GGR376: Regression Produce an Explanatory Regression Model - IT Assignment Help

Assignment Task:

Task:

Regression:

Modelling the relationship between a response (or dependent variable) and one or more explanatory variables (or independent variables). linear regression is a linear approach to modelling the relationship.
Before completing the assignment, review the example R markdown document from Tutorial 4.
 

NOTE: Join the spatial data at the beginning, as it causes issues to do it at the end.
 

Research Problem:
Produce an explanatory regression model for the variation in housing costs by census tract in the City of Hamilton, Ontario, Canada.
 

Data:
Hamilton Census Tract boundaries, which includes the average house price and the unique identifier: CTUID.
You can access the data with the following command and URL: library(rgdal) rgdal::readOGR("https://raw.githubusercontent.com/gisUTM/GGR376/master/Lab_1/houseValu es.geojson")
You will need to obtain 10 potential explanatory variables from the 2016 Census Data, available from CHASS: http://dc2.chass.utoronto.ca.myaccess.library.utoronto.ca/census/
 

Assignment Format:
The assignment submission will be composed of three files.
 

1. An R script of your code produced during the project, with the .R file extension.
2. A CSV file of the additional input data you utilized in your model (one table).
3. Answers to the questions listed below in a PDF file.
All three files must be submitted online.
 

Assignment Requirements:
• Ensure all procedures from the lab tutorial are replicated in your work.
• Fit and test 10 linear regression models.
o Example model names: model_1, model_2, etc.

o All models should remain in the code.
o Rename your final model: final_model
• The final model must meet all assumptions with the possible exemption:
o Independent errors due to spatial autocorrelation.
? Validate the independent errors assumption in your model with spatial autoregressive modelling.

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