Linear Regression practice

The slope (m) represents the rate of change of the dependent variable (y) with respect to the independent variable (x). It indicates how much y changes for a one-unit increase in x.

The y-intercept (b) is the value of y when x is 0. It represents the point where the line crosses the y-axis.

The least-squares method is used to find the best-fitting line by minimizing the sum of the squares of the vertical distances (residuals) between the observed values and the values predicted by the line.

The correlation coefficient (r) measures the strength and direction of a linear relationship between two variables. It ranges from -1 to 1, where 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation.

A correlation coefficient of 0.8 indicates a strong positive linear relationship between the two variables, meaning as one variable increases, the other variable tends to also increase.

The line of best fit is a straight line that best represents the data on a scatter plot. It is determined using linear regression and minimizes the distance between the line and the data points.