Yujin Kim, PhD
The aim of this task is to predict the annual cooling degree days (CDDs) in the UK, using the R programming language [1] in RStudio [2]. A simple linear regression model was built to represent the relationship between the CDDs and annual count of hot summer days. A multiple linear regression model was also created to estimate the correlation between CDDs, hot summer days and latitudes. Same dataset from Section - ArcGIS was used for this task.
Prediction Using R Programming Language
R – Linear Regression Modelling
Aim
R Code
Fig. 1.1 displays code that I wrote to build linear regression models by referencing the R manuals [3].
# Estimate a coefficient for Hot_summer (an independent variable) and COOLING (a dependent variable).
# Build a simple linear regression model.
# Represent a summary of the model.
# Represent a Residual Standard Error (RSE).
# Plot the graph - simple linear regression model.
# R-squared value of the simple linear regression model.
# Estimate the coefficient of Latitude (an independent variable) and COOLING (the dependent variable).
# Build a multiple linear regression model using Hot_summer and Latitude (the independent variables), as well as COOLING (the dependent variable).
# Represent a Residual Standard Error (RSE).
# R-squared value of the multiple linear regression model.
Fig. 1.1. R code for linear regression models.
References
[1] The R Project for Statistical Computing,
https://www.r-project.org/.
[2] RStudio Desktop,
https://posit.co/download/rstudio-desktop/.
[3] The R Manual,
https://cran.r-project.org/manuals.html.