Linear Regression
Linear regression is the process of fitting a straight line to a data set. A linear function has the form y = ax + b. Hence, the task of linear regression is to determine values for a and b that create the straight line that best fits the given data. For example, we consider a data set from Sullivan and Sullivan, Precalculus enhanced with graphing utilities, Second Edition, page 112, Example 3.

First we enter the data set into the calculator. In the TI-85 we enter the input data (here, fertilizer) as xStat and the output data (here, yield) as yStat.

Step 1: Data Entry

1. Press 2. Press to select EDIT. 3. If needed, use the arrow keys to position the cursor after xlist Name=. If xlist Name=xStat, press ; otherwise, Press to select xStat, then press . If ylist Name=yStat, press ; otherwise, Press to select yStat, then press . 4. If needed, clear any old data by pressing to select CLRxy. Enter the data. For each data entry, type in a number, then press . Here, x1 = 0, y1 = 4, x2 = 0, y2 = 6, x3 = 5, y3 = 7, etc. 5. When data entry is complete, press twice to return to the home screen.

Step 2: Plotting the Data Set

1. Press . 2. Press to select RANGE. Enter an appropriate viewing window. Here, xMin = -1, xMax = 26, xScl = 5, yMin = 0, yMax = 25, yScl = 5 work well. After the range is set, press to return to the home screen. 3. Press . Then press to select DRAW. 4. Press to select SCAT (scatter plot). After the scatter plot appears, then press . Note If an extraneous graph appears with the data set, press to select y(x)= and clear any functions present (use the arrow keys to highlight the function, then press ). Then and reDRAW the graph.

Step 3: The Calculations
Now we fit a straight line through the data points. Of course, two points uniquely determine a straight line. Although our twelve points don't fit exactly on any straight line, the TI-85 has a built in feature called linear regression that determines the straight line that best fits the data. In statistics, this line of best fit is called a regression line.

1. Press to select CALC. Press twice to select xlist Name=xStat and ylist Name=yStat. 2. Press to select LINR (linear regression).

The screen displays the results of the linear regression: the regression equation is y = a+bx with y-intercept a = 4.7857 and slope b = .7171. We can report that the average rate of change of crop yield is .7 bushels for each pound of fertilizer applied per 100 square feet.

Step 4: Plotting the Data and Regression Curve
Since the plot setup has already been done in viewing the data set, we are ready to graph.

1. Press , then press to select DRAW. 2. Press to select DRREG (draw regression). Press . You now have a nice view of the scatter plot with the regression line.

Nonlinear Regression

To illustrate obtaining a function from data, we utilize a data set for the growth of the brewers' yeast, Saccharomyces cerevisiae, obtained by the biologist Tor Carlson in 1913. Here, time is measured in hours and population in biomass units. First enter the data set. Press to return to the home screen. Then press , then to select EDIT. Press to retain xlist Name=xStat, then press again to retain ylist Name=yStat. To clear the old data, press to select CLRxy. Now, enter the new data set. After the data are entered and checked, then we are ready to view the scatter plot.

1. Press . 2. Press to select RANGE so we can set an appropriate viewing window for the data. Here, we suggest xMin = -1, xMax = 8, xScl = 1, yMin = -25, yMax = 275, yScl = 25 (these settings give a border to the scatter plot so that the lower menus don't obstruct the view). Then press to return to the home screen. 3. Press , press to select DRAW, press to select SCAT (scatter plot), then press . You now have a view of the scatter plot.

Step 3: The Calculations
We are now ready to fit a function to the data. First we must decide on a function whose graph looks like a good descriptor for the data. Here, we choose an exponential function. For the TI-85, the general exponential function is y = ab^x. Using exponential regression, the TI-85 will determine the values for a and b that provide a general exponential function of best fit based on the given data.

The calculations and plots are done exactly as in linear regression, with one exception. In the STAT menu, instead of selecting LINR for linear regression, we select EXPR for exponential regression. For convenience, we summarize the calculations and viewing the scatter plot with the regression equation superimposed on the data set.

1. Press to select CALC. 2. Press to select xlist Name=xStat. 3. Press again to and ylist Name=yStat. 4. Press to select EXPR, exponential regression.

The screen now displays the results of the exponential regression: the regression equation is y = ab^x where a = 10.9757 and b = 1.5898.

Step 4: Plotting the Data and Regression Curve
Since the plot setup has already been done in viewing the data set, we are ready to graph.

1. Press . 2. Press to select DRAW. 3. Press to select DRREG. Watch the TI-85 draw the exponential regression line over the scatter plot. 4. When drawing is complete, press to put get a better view.

We now can forecast based on the regression equation. For example, let's use the regression equation to predict the population at time t = 8. Press to select FCST (forecast). Press to obtain x = 8. Then press to select SOLVE. We predict about 448 biomass units after 8 hours of growth.

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