Refer to Exercise 75. The formula for finding triangular numbers is n [ 1 n − ( − 1 ) ] 2 or n ( n + 1 ) 2 . The formula for finding square numbers is n ( 2 n − 0 ) 2 or n 2 . The formula for pentagonal numbers is n ( 3 n − 1 ) 2 . Find the formula for hexagonal numbers, using inductive reasoning.
Refer to Exercise 75. The formula for finding triangular numbers is n [ 1 n − ( − 1 ) ] 2 or n ( n + 1 ) 2 . The formula for finding square numbers is n ( 2 n − 0 ) 2 or n 2 . The formula for pentagonal numbers is n ( 3 n − 1 ) 2 . Find the formula for hexagonal numbers, using inductive reasoning.
Refer to Exercise 75. The formula for finding triangular numbers is
n
[
1
n
−
(
−
1
)
]
2
or
n
(
n
+
1
)
2
. The formula for finding square numbers is
n
(
2
n
−
0
)
2
or n2. The formula for pentagonal numbers is
n
(
3
n
−
1
)
2
. Find the formula for hexagonal numbers, using inductive reasoning.
Reconsider the patient satisfaction data in Table 1. Fit a multiple regression model using both patient age and
severity as the regressors.
(a) Test for significance of regression.
(b) Test for the individual contribution of the two regressors. Are both regressor variables needed in the model?
(c) Has adding severity to the model improved the quality of the model fit? Explain your answer.
The output voltage of a power supply is assumed to be normally distributed. Sixteen observations taken at
random on voltage are as follows: 10.35, 9.30, 10.00, 9.96, 11.65, 12.00, 11.25, 9.58, 11.54, 9.95, 10.28, 8.37,
10.44, 9.25, 9.38, and 10.85.
(a) Test the hypothesis that the mean voltage equals 12 V against a two-sided alternative using a = 0.05.
(b) Construct a 95% two-sided confidence interval on μ.
(c) Test the hypothesis that σ² = 11 using α = 0.05.
(d) Construct a 95% two-sided confidence interval on σ.
(e) Construct a 95% upper confidence interval on σ.
(f) Does the assumption of normality seem reasonable for the output voltage?
Analyze the residuals from the regression model on the patient satisfaction data from Exercise 3. Comment on
the adequacy of the regression model.
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