You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
This notebook creates a linear regression model and related statistics by hand using the underlying mathematical formulas and a sample dataset. It then creates another linear regression model and related statistics using python libraries and the same sample dataset. Both methods include calculating the equation of a line $y = mx+b$, the line's slope ($m$) and y-intercept ($b$), the coefficient of determination R-squared ($r^2$), and Pearson's correlation coefficient ($r$). The results of the two modeling methods are then compared and summarized.
Highlights
Both models produced the same linear regression line $y = mx+b$, slope ($m$) and y-intercept ($b$), the coefficient of determination R-squared ($r^2$), and Pearson's correlation coefficient ($r$).