linreg-class: A Reference Class to represent the regression model.
In mpirmoradiyan/lab4a: Multiple Regression Model

A Reference Class to represent the regression model.

`formula`	A formula indicating the dependent and independent variables.
`data`	The data set from which values of observations are extracted based on the formula specification, for building the regression model.

formula: A character object representing the formula.
data: A string representing the name of the data set implemented to build the model.
Coeffficients: A named numeric vector representing the regressin coefficients of the model parameters.
Fitted_Values: A matrix containing the predicted values of the model.
Residuals: A matrix consisting the residual values of the models.
Standard_Residuals: a matrix containing the standardized residuals.
Sqrt_Standard_Residuals: A matrix including sqrt of the standardized residuals.
degrees_of_freedom: A numeric representing the degrees of freedom.
Residual_Variance: A matrix consisting the variance of the residuals.
Coefficients_Variance: A numeric vector representing the variances of the coefficients.
t_value: A numeric vector representing the t-value.
p-value: A numeric vector representing the p-value.

coef(): Returns the coefficients.
initialize(formula, data): Initializing Function. A function to create a multiple linear regression model.
plot(): Plots two different plots. The fiitted values vs the residuals, and the fiitted values vs square root of standardized residuals.
pred(): Returns a the predicted values.
print(): Prints out the coefficients and coefficient names, in addition to the call of the function.
resid(): Returns a vector of residual values.
summary(): Returns the coefficients with their standard error, t-value and p-value as well as the estimate of standard error and the degrees of freedom in the model.