In this article, we will learn the concept of linear regression. We will introduce what is Regression. What is Linear Regression? What is the use of Regression in ML as well as the basic terminology of Linear Regression?
Table of Contents
Concept of Regression
Regression is a statistical technique used to analyze the relationship between a dependent variable and one or more independent variables. In simple language, it helps to predict the value of the dependent variable (Y) based on the Independent variable (X).
The dependent variable is the variable you are trying to predict, while the independent variables are the variables that are used to predict. For example, in a study looking at the relationship of cricket between the ball and score, the ball is the independent variable, while the how much score earned is the dependent variable.
Regression analysis involves fitting a mathematical model to the data that describes the relationship between the dependent variable and independent variables. The most common type of regression analysis is linear regression, which assumes a linear relationship between the dependent and independent variables.
Regression analysis is widely used in many fields, including economics, finance, marketing, psychology, medicine, etc.
Regression – Regression is the study of numerical value. Regarding Machine Learning or Linear Regression, Regression is defined as variable Y, the dependent variable.
Concept of Linear Regression
Linear Regression – Linear Regression is the statistical model of Machine Learning to find out the Regression value (Y) (dependent variable) based on the independent variable (X).
The dependent variable (Y) is considered as Output and the Independent variable (X) is the Input. As a result, it is a type of regression analysis where a linear equation is fitted to the data to estimate the values of the dependent variable based on the values of the independent variables.
In simple terms, linear regression is used to find a straight line that best fits the data, where the line represents the relationship between the independent variable(s) and the dependent variable. The linear equation takes the form:
Y = mX + b, where Y is the dependent variable, X is the independent variable, m is the estimated slope, and b is the estimated intercept.
The goal of linear regression is to find the values of the coefficients that minimize the sum of the squared differences between the observed values of the dependent variable and the values predicted by the linear equation. This is done using a method called least squares estimation.
Linear regression is widely used in many fields, such as economics, finance, marketing, and science, to analyze and predict the relationship between variables.
Examples of Simple Linear Regression
- Students’ grades are based on the number of hours they study – Here, exam grades depend on the number of hours they study.
- Crop yields can be predicted using rainfall data – Yield is a dependent variable, while rainfall data is an independent variable.
- Predicting a person’s salary based on years of experience – Experience becomes the independent variable, while Salary becomes the dependent variable.
Linear Regression: Important Terms
Linear regression is a statistical technique used to study the relationship between two variables by fitting a linear equation to observed data. Here are some important terms related to linear regression:
- Dependent variable: The variable being predicted or explained by the independent variable(s).
- Independent variable: The variable(s) that can be used to predict or explain the dependent variable.
- Slope: The rate of change in the dependent variable for every unit change in the independent variable.
- Intercept: The point at which the regression line intersects the y-axis.
- Residuals: The differences between the observed values of the dependent variable and the predicted values by the regression line.
- Regression line: The line that best fits the observed data points.
- Coefficient of determination (R-squared): A measure of the proportion of the variation in the dependent variable that can be explained by the independent variable(s).
- Ordinary Least Squares (OLS): A method for estimating the coefficients of a linear regression model by minimizing the sum of squared residuals.
- Multicollinearity: The phenomenon where two or more independent variables are highly correlated, making it difficult to determine the unique contribution of each variable to the dependent variable.
- Heteroscedasticity: The phenomenon where the variance of the residuals is not constant across the range of the independent variable(s).
- Linear Regression: Mean Absolute Error
In linear regression, the Mean Absolute Error (MAE) is a measure of the average absolute difference between the predicted values and the actual values. It is a common metric used to evaluate the performance of a regression model.
The formula for calculating MAE is:
MAE = (1/n) * ∑|yi – ŷi|
where n is the number of observations, yi is the actual value of the dependent variable, and ŷi is the predicted value of the dependent variable.
The MAE is a good metric for linear regression when there are outliers in the data, as it gives equal weight to all errors, regardless of their direction. However, it does not penalize large errors more than small errors.
A lower MAE value indicates that the model is performing better, as it means that the average absolute difference between the predicted values and the actual values is lower.
Linear Regression: Mean Squared Error
In linear regression, the Mean Squared Error (MSE) is a measure of the average squared difference between the predicted values and the actual values. It is a common metric used to evaluate the performance of a regression model.
The formula for calculating MSE is:
MSE = (1/n) * ∑(yi – ŷi)²
where n is the number of observations, yi is the actual value of the dependent variable, and ŷi is the predicted value of the dependent variable.
