The null hypothesis: Н = 0 The alternative hypothesis: H 1 :B, 1 Degrees of freedom: The type of test statistic: 27 The value of the test statistic: (Round to at least three decimal places.) The two critical values at the 0.10 level of significance: | and | (Round to at least three decimal places.) Based on the information, can we conclude (using the 0.10 level) that there is a significant linear relationship between mileage and used selling price for two-year-old Cadets? Yes No
The null hypothesis: Н = 0 The alternative hypothesis: H 1 :B, 1 Degrees of freedom: The type of test statistic: 27 The value of the test statistic: (Round to at least three decimal places.) The two critical values at the 0.10 level of significance: | and | (Round to at least three decimal places.) Based on the information, can we conclude (using the 0.10 level) that there is a significant linear relationship between mileage and used selling price for two-year-old Cadets? Yes No
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Transcribed Image Text:The null hypothesis:
Н
= 0
The alternative hypothesis:
H
1
:B,
1
Degrees of
freedom:
The type of test statistic:
27
The value of the test statistic:
(Round to at least three
decimal places.)
The two critical values at the
0.10 level of significance:
D and O
(Round to at least three
decimal places.)
Based on the information, can we conclude (using
the 0.10 level) that there is a significant linear
relationship between mileage and used selling price
for two-year-old Cadets?
Yes
No

Transcribed Image Text:The Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. For a random sample of 29 Cadets, each bought
"new" two years ago and each sold "used" within the past month, the least-squares regression equation relating the two variables mileage
(denoted by x) and used selling price (denoted by y, in dollars) was y = 40.01 – 0.50x . The standard error of the slope of this least-squares
regression line was approximately 0.34. Based on this information, test for a significant linear relationship between the two variables by doing
a hypothesis test regarding the population slope B,. (Assume that the variable y follows a normal distribution for each value of x and that the
other regression assumptions are satisfied.) Use the 0.10 level of significance, and perform a two-tailed test. Then fill in the table below.
(If necessary, consult a list of formulas.)
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