Linear regression is a fundamental statistical and machine learning technique that attempts to model the relationship between two variables by fitting a linear equation to observed data. The steps to perform a linear regression analysis are quite simple and after this comprehensive guide, you will be able to understand how the method works, what the underlying assumptions are, how to interpret its results, and how to use it for predictions.
The term “linear” refers to the relationship between the predictor and response variables in the model. It does not necessarily mean that the change in the response variable per unit change in the predictor is constant. Linear regression can model relationships where the rate of change in the response variable varies with the predictor.
Understanding the Basics of Linear Regression
Before diving into the complexities of linear regression, it’s important to understand the basic principles that underpin this method. Linear regression is a form of predictive modelling technique which investigates the relationship between a dependent and independent variable.
Linear regression uses the relationship between the data-points to draw a straight line through all them. This line can be used to predict future values. It works by assigning optimal weights to the variables in order to create a line (hence, linear) that will minimizes the total distance between the line and the actual data points.
Simple Linear Regression
Simple linear regression is the most basic form of linear regression. It involves a single independent variable and a single dependent variable. The dependent variable is what you want to predict or explain, while the independent variable is what you want to use to predict the dependent variable.
In simple linear regression, we predict the output/dependent variable based on only one input/independent variable. The relationship between the two variables is said to be linear if the change in the dependent variable is constant relative to the change in the independent variable.
Multiple Linear Regression
Multiple linear regression is a bit more complex than simple linear regression, as it involves multiple independent variables. Therefore, it is used when we want to predict the value of a variable based on the value of two or more other variables.
The variable we want to predict is called the dependent variable (or sometimes, the outcome, target or criterion variable). The variables we are using to predict the value of the dependent variable are called the independent variables (or sometimes, the predictor, explanatory or regressor variables).
Assumptions of Linear Regression
Linear regression analysis, like all statistical techniques, makes certain assumptions in order to be valid. These assumptions are essentially conditions that should be met before we draw inferences regarding the model estimates or before we use a model to make a prediction.
Many of these assumptions relate to the residuals, or the leftovers, after fitting a model. The residuals are the differences between the observed and predicted values of the response variable. Here are the key assumptions:
Linearity
The relationship between the independent and dependent variable is linear. This can be checked by creating a scatter plot of the independent variable against the dependent variable and looking for a linear pattern in the data points.
If the relationship displayed in your scatter plot is not linear, you will have to either run a non-linear regression analysis or “transform” your data, which is beyond the scope of this article.
Independence
The residuals are independent, meaning that the residuals are not correlated. If you have time series data, you should expect the residuals to be correlated over time and you will need to use a time series regression model.
Independence can be checked by creating a residual plot against the time order of the data (i.e., the order that the data was collected). The residuals should be randomly and evenly scattered around the zero line.
Interpreting Linear Regression Coefficients
One of the most important steps in using linear regression is interpreting the regression coefficients. The coefficients describe the mathematical relationship between each independent variable and the dependent variable. The coefficients are interpreted differently depending on whether the independent variable is categorical or continuous.
A continuous variable is a variable that has an infinite number of possible values. In other words, any value is possible for the variable. A categorical variable, on the other hand, is a variable that has a limited number of possible values.
Interpreting Coefficients of Continuous Variables
The coefficients of continuous variables are interpreted as the change in the dependent variable for a one unit change in the independent variable, assuming all other variables are held constant.
For example, if a coefficient was 1.5, we would say that for every one unit increase in the independent variable, the dependent variable would increase by 1.5, assuming all other variables in the model are held constant.
Interpreting Coefficients of Categorical Variables
Categorical variables are variables that can be divided into multiple categories but having no order or priority. An example is gender, which can be categorized into male and female without any order.
In regression with categorical variables, we compare the mean of the dependent variable for different categories. If we have a coefficient of 1.5, we can say that the mean of the dependent variable for one category is 1.5 units higher than the reference category, assuming all other variables in the model are held constant.
Using Linear Regression for Prediction
One of the primary uses of linear regression is to predict future values. Linear regression can be used to predict the value of one variable based on the value of another, as long as the relationship between the two variables is linear.
When using linear regression for prediction, it’s important to remember that the prediction is only an estimate and will not be exact. There is always some error associated with any prediction, and this error can be larger or smaller depending on the specifics of your data and the model you have fit.
Confidence Intervals for Predictions
When making a prediction, it’s not enough to just give a single number. We also want to have an idea of how certain we are of that prediction. This is where confidence intervals come in. A confidence interval gives an estimated range of values which is likely to include an unknown population parameter.
For a prediction, the confidence interval gives a range of values for our prediction with a specified level of confidence. For example, a 95% confidence interval means that we can be 95% certain that the true value lies within this range.
Limitations of Predictions
While linear regression is a powerful tool for prediction, it’s important to remember that it has limitations. One of the main limitations is that it assumes a linear relationship between the variables. If this assumption is not true, then the predictions may not be accurate.
Another limitation is that the predictions are most accurate for values within the range of the data used to fit the model. Predicting values outside this range (known as extrapolation) can be risky and lead to inaccurate predictions.
Conclusion
Linear regression is a powerful statistical tool that can provide useful insights into data by establishing a mathematical relationship between variables. It’s a fundamental technique in the field of data analysis and is widely used in various fields, including business, healthcare, social sciences, and more.
While it’s a powerful tool, it’s also important to remember that it has its limitations and assumptions. Understanding these will help you interpret the results correctly and use the method effectively.
