# Question 2 Simple linear model. # CG Q2a # Build a linear regression model that...
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# Question 2 Simple linear model. # CG Q2a # Build a linear regression model that has ####### count as the response and ####### the weather situation variable as predictor. ####### Name your fitted model simplefit. # CG Q2b # Use the summary() function on simplefit ###### to access the results of the regression. # CG Q2c # Print the coefficient for the wet weather situation. ####### Use coef(simplefit) followed by the name of that ####### coefficient in quotes inside square brackets. ####### For example, coef(simplefit) ["weathersitcloudy"] prints ####### the coefficient for the cloudy weather situation. # CG Q2d # Using the regression output, determine how the ride count ####### for wet days compares to the ride count on clear days. ####### If it's higher on wet days, type paste("higher") ####### If it's lower on wet days, type paste("lower") # CG Q2e # Find the R-squared for the regression. ####### In your calculation, use simplefit$deviance and ####### simplefit$null.deviance and not the numbers printed ####### in the summary output for these. # Question 3 - Linear model with multiple predictors. # CG Q3a # Run a linear regression using ride counts as the response ###### modeled by the weather variables weathersit, temp, hum, and windspeed. ###### Use an additive model (don't model interactions or anything fancy). ###### Name your fitted model ridefit. # CG Q3b # Use summary() on your fitted model to print the results. # CG Q3c # How does expected ride count change with an increase in temperature? ####### Print the coefficient for temp in the ridefit model ####### using the same strategy used in Q2c. # CG Q3d # Based on the temp coefficient, do we expect ride count to increase ####### or decrease by 156 rides with an increase in temperature? ####### If ride count is expected to increase with temperature, ####### type paste("increase"). Otherwise paste("decrease"). # CG Q3e # Find the R-squared for the ridefit regression. ####### In your calculation, use ridefit$deviance and ####### ridefit$null.deviance and not the numbers printed ####### in the summary output for these. # Question 2 Simple linear model. # CG Q2a # Build a linear regression model that has ####### count as the response and ####### the weather situation variable as predictor. ####### Name your fitted model simplefit. # CG Q2b # Use the summary() function on simplefit ###### to access the results of the regression. # CG Q2c # Print the coefficient for the wet weather situation. ####### Use coef(simplefit) followed by the name of that ####### coefficient in quotes inside square brackets. ####### For example, coef(simplefit) ["weathersitcloudy"] prints ####### the coefficient for the cloudy weather situation. # CG Q2d # Using the regression output, determine how the ride count ####### for wet days compares to the ride count on clear days. ####### If it's higher on wet days, type paste("higher") ####### If it's lower on wet days, type paste("lower") # CG Q2e # Find the R-squared for the regression. ####### In your calculation, use simplefit$deviance and ####### simplefit$null.deviance and not the numbers printed ####### in the summary output for these. # Question 3 - Linear model with multiple predictors. # CG Q3a # Run a linear regression using ride counts as the response ###### modeled by the weather variables weathersit, temp, hum, and windspeed. ###### Use an additive model (don't model interactions or anything fancy). ###### Name your fitted model ridefit. # CG Q3b # Use summary() on your fitted model to print the results. # CG Q3c # How does expected ride count change with an increase in temperature? ####### Print the coefficient for temp in the ridefit model ####### using the same strategy used in Q2c. # CG Q3d # Based on the temp coefficient, do we expect ride count to increase ####### or decrease by 156 rides with an increase in temperature? ####### If ride count is expected to increase with temperature, ####### type paste("increase"). Otherwise paste("decrease"). # CG Q3e # Find the R-squared for the ridefit regression. ####### In your calculation, use ridefit$deviance and ####### ridefit$null.deviance and not the numbers printed ####### in the summary output for these. # Question 2 Simple linear model. # CG Q2a # Build a linear regression model that has ####### count as the response and ####### the weather situation variable as predictor. ####### Name your fitted model simplefit. # CG Q2b # Use the summary() function on simplefit ###### to access the results of the regression. # CG Q2c # Print the coefficient for the wet weather situation. ####### Use coef(simplefit) followed by the name of that ####### coefficient in quotes inside square brackets. ####### For example, coef(simplefit) ["weathersitcloudy"] prints ####### the coefficient for the cloudy weather situation. # CG Q2d # Using the regression output, determine how the ride count ####### for wet days compares to the ride count on clear days. ####### If it's higher on wet days, type paste("higher") ####### If it's lower on wet days, type paste("lower") # CG Q2e # Find the R-squared for the regression. ####### In your calculation, use simplefit$deviance and ####### simplefit$null.deviance and not the numbers printed ####### in the summary output for these. # Question 3 - Linear model with multiple predictors. # CG Q3a # Run a linear regression using ride counts as the response ###### modeled by the weather variables weathersit, temp, hum, and windspeed. ###### Use an additive model (don't model interactions or anything fancy). ###### Name your fitted model ridefit. # CG Q3b # Use summary() on your fitted model to print the results. # CG Q3c # How does expected ride count change with an increase in temperature? ####### Print the coefficient for temp in the ridefit model ####### using the same strategy used in Q2c. # CG Q3d # Based on the temp coefficient, do we expect ride count to increase ####### or decrease by 156 rides with an increase in temperature? ####### If ride count is expected to increase with temperature, ####### type paste("increase"). Otherwise paste("decrease"). # CG Q3e # Find the R-squared for the ridefit regression. ####### In your calculation, use ridefit$deviance and ####### ridefit$null.deviance and not the numbers printed ####### in the summary output for these. # Question 2 Simple linear model. # CG Q2a # Build a linear regression model that has ####### count as the response and ####### the weather situation variable as predictor. ####### Name your fitted