In section 3.6 of your text you learned how to find the least squares best approximation line for a collection of data pairs.  Another name for this line is called the linear regression line.  The "Graphing Calculator Exploration" on p. 247 shows one way of using your calculator to find this line.  Your TI graphing calculator also has built-in routines to determine this linear regression line for a set of data.  To complete this assignment, you may choose to use either of the techniques.  Please follow the links below to find instructions on how to use the built-in linear regression routines on your calculator:
Regression Analysis on the TI-86,  Regression Analysis on the TI-85,
Regression Analysis on the TI-82,  Regression Analysis on the TI-83.

Preparation:  Reread the Application Preview on pages 2 and 3 of your text.

Practice:  Try problem #75 on page 16 of your text.  Verify your answer with the correct answer in the back of the text.

Assignment:  The Energy Information Administration (http://www.eia.doe.gov/aer) gives the following data for U.S. residential electricity end use for the years given in the table below.  The data represents residential electric utility retail sales in billion kilowatt-hours.
 

year	1950	1955	1960	1965	1970	1975	1980	1985	1990	1995	2000
Electricity	72	128	201	291	466	588	717	794	924	1043	1192

1.  Enter the data into your calculator and make a plot of the resulting points, with years since 1950 on the x-axis.

2.  Use your calculator to find the linear regression line for these points.  Graph the points together with the regression line. Sketch or use a computer to make a detailed graph of the points and the line to hand in.  Don't forget to label the axes and provide units and a scale on each axis.  Label the line with it's equation.

3.  Use your line to estimate the residential electric utility retail sales for 2001 and 2005.  When will the residential electric utility retail sales reach 1500 billion kilowatt-hours?