In a linear regression analysis of diabetes and inactivity, the goal would be to model the relationship between these two variables using a linear equation. The equation takes the form y=mx+b, where
y represents the predicted values of diabetes, x is the level of inactivity, m is the slope (indicating the change in diabetes for a unit change in inactivity), and b is the y-intercept (representing the predicted level of diabetes when inactivity is zero).
For instance, if the linear regression indicates a positive slope, it suggests that as inactivity increases, the predicted level of diabetes also increases. Conversely, a negative slope implies a decrease in predicted diabetes with decreasing levels of inactivity.
and R-squared is the square of the correlation between two variables
we got Slope = 0.23 , intercept = 3.77, R- squared = 0.1915, p- value = 1.63 and standard error = 0.012
