Use linear_model() to fit a linear regression model. The function also calculates standard errors, p-values, the F-statistic & R-squared
linear_model(formula, data)
| formula | an object of class 'formula' or a string that can be coerced to this class. The formula is a symbolic description of the data and should be passed as 'DV ~ IV1 + IV2 + ...' (where DV stands for 'dependent variable' and the IVs are the independent variables). You are also allowed to pass 'DV ~ .' indicating that you will use all all variables in the data as predictors. |
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| data | data frame containing the variables of interest |
list of class 'linear_model' containing:
inputs: user inputs: formula, DV and IV(s), data entered, number of observations (n), number of predictors (m)
summary_statistics: mean, variance, minimum, maximum for each variable. Also contains the number of observations (n) and the degrees of freedom (df)
coefficients: estimators for each predictor in the model obtained from the linear regression model
tests: list containing the results of analysis of the coefficients. Specifically:
coef: a list containing standard errors, t-values and p-values for the estimators.
predicted: predicted values.
residuals: residuals
sums_of_squares: a list containg the sums of squares (total, model, residual sums of squares)
means_of_squares: a list containing the mean sums of squares (total, model and residual mean of squares
f_test: a list containing F-statistic and associated p-value.
R_squared: unajusted R-squared value