![]() Resource: An Introduction to Lasso Regression 6. When multicollinearity is a problem in a dataset, i’s recommended to fit both a Lasso and Ridge regression model to see which model performs best. Note that Lasso regression and ridge regression are quite similar. Thus, multicollinearity is likely to be a problem so we can minimize this problem by using lasso regression. The predictor variables are likely to be highly correlated since individuals who receive more schooling also tend to live in cities with higher costs of living and work more hours. Lasso regression is very similar to ridge regression and is used to fit a regression model that describes the relationship between one or more predictor variables and a numeric response variable.Įxample: An economist may fit a lasso regression model using predictor variables like total years of schooling, hours worked, and cost of living to predict household income. Resource: An Introduction to Ridge Regression 5. Thus, multicollinearity is likely to be a problem so we can minimize this problem by using ridge regression. The predictor variables are likely to be highly correlated since better players tend to get more points, assists, and rebounds. The predictor variables are highly correlated and multicollinearity becomes a problem.Įxample: A basketball data scientist may fit a ridge regression model using predictor variables like points, assists, and rebounds to predict player salary.Ridge regression is used to fit a regression model that describes the relationship between one or more predictor variables and a numeric response variable. Resource: An Introduction to Polynomial Regression 4. Since this relationship between the predictor variable and response variable is nonlinear, it makes sense to fit a polynomial regression model. That is, as hours increases an individual may report higher happiness but beyond a certain number of hours worked, overall happiness is likely to decrease. The relationship between these two variables is likely to be nonlinear. The relationship between the predictor variable(s) and the response variable is non-linear.Įxample: Psychologists may fit a polynomial regression using ‘hours worked’ to predict ‘overall happiness’ of employees in a certain industry.Polynomial regression is used to fit a regression model that describes the relationship between one or more predictor variables and a numeric response variable. Resource: An Introduction to Logistic Regression 3. Since the response variable (heart attack) is binary – an individual either does or does not have a heart attack – it’s appropriate to fit a logistic regression model. The response variable is binary – it can only take on two values.Įxample: Medical researchers may fit a logistic regression model using exercise and smoking habits to predict the likelihood that an individual experiences a heart attack. ![]() Logistic regression is used to fit a regression model that describes the relationship between one or more predictor variables and a binary response variable. Resource: An Introduction to Multiple Linear Regression 2. Since the relationship between these two variables is likely linear (more money spent on advertising generally leads to an increase in sales) and the response variable (total sales) is a continuous numeric variable, it makes sense to fit a linear regression model. The response variable is a continuous numeric variable.Įxample: A retail company may fit a linear regression model using advertising spend to predict total sales.The relationship between the predictor variable(s) and the response variable is reasonably linear.Linear regression is used to fit a regression model that describes the relationship between one or more predictor variables and a numeric response variable. In this article we share the 7 most commonly used regression models in real life along with when to use each type of regression. The basic goal of regression analysis is to fit a model that best describes the relationship between one or more predictor variables and a response variable. Regression analysis is one of the most commonly used techniques in statistics.
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