The correct answer is B. Only 1.
Linear regression is a statistical method that uses a linear equation to predict the value of a dependent variable based on the values of one or more independent variables. The linear equation is typically written in the form $y = mx + b$, where $y$ is the dependent variable, $x$ is the independent variable, $m$ is the slope of the line, and $b$ is the y-intercept.
Logistic regression is a statistical method that is used to predict the probability of a binary outcome. The outcome can be either “success” or “failure”, or “yes” or “no”. Logistic regression uses a logistic function to model the relationship between the independent variables and the probability of the outcome.
Both linear regression and logistic regression are used for prediction, but they are used for different types of data. Linear regression is used for continuous data, while logistic regression is used for binary data.
In the case of predicting a continuous dependent variable, linear regression is the appropriate method to use. Logistic regression would not be appropriate because it is designed for binary data.