Which of the following are true about regression with one predictor variable (often called "simple regression") ? Check all that apply.

A. The slope describes the amount of change in Y for a one-unit increase in X

B. The regression equation is the line that best fits a set of data as determined by having the least squared error

C. The slope, b, of the regression equation has the same value as r, the estimated correlation

A. The slope describes the amount of change in Y for a one-unit increase in X. B. The regression equation is the line that best fits a set of data as determined by having the least squared error.

Step-by-step explanation:

In statistics, linear regression is a analysis we do to describe the relationship between two variables. With this study, we pretend to know if there's a positive or negative correlation between those variables, if that correlation is strong or weak.

In a linear regression analysis, we modeled the data set using a regression equation, which is basically the line that best fits to the data set, this line is like the average where the majority of data falls. That means choice A is right.

When we use linear equations, we need to know its characteristics, and the most important one is the slope, which is the ratio between the dependent variable and the independent variable. Basically, the slope states the unit rate between Y and X, in other words, it states the amount of Y per unit of X. That means choice B is correct.

Therefore, the correct answers are A and B.