BT Question 20.20.3 20.20.3. Patrick generated a simple regression line for a sample of 50 pairwise observations. After generating the regression model, he ran R’s built-in plot(model) function which produces a standard set of regression diagnostics.

P1.T2.20.19. Regression diagnostics: omitted variables, heteroskedasticity, and multicollinearity Question 1: Fama-french 2-factor <- omitted variable 20.19.1. Jane manages a market-neutral equity fund for her investment management firm. The fund’s market-neutral style implies (we will assume) that the fund’s beta with respect to the market’s excess return is zero.

Multiple regression Question 1: Fama-french library(tidyverse) library(broom) library(gt) intercept <- .03 intercept_sig <- .01 x1_mu <- .04 x1_sig <- .01 x1_beta <- 0.4 x2_mu <- .03 x2_sig <- .01 x2_beta <- -0.

Learning objectives Construct, apply, and interpret hypothesis tests and confidence intervals for a single regression coefficient in a regression. Explain the steps needed to perform a hypothesis test in a linear regression.

20.16.3. Sally works at a real estate firm and was asked by her client to quantify the relationship between rental size (in square feet) and rental price. She explained to her client that the relationship is multivariate but, given that caveat, she offered to perform a linear regression with a single explanatory variable.

20.16.2. Peter is an analyst who is evaluating an investment fund whose managers claim has outperformed their benchmark. He collected monthly returns for the last five years; i.e., the sample size is excess return pairs over n = 60 months.

Background BT is known for our tough training-style practice questions, but I wanted to take it further and add more realism. I’ve been writing a fresh question sets on the regression topics; I’m always writing new questions!

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