model simplefit. # CG Q2b # Use the summary() function on simplefit ###### to access the results of the regression. # CG Q2c # Print the coefficient for the wet weather situation. ####### Use coef(simplefit) followed by the name of that ####### coefficient in quotes inside square brackets. ####### For example, coef(simplefit) ["weathersitcloudy"] prints ####### the coefficient for the cloudy weather situation. # CG Q2d # Using the regression output, determine how the ride count ####### for wet days compares to the ride count on clear days. ####### If it's higher on wet days, type paste("higher") ####### If it's lower on wet days, type paste("lower") # CG Q2e # Find the R-squared for the regression. ####### In your calculation, use simplefit$deviance and ####### simplefit$null.deviance and not the numbers printed ####### in the summary output for these. # Question 3 - Linear model with multiple predictors. # CG Q3a # Run a linear regression using ride counts as the response ###### modeled by the weather variables weathersit, temp, hum, and windspeed. ###### Use an additive model (don't model interactions or anything fancy). ###### Name your fitted model ridefit. # CG Q3b # Use summary() on your fitted model to print the results. # CG Q3c # How does expected ride count change with an increase in temperature? ####### Print the coefficient for temp in the ridefit model ####### using the same strategy used in Q2c. # CG Q3d # Based on the temp coefficient, do we expect ride count to increase ####### or decrease by 156 rides with an increase in temperature? ####### If ride count is expected to increase with temperature, ####### type paste("increase"). Otherwise paste("decrease"). # CG Q3e # Find the R-squared for the ridefit regression. ####### In your calculation, use ridefit$deviance and ####### ridefit$null.deviance and not the numbers printed ####### in the summary output for these. # Question 2 Simple linear model. # CG Q2a # Build a linear regression model that has ####### count as the response and ####### the weather situation variable as predictor. ####### Name your fitted model simplefit. # CG Q2b # Use the summary() function on simplefit ###### to access the results of the regression. # CG Q2c # Print the coefficient for the wet weather situation. ####### Use coef(simplefit) followed by the name of that ####### coefficient in quotes inside square brackets. ####### For example, coef(simplefit) ["weathersitcloudy"] prints ####### the coefficient for the cloudy weather situation. # CG Q2d # Using the regression output, determine how the ride count ####### for wet days compares to the ride count on clear days. ####### If it's higher on wet days, type paste("higher") ####### If it's lower on wet days, type paste("lower") # CG Q2e # Find the R-squared for the regression. ####### In your calculation, use simplefit$deviance and ####### simplefit$null.deviance and not the numbers printed ####### in the summary output for these. # Question 3 - Linear model with multiple predictors. # CG Q3a # Run a linear regression using ride counts as the response ###### modeled by the weather variables weathersit, temp, hum, and windspeed. ###### Use an additive model (don't model interactions or anything fancy). ###### Name your fitted model ridefit. # CG Q3b # Use summary() on your fitted model to print the results. # CG Q3c # How does expected ride count change with an increase in temperature? ####### Print the coefficient for temp in the ridefit model ####### using the same strategy used in Q2c. # CG Q3d # Based on the temp coefficient, do we expect ride count to increase ####### or decrease by 156 rides with an increase in temperature? ####### If ride count is expected to increase with temperature, ####### type paste("increase"). Otherwise paste("decrease"). # CG Q3e # Find the R-squared for the ridefit regression. ####### In your calculation, use ridefit$deviance and ####### ridefit$null.deviance and not the numbers printed ####### in the summary output for these. # Question 2 Simple linear model. # CG Q2a # Build a linear regression model that has ####### count as the response and ####### the weather situation variable as predictor. ####### Name your fitted model simplefit. # CG Q2b # Use the summary() function on simplefit ###### to access the results of the regression. # CG Q2c # Print the coefficient for the wet weather situation. ####### Use coef(simplefit) followed by the name of that ####### coefficient in quotes inside square brackets. ####### For example, coef(simplefit) ["weathersitcloudy"] prints ####### the coefficient for the cloudy weather situation. # CG Q2d # Using the regression output, determine how the ride count ####### for wet days compares to the ride count on clear days. ####### If it's higher on wet days, type paste("higher") ####### If it's lower on wet days, type paste("lower") # CG Q2e # Find the R-squared for the regression. ####### In your calculation, use simplefit$deviance and ####### simplefit$null.deviance and not the numbers printed ####### in the summary output for these. # Question 3 - Linear model with multiple predictors. # CG Q3a # Run a linear regression using ride counts as the response ###### modeled by the weather variables weathersit, temp, hum, and windspeed. ###### Use an additive model (don't model interactions or anything fancy). ###### Name your fitted model ridefit. # CG Q3b # Use summary() on your fitted model to print the results. # CG Q3c # How does expected ride count change with an increase in temperature? ####### Print the coefficient for temp in the ridefit model ####### using the same strategy used in Q2c. # CG Q3d # Based on the temp coefficient, do we expect ride count to increase ####### or decrease by 156 rides with an increase in temperature? ####### If ride count is expected to increase with temperature, ####### type paste("increase"). Otherwise paste("decrease"). # CG Q3e # Find the R-squared for the ridefit regression. ####### In your calculation, use ridefit$deviance and ####### ridefit$null.deviance and not the numbers printed ####### in the summary output for these.
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Question 2 Simple linear model CG Q2a Build a linear regression model Python Code import pandas as pd from sklearnlinearmodel import LinearRegression Load the data data pdreadcsvridedatacsv Split the ... View the full answer
Related Book For
Applied Regression Analysis and Other Multivariable Methods
ISBN: 978-1285051086
5th edition
Authors: David G. Kleinbaum, Lawrence L. Kupper, Azhar Nizam, Eli S. Rosenberg
Posted Date:
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