1. The problem is to find the best fit line for a set of data points using the Least Squares Method. 2. The formula for the best fit line is $y = mx + b$, where $m$ is the slope and $b$ is the y-intercept. 3. The slope $m$ is calculated by $$m = \frac{n\sum xy - \sum x \sum y}{n\sum x^2 - (\sum x)^2}$$ where $n$ is the number of points, $\sum xy$ is the sum of the product of $x$ and $y$ values, $\sum x$ and $\sum y$ are sums of $x$ and $y$ values respectively, and $\sum x^2$ is the sum of squares of $x$ values. 4. The intercept $b$ is calculated by $$b = \frac{\sum y - m \sum x}{n}$$. 5. To solve, first calculate all sums: $\sum x$, $\sum y$, $\sum xy$, and $\sum x^2$ from the data. 6. Substitute these sums into the formulas for $m$ and $b$ to find the slope and intercept. 7. The resulting equation $y = mx + b$ is the line that best fits the data in the least squares sense. 8. This method minimizes the sum of the squares of the vertical distances of the points from the line, providing the best linear approximation.