30_44 + age45_64 + age65up) coef.est coef.se (Intercept) 157.2 5.4 age30_44TRUE 19.1 7.0 age45_64TRUE 27.2 7.6 age65upTRUE 8.5 8.7 n […]
Question 9 of our Applied Regression final exam (and solution to question 8)
Here’s question 9 of our exam: 9. We downloaded data with weight (in pounds) and age (in years) from a random sample of American adults. We created a new variables, age10 = age/10. We then fit a regression: lm(formula = weight ~ age10) coef.est coef.se (Intercept) 161.0 7.3 age10 2.6 1.6 n = 2009, k […]
Question 8 of our Applied Regression final exam (and solution to question 7)
Here’s question 8 of our exam: 8. Out of a random sample of 50 Americans, zero report having ever held political office. From this information, give a 95% confidence interval for the proportion of Americans who have ever held political office. And the solution to question 7: 7. You conduct an experiment in which some […]
Question 7 of our Applied Regression final exam (and solution to question 6)
Here’s question 7 of our exam: 7. You conduct an experiment in which some people get a special get-out-the-vote message and others do not. Then you follow up with a sample, after the election, to see if they voted. If you follow up with 500 people, how large an effect would you be able to […]
Question 6 of our Applied Regression final exam (and solution to question 5)
Here’s question 6 of our exam: 6. You are applying hierarchical logistic regression on a survey of 1500 people to estimate support for a federal jobs program. The model is fit using, as a state-level predictor, the Republican presidential vote in the state. Which of the following two statements is basically true? (a) Adding a […]
Question 5 of our Applied Regression final exam (and solution to question 4)
Here’s question 5 of our exam: 5. You have just graded an exam with 28 questions and 15 students. You fit a logistic item-response model estimating ability, difficulty, and discrimination parameters. Which of the following statements are basically true? (a) If a question is answered correctly by students with low ability, but is missed by […]
Tony nominations mean nothing
Someone writes: I searched up *Tony nominations mean nothing* and I found nothing. So I had to write this. There are currently 41 theaters that the Tony awards accept when nominating their choices. If we are being as generous as possible, we could say that every one of those theaters will be hosting a performance […]
Question 4 of our Applied Regression final exam (and solution to question 3)
Here’s question 4 of our exam: 4. A researcher is imputing missing responses for income in a social survey of American households, using for the imputation a regression model given demographic variables. Which of the following two statements is basically true? (a) If you impute income deterministically using a fitted regression model (that is, imputing […]
Question 3 of our Applied Regression final exam (and solution to question 2)
Here’s question 3 of our exam: Here is a fitted model from the Bangladesh analysis predicting whether a person with high-arsenic drinking water will switch wells, given the arsenic level in their existing well and the distance to the nearest safe well. glm(formula = switch ~ dist100 + arsenic, family=binomial(link=”logit”)) coef.est coef.se (Intercept) 0.00 0.08 […]
Still at work on the piranha theorems
We’re still at work on the piranha theorems. But, in the meantime, I happened to show somebody this: There can be some large and predictable effects on behavior, but not a lot, because, if there were, then these different effects would interfere with each other, and as a result it would be hard to see […]
Question 2 of our Applied Regression final exam (and solution to question 1)
Here’s question 2 of our exam: 2. A multiple-choice test item has four options. Assume that a student taking this question either knows the answer or does a pure guess. A random sample of 100 students take the item. 60% get it correct. Give an estimate and 95% confidence interval for the percentage in the […]
Question 1 of our Applied Regression final exam
As promised, it’s time to go over the final exam of our applied regression class. It was an in-class exam, 3 hours for 15 questions. Here’s the first question on the test: 1. A randomized experiment is performed within a survey. 1000 people are contacted. Half the people contacted are promised a $5 incentive to […]
New! from Bales/Pourzanjani/Vehtari/Petzold: Selecting the Metric in Hamiltonian Monte Carlo
Ben Bales, Arya Pourzanjani, Aki Vehtari, Linda Petzold write: We present a selection criterion for the Euclidean metric adapted during warmup in a Hamiltonian Monte Carlo sampler that makes it possible for a sampler to automatically pick the metric based on the model and the availability of warmup draws. Additionally, we present a new adaptation […]