Here’s what you should find when performing a linear regression and interpreting the results:

• Line of Best Fit: A straight line that best represents the data, typically written as $y = mx + b$, where $m$ is the slope and $b$ is the y-intercept.
• Slope: Indicates how much the dependent variable changes for a one-unit change in the independent variable; shows the strength and direction of the relationship.
• Intercept: The value of the dependent variable when the independent variable is zero; gives context to the starting point of the regression.
• R-squared ( $R^2$ ): Measures how well the regression line fits the data, with values closer to 1 indicating a better fit.
• P-value: Determines the statistical significance of the relationship between variables, with values below 0.05 generally indicating a significant relationship.

Would you like more details or have any questions?

Here are 5 related questions:

1. How do I calculate the slope and intercept from the data set?
2. What does a low R-squared value indicate about the fit?
3. How can I test for multicollinearity in a regression model?
4. What is the difference between simple and multiple linear regression?
5. How do outliers affect the regression results?

Tip: Always plot your data and the regression line to visually assess the fit and spot any anomalies.