Obs. 1        
2       
3      4    
   5         6
   7    8 9      
10    11     
12     13     14

x     13.5   
13.5    14     15   
17.5     19 20     
21     22    23    25
   25     26    
27

y        5    15
   35 25    
25      70   
80    140 75    125   
190 300   240 315

a.   Enter the data in L1 and L2 in your TI
calculator, find the regression line, and construct a scatterplot
with the lesson line included. Does a line appear to be a good
model for these data? (4 points)
b.   What is r2? (1 point)
c.   Find the natural logarithms (ln) of the
y-data.
d.   Draw a scatterplot of x vs. ln y. Find the
regression equation on ln y on x and include it on the graph. Does
it appear to be a better fit than the fit in a.? (4 points)
e.   Use the regression equation you found in d. to
predict the value of y when x = 16. (2 points)
2.   This problem deals with the increase in Life
Expectancy in the United States during the 20th century.
Year
Life Expectancy (Years)
1920 54.1
1930 59.7
1940 62.9
1950 68.2
1960 69.7
1970 70.8
1975 72.6
1980 73.7
1985 74.7
1990 75.4
1995 75.7
2000 76.9
a.   Make a scatterplot of the data. Describe the
scatterplot. What type of relationship does the scatterplot model?
(4 points)
b.   After making the proper transformation, give the
equation of the line, r, and r2. (4 points)
c.   Using the transformed data and regression equation
create a residual plot and interpret it. (3 points).
3.   For any particular car, the distance it takes to
stop is a function of the speed it is traveling when the brakes are
applied. The following chart shows the distance it took a
particular car to stop for the given speed.

Speed (mph)
10
20
30
40
50
60
70
Stopping Distance(ft)
22
46
76
120
178
255
350

a. Construct a scatterplot. (3 Points)
b. Determine an appropriate model. Justify your selection. (4
points)
c. What would be the stopping distance for 45 mph? (2 points)
d. What would be the speed that corresponds to a stopping distance
of 64 ft.?