Basic Statistics and Data Analysis

Lecture notes, MCQS of Statistics

Tag: Regression

Multiple Regression Introduction

Multivariable / Multiple Regression

Multiple regression (a regression having multi-variable) is referred as a regression model having more than one predictor (independent and explanatory variable) to explain a response (dependent) variable. We know that in simple regression models has one predictor used to explain a single response while for case of multiple (multivariable) regression models, more than one predictor in the models. Simple regression models and multiple (multivariable) regression models can further be categorized as linear or non-linear regression models.

Note that linearity does not based on predictors or addition of more predictors in simple regression model, it is referred to the parameter of variability (parameters attached with predictors). If the parameters of variability having constant rate of change then the models are referred to as linear models either it is a simple regression model or multiple (multivariable) regression models. It is assumed that the relationship between variables is considered as linear, though this assumption can never be confirmed for case of multiple linear regression. However, as a rule, it is better to look at bivariate scatter diagram of the variable of interests, you check that there should be no the curvature in the relationship.

Multiple regression also allows to determine the overall fit (which is known as variance explained) of the model and the relative contribution of each of the predictors to the total variance explained (overall fit of the model). For example, one may be interested to know how much of the variation in exam performance can be explained by the following predictors such as revision time, test anxiety, lecture attendance and gender “as a whole”, but also the “relative contribution” of each independent variable in explaining the variance.

A multiple regression model have the form

\[y=\alpha+\beta_1 x_1+\beta_2 x_2+\cdots+\beta_k x_k+\varepsilon\]

Here y is continuous variables, x’s are known as predictors which may be continuous, categorical or discrete. The above model is referred to as a linear multiple (multivariable) regression model.

For example prediction of college GPA by using, high school GPA, test scores, time gives to study and rating of high school as predictors.

 

 

How is the regression coefficient interpreted in simple regression?

The basic or unstandardized regression coefficient is interpreted as the predicted change in Y (i.e., the DV) given a one unit change in X (i.e., the IV). It is in the same units as the dependent variable.

  • Note that there is another form of the regression coefficient that is important: the standardized regression coefficient. The standardized coefficient varies from –1.00 to +1.00 just like a simple correlation coefficient;
  • If the regression coefficient is in standardized units, then in simple regression the regression coefficient is the same thing as the correlation coefficient.
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