R

T2-20-25 20.25.1. Over the prior ten months of the calendar year, below is plotted the monthly growth rate of a new cryptocurrency. Two months ago, the growth rate was + 0.

P1.T2.20.22. Stationary Time Series: autoregressive (AR) and moving average (MA) processes Learning objectives Define and describe the properties of autoregressive (AR) processes. Define and describe the properties of moving average (MA) processes.

P1-T2-20-21-3: White Noise (WN) Process) 20.21.3. Barbara was delighted to learn that she can easily simulate white noise in R with a single command. She learned that she can use arima.

20.21.2. Shown below is the autocorrelation function (ACF) for a time series object that contains the total quarterly beer production in Australia (in megalitres) from 1956:Q1 to 2010:Q2 (source: https://cran.

m-fold cross-validation Our question P1-T2-20-2 (located at https://www.bionicturtle.com/forum/threads/p1-t2-20-20-regression-diagnostics-outliers-cooks-distance-m-fold-cross-validation-and-residual-diagnostics.23497/) mimics GARP’s approach in their Chapter 9; in particular their solution 9.14. However, it makes the mistake of using cross-validation to select the regression coefficients.

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.