## Introduction

As you've seen in chapter 7, the method of least squares fits a line to a set of (X,Y) data by choosing slope and intercept parameters to minimize SSE, the sum of squared errors. It is a testimony to the power of mathematics that simple formulas exist for calculating the values of the parameters that minimize SSE. Otherwise, we might have to approach the problem by trial and error. In this laboratory you will take the trial and error approach: you will try to visually fit a line to a set of data.

Two methods are provided for obtaining a visual fit. The first method consists of initially drawing a line by choosing two points. This line can then be moved to attempt to minimize the SSE. The second method takes a slightly different approach. The first step is to choose a point that you believe to be
_{}, the means of the x and y values (the least squares line always passes through this point). You will then be able to rotate the line about this point to find the best-fit line.

The residual plot can aid in finding the best-fit line. When plotted against the x values, the residuals should look random. If the residuals from your guessed line display a pattern, you can use the form of the pattern to help make a better guess.

The menu bar on the left side of this window will be available to you at all times in this lab. You can navigate through this lab by clicking on the desired component.

The applets for the two methods mentioned above can be launched from the menu bar by clicking on either Method 1 or Method 2. The applet window will have instructions displayed at the bottom. By using the slide bar or pressing “next” or “previous”, you will be able to navigate through instructions for the lab. Once you complete all instructions for the applet, close the window. If instructed to do so, go on to the summary questions after you are done with the lab.