The MSE is a good metric for linear regression when there are no significant outliers in the data, as it penalizes large errors more than small errors. However, it does not give equal weight to all errors, as large errors are weighted more heavily.
A lower MSE value indicates that the model is performing better, as it means that the average squared difference between the predicted values and the actual values is lower. The square root of the MSE is known as the Root Mean Squared Error (RMSE), which is also a commonly used metric for evaluating the performance of a regression model.
Linear Regression: R Squared
In linear regression, the coefficient of determination, commonly known as R-squared, is a statistical measure that represents the proportion of the variance in the dependent variable that is explained by the independent variable(s).
The R-squared value is a number between 0 and 1, with higher values indicating a better fit of the model to the data. An R-squared value of 1 indicates that the model perfectly fits the data, while a value of 0 indicates that the model does not explain any of the variations in the dependent variable.
The formula for calculating R-squared is:
R² = 1 – (SSres / SStot)
where SSres is the sum of squared residuals (the difference between the predicted and actual values), and SStot is the total sum of squares (the difference between each data point and the mean of the dependent variable).
R-squared can be used to determine the goodness of fit of the model, and to compare different models. However, it is important to note that a high R-squared value does not necessarily mean that the model is a good predictor of the dependent variable, as it may overfit the data. Therefore, it is important to also consider other metrics, such as the Mean Squared Error and the Mean Absolute Error, when evaluating the performance of a regression model.
Linear Regression: Adjusted R-Squared
In linear regression, the Adjusted R-squared is a modification of the R-squared value that adjusts for the number of independent variables in the model. Unlike R-squared, the Adjusted R-squared value can decrease if additional independent variables are added to the model that does not improve the model’s fit.
The formula for calculating Adjusted R-squared is:
Adjusted R² = 1 – [(1 – R²) * (n – 1) / (n – k – 1)]
where n is the number of observations, and k is the number of independent variables in the model.
The Adjusted R-squared value takes into account the number of independent variables in the model and adjusts the R-squared value accordingly. This helps to avoid overfitting, as it penalizes the inclusion of additional independent variables that do not significantly improve the model’s fit.
A higher Adjusted R-squared value indicates a better fit of the model to the data, as it means that a larger proportion of the variance in the dependent variable is explained by the independent variable(s). However, it is important to also consider other metrics, such as the Mean Squared Error and the Mean Absolute Error, when evaluating the performance of a regression model.
Applications of Simple Linear Regression
Linear regression is a widely used statistical technique that has numerous applications in today’s world. It is a powerful tool for modeling the relationship between a dependent variable and one or more independent variables. Here are a few examples of how linear regression is used in various industries:
- Finance: Linear regression is used in finance to analyze the relationship between stock prices and various economic factors such as interest rates, inflation, and GDP growth. It can also be used to predict the price of a security based on its historical data.
- Marketing: Linear regression is used in marketing to identify the key factors that influence consumer behavior and purchasing decisions. It can be used to determine the impact of advertising and promotional activities on sales and to optimize pricing strategies.
- Healthcare: Linear regression is used in healthcare to analyze the relationship between patient outcomes and various factors such as age, gender, medical history, and treatment protocols. It can also be used to predict the likelihood of a patient developing a certain condition based on their demographic and medical data.
- Engineering: Linear regression is used in engineering to model the relationship between various physical parameters such as temperature, pressure, and flow rate. It can also be used to predict the performance of a system based on its design parameters.
- Sports: Linear regression is used in sports to analyze the relationship between various factors such as player statistics, team performance, and playing conditions. It can be used to predict the outcome of a game or tournament based on historical data.
Overall, linear regression is a versatile and powerful tool that has numerous applications in various industries. It can be used for predictive modeling, trend analysis, and decision-making, making it an essential tool for many data-driven organizations.
Conclusion
In conclusion, linear regression is a statistical technique widely used in various industries to model the relationship between a dependent variable and one or more independent variables. It is a powerful tool for predictive modeling, trend analysis, and decision-making and has numerous applications in finance, marketing, healthcare, engineering, sports, and many other fields.
Linear regression models can be evaluated using metrics such as mean squared error, mean absolute error, R-squared, and adjusted R-squared. These metrics help to determine the model’s goodness of fit and compare different models.
However, it is important to note that linear regression has certain assumptions that must be met for the model to be accurate. These assumptions include linearity, independence, normality, and homoscedasticity. Violation of these assumptions can lead to inaccurate results.
Overall, linear regression is a valuable tool for data analysis & decision-making and is likely to continue to be widely used in many industries.
