1What is the predicted variable in multiple linear regression (MLR)?
A numerical (continuous) value.
2What are the two main objectives of MLR?
To establish if there is a relationship between input variables and the output and to predict new observations.
3How does MLR describe the relationship between variables?
By fitting a line to the observed data to minimize the overall modeling error.
4What does the equation of MLR include?
A dependent variable modeled as a function of several independent variables with corresponding coefficients.
5What is the role of regression coefficients in MLR?
They represent the increase/decrease factor that a change in an independent variable has on the predicted value of the dependent variable.
6What does R-squared represent in MLR?
It measures how well the model accounts for variations in predictions explained by predictor variables.
7When is a predictor variable considered statistically significant?
When the p-value is less than 0.05.
8What is the method used to find the best-fitting line in MLR?
The method of least squares.
9What does a high R-squared value indicate?
That the model has a strong linear association and explains most of the variation in the dependent variable.
10What is the impact of adding more predictor variables to a model?
It increases the complexity and may improve the model’s predictive accuracy.
11What does the least squares approach help with in MLR?
It helps estimate the model coefficients to minimize errors and fit the line of best fit.