includes prediction of the number of manatees kifled when there are 900,000 boat
ID: 3159801 • Letter: I
Question
includes prediction of the number of manatees kifled when there are 900,000 boats registered in Fiorida Give 95% intervals for (a) the increase in the number of ma:atees die for each additional 1000 boats register (b) the number of manatees that will beated fez? year if there are 900,000 boats registered next yeat. 26.32 Fidgeting keeps you slinm 26.32 Fidgeting keeps you slim: inference. Our first example of regression (Example 5.1, page 128) pre- sented data showing that people who increased their nonexercise activity (NEA) when they were deliber- ately overfed gained less fat than other people. Use software to add formal inference to the data analysis for these data. FATGAIN (a) Based on 16 subjects, the correlation between NEA increase and fat gain was r =-0.7786. Is this significant evidence that people with higher NEA increase gain less fat? (Report a t statistic from regression output and give the one-sided P-value.) (b) The slope of the least-squares regression line was b =-0.00344, so that fat gain decreased by 0.00344 kilogram for each added calorie of NEA. Give a 90% confidence interval for the slope of the population regression line. This rate of change is the most important parameter to be estimated. (c) Sam's NEA increases by 400 calories. His predicted fat gain is 2.13 kilograms. Give a 95% interval for predicting Sam's fat gain. 26. SST Resi 26.33 Predicting tropical storms. Exercise 5.61 (page 160) gives data on William Gray's predictions of the num- ber of named tropical storms in Atlantic hurricane ons from 1984 to 2013. Use these data for regres-Explanation / Answer
c).
Regression Analysis
r²
0.606
n
16
r
-0.779
k
1
Std. Error
0.740
Dep. Var.
Fat gain (kg)
ANOVA table
Source
SS
df
MS
F
p-value
Regression
11.7941
1
11.7941
21.55
.0004
Residual
7.6634
14
0.5474
Total
19.4575
15
Regression output
confidence interval
variables
coefficients
std. error
t (df=14)
p-value
95% lower
95% upper
Intercept
3.5051
0.3036
11.545
1.53E-08
2.8539
4.1563
NEA change (cal)
-0.0034
0.0007
-4.642
.0004
-0.0050
-0.0019
Predicted values for: Fat gain (kg)
95% Confidence Interval
95% Prediction Interval
NEA change (cal)
Predicted
lower
upper
lower
upper
Leverage
400
2.1285
1.7142
2.5429
0.4885
3.7686
0.068
95% prediction interval =(0.4885, 3.7686).
Regression Analysis
r²
0.606
n
16
r
-0.779
k
1
Std. Error
0.740
Dep. Var.
Fat gain (kg)
ANOVA table
Source
SS
df
MS
F
p-value
Regression
11.7941
1
11.7941
21.55
.0004
Residual
7.6634
14
0.5474
Total
19.4575
15
Regression output
confidence interval
variables
coefficients
std. error
t (df=14)
p-value
95% lower
95% upper
Intercept
3.5051
0.3036
11.545
1.53E-08
2.8539
4.1563
NEA change (cal)
-0.0034
0.0007
-4.642
.0004
-0.0050
-0.0019
Predicted values for: Fat gain (kg)
95% Confidence Interval
95% Prediction Interval
NEA change (cal)
Predicted
lower
upper
lower
upper
Leverage
400
2.1285
1.7142
2.5429
0.4885
3.7686
0.068
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