Chapter 5 regression PDF

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Linear regression Linear regression is mainly used for predicting and forecasting values based on historical information. Regression is a supervised machine-learning technique to identify the linear relationship between target variables and explanatory variables. It is used for predicting the target variable values in numeric form. Variables that are going to be predicted are considered as target variables and the variables that are going to help predict the target variables are called explanatory variables. With the linear relationship, we can identify the impact of a change in explanatory variables on the target variable. In mathematics, regression can be formulated as follows: y = ax +e x and y are variables that form a dataset and N is the total numbers of values. Applications of linear regression include: • Sales forecasting • Predicting optimum product price • Predicting the next online purchase from various sources and campaigns

Linear regression with R Now we will see how to perform linear regression in R. We can use the in-built lm() method to build a linear regression model with R. Model...