Please respond from this discussion. Just need a discussion response from this discussion.

Regression and Analysis of Variance (ANOVA) were discussed this week. Regression helps determine the relationship between our input and our output. Regression can also be linear or nonlinear. Linear equations are much simpler to solve because it forms or represents a straight line and is represented by the equation y=mx+b. X is the input, y is the output, m is the slope, and b is the y-intercept. An example I found was using height as the independent variable and body weight as the dependent variable. "In this simple linear regression, we are examining the impact of one independent variable on the outcome. If height were the only determinant of body weight, we would expect that the points for individual subjects would lie close to the line. However, if there were other factors (independent variables) that influenced body weight besides height (e.g., age, calorie intake, and exercise level), we might expect that the points for individual subjects would be more loosely scattered around the line, since we are only taking height into account" (LaMorte, 2016). Analysis of Variance is used to analyze the differences between the mean values of two interdependent groups. A simple way to do this in the clinic I work would be to use it to analyze the patient's blood pressure before and after they started/used medication.