1. 1.6 Modeling Real-World Data with Linear Functions
Objectives: Draw and analyze scatterplots, write a prediction equation and draw best-fit lines, use a graphing calculator to compute correlation coefficients to determine goodness of fit, and solve problems using prediction equation models.

2. Scatterplot: Prediction Equation: Best-fit-line: See page 38
A visual representation of data
Prediction
Equation:
An equation suggested by the points of a
scatterplot used to predict other points.
Best-fit-line:
The graph of a prediction equation.
See page 38

3. The table summarizes the total U. S
The table summarizes the total U.S. personal income from the years 1986 to Predict personal income in the year 2001
Ex. 1)
Personal Income
Year
Income
($ billion)
1986
3658.4
1987
3888.7
1988
4184.6
1989
4501.0
1990
4804.2
1991
4981.6
1992
5277.2
1993
5519.2
1994
5757.9
1995
6072.1
1996
6425.2
1997
6784.0

4. The degree to which data fits a regression line.
Goodness to fit:
The degree to which data fits a regression line.
Correlation Coefficient:
A value that describes the nature of a set of
data. The more closely the data fit a line,
the closer the correlation coefficient, r,
approaches 1 or -1.
See pg. 40
Regression
Line:
A best-fit line

5. Food Carbohydrates (g) calories
The table contains the carbohydrate and calorie content of 8 foods, ranked according to carbohydrate content.
Ex. 2)
Food
Carbohydrates (g)
calories
Cabbage
1.1 9
Peas
4.3 41
Orange
8.5 35
Apple
11.9 46
Potatoes
19.7 80
Rice
29.6
123
White bread
49.7
233
Whole wheat flour
65.8
318
a.) Find an equation with a calculator
b.) Predict the number of calories in a food with 75.9 grams of carbohydrates.