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Arwell Nathán Leyva Chávez

Arwell Nathán L.

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Robust linear regression using the $t$ model: The folder congress has the votes for the Democratic and Republican candidates in each U.S. congressional district in 1988, along with the parties' vote proportions in 1986 and an indicator for whether the incumbent was running for reelection in 1988. For your analysis, just use the elections that were contested by both parties in both years. (a) Fit a linear regression (with the usual normal-distribution model for the errors) predicting 1988 Democratic vote share from the other variables and assess model fit. (b) Fit a t-regression model predicting 1988 Democratic vote share from the other variables and assess model fit; to fit this model in $R$ you can use the tlm () function in the hett package. (See the end of Section C.2 for instructions on loading R. packrges.) (c) Which model do you prefer?

Data Analysis Using Regression and Multilevel/Hierarchical Models

Robust linear regression using the t model: The folder Congress has the votes for the Democratic and Republican candidates in each U.S. congressional district in 1988, along with the parties' vote proportions in 1986 and an indicator for whether the incumbent was running for reelection in 1988. For your analysis, just use the elections that were contested by both parties in both years. (a) Fit a linear regression using stan_glm with the usual normal-distribution model for the errors predicting 1988 Democratic vote share from the other variables and assess model fit. (b) Fit the same sort of model using the bres package with a $t$ distribution, using the brm function with the student family. Again assess model fit. (c) Which model do you prefer?

Regression and Other Stories

Robust regression for binary data using the robit model: Use the same data as the previous example with the goal instead of predicting for each district whether it was won by the Democratic or Republican candidate. (a) Fit a standard logistic or probit regression and assess model fit. (b) Fit a robit regression and assess model fit. (c) Which model do you prefer?

Regression and Other Stories

Questions asked

ANSWERED

Farruh Turgunov verified

Numerade educator

Robust regression for binary data using the robit model: Use the same data as the previous example with the goal instead of predicting for each district whether it was won by the Democratic or Republican candidate. (a) Fit a standard logistic or probit regression and assess model fit. (b) Fit a robit regression and assess model fit. (c) Which model do you prefer?

View Answer
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ANSWERED

Farruh Turgunov verified

Numerade educator

Robust linear regression using the t model: The folder congress has the votes for the Democratic and Republican candidates in each U.S. congressional district in 1988, along with the parties’ vote proportions in 1986 and an indicator for whether the incumbent was running for reelection in 1988. For your analysis, just use the elections that were contested by both parties in both years. (a) Fit a linear regression (with the usual normal-distribution model for the errors) predicting 1988 Democratic vote share from the other variables and assess model fit. (b) Fit a t-regression model predicting 1988 Democratic vote share from the other variables and assess model fit; to fit this model in R you can use the tlm() function in the hett package. (See the end of Section C.2 for instructions on loading R packages.) (c) Which model do you prefer?

View Answer
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