{Exercise 16.5} a. Use the data to compute the b1 coefficient of this estimated
ID: 3172244 • Letter: #
Question
{Exercise 16.5}
a. Use the data to compute the b1 coefficient of this estimated regression equation (to 4 decimals).
b1 =
(Create the xSquare variable first using Data/Transform Data/Square.)
b. Using = .01, test for a significant relationship.
F = (to 2 decimals)
p-value =
The relationship - Select your answer -isis notItem 4 significant.
c. Estimate the traffic flow in vehicles per hour at a speed of 38 miles per hour (to 2 decimals).
95% Prediction interval = (n1,n2)
Explanation / Answer
Result:
Using the data above, statisticians suggested the use of the following curvilinear estimated regression equation.
a. Use the data to compute the b1 coefficient of this estimated regression equation (to 4 decimals).
b0 = 432.5714
b1 = 37.4286
b2 = -0.3829
(Create the xSquare variable first using Data/Transform Data/Square.)
b. Using = .01, test for a significant relationship.
F = 73.15 (to 2 decimals)
p-value = 0.0028
The relationship - Select your answer is significant.
c. Estimate the traffic flow in vehicles per hour at a speed of 38 miles per hour (to 2 decimals).
95% Prediction interval = (1242.55, 1361.47)
Regression Analysis
R²
0.980
Adjusted R²
0.967
n
6
R
0.990
k
2
Std. Error
15.826
Dep. Var.
y
ANOVA table
Source
SS
df
MS
F
p-value
Regression
36,643.4048
2
18,321.7024
73.15
.0028
Residual
751.4286
3
250.4762
Total
37,394.8333
5
Regression output
confidence interval
variables
coefficients
std. error
t (df=3)
p-value
95% lower
95% upper
Intercept
432.5714
141.1763
3.064
.0548
-16.7146
881.8574
x
37.4286
7.8074
4.794
.0173
12.5820
62.2751
x*x
-0.3829
0.1036
-3.695
.0344
-0.7126
-0.0531
Predicted values for: y
95% Confidence Interval
95% Prediction Interval
x
x*x
Predicted
lower
upper
lower
upper
Leverage
38
1,444
1,302.011
1,270.415
1,333.608
1,242.554
1,361.469
0.394
Regression Analysis
R²
0.980
Adjusted R²
0.967
n
6
R
0.990
k
2
Std. Error
15.826
Dep. Var.
y
ANOVA table
Source
SS
df
MS
F
p-value
Regression
36,643.4048
2
18,321.7024
73.15
.0028
Residual
751.4286
3
250.4762
Total
37,394.8333
5
Regression output
confidence interval
variables
coefficients
std. error
t (df=3)
p-value
95% lower
95% upper
Intercept
432.5714
141.1763
3.064
.0548
-16.7146
881.8574
x
37.4286
7.8074
4.794
.0173
12.5820
62.2751
x*x
-0.3829
0.1036
-3.695
.0344
-0.7126
-0.0531
Predicted values for: y
95% Confidence Interval
95% Prediction Interval
x
x*x
Predicted
lower
upper
lower
upper
Leverage
38
1,444
1,302.011
1,270.415
1,333.608
1,242.554
1,361.469
0.394
Related Questions
Navigate
Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.