Develop a multiple linear regression model in R to predict the demand for Fry$$s Burger. Use the following information as independent variables:

$-$ Weather: precipitation, temperature, humidity

$-$ Price

$-$ Dummy variables representing various factors: festival presence, type of weekday, and city

$($London$,$ Waterloo, Toronto$)$

Report two models applying the backward stepwise: $1)$ Full model and $2)$ Final model

For the backward stepwise regression process, follow these detailed steps:

$1)$ Start with a full model: Begin by including all the listed independent variables in your regression model.

$2)$ Significance testing: After fitting the full model, examine the p$-$values of all the independent variables.

$3)$ Remove the least significant variable: Identify the variable with the highest p$-$value $($least Significant$)$ that is greater than the $0\mathrm{.}05$ significance level. Remove this variable from your model.

$4)$ Refit the model: with the identified variable removed, refit your regression model.

$5)$ Iterate: Repeat steps $2-4.$ Each time, remove the least significance variable and refit the model.

Continue this process until all remaining variables in the model are significance at the $0\mathrm{.}05$ level.

$6)$ Final model evaluation: Once you have your final set of significant variables, evaluate the

model by looking at the overall fit $($R$-$squared value$)$ and the individual variable coefficients.