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a. employ the t-test statistic and name the sampling distribution  of the test statistic. b.Should we reject the null and justify? C. use p-value to and draw the sampling distribution and shade area corresponding to p-value

a. employ the t-test statistic and name the sampling distribution  of the test statistic. b.Should we reject the null and justify? C. use p-value to and draw the sampling distribution and shade area corresponding to p-value

MATLAB: An Introduction with Applications
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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From the attached file we will conduct a hyothesis test to determine whether oxidation thickness is linearly  related to tempertature. Assume significance is alpha 0.05. 

Null hypothesis = 0

Alternative hypothsis  does not equal 0

a. employ the t-test statistic and name the sampling distribution  of the test statistic.

b.Should we reject the null and justify?

C. use p-value to and draw the sampling distribution and shade area corresponding to p-value 

Bivariate Fit of oxidation By temperature oxidation ♡ 2. N 1 -2 -1 Linear Fit Linear Fit oxidation=2-0.7*temperature Summary of Fit RSquare RSquare Adj Source Model Error C. Total 0 temperature Root Mean Square Error Mean of Response Observations (or Sum Wgts) Analysis of Variance Term Intercept temperature 0.816667 0.755556 0.60553 DF 1 3 4 6.0000000 Parameter Estimates 2 5 4.9000000 1.1000000 • 1 Sum of Squares Mean Square F Ratio 4.90000 13.3636 0.36667 Prob > F 0.0354 2 G 10 < Estimate Std Error t Ratio Prob>lt] Lower 95% Upper 95% 7.39 0.0051" 1.1381895 2 0.270801 2.8618105 -0.7 0.191485 -3.66 0.0354" -1.309392 -0.090608 0.
Transcribed Image Text:Bivariate Fit of oxidation By temperature oxidation ♡ 2. N 1 -2 -1 Linear Fit Linear Fit oxidation=2-0.7*temperature Summary of Fit RSquare RSquare Adj Source Model Error C. Total 0 temperature Root Mean Square Error Mean of Response Observations (or Sum Wgts) Analysis of Variance Term Intercept temperature 0.816667 0.755556 0.60553 DF 1 3 4 6.0000000 Parameter Estimates 2 5 4.9000000 1.1000000 • 1 Sum of Squares Mean Square F Ratio 4.90000 13.3636 0.36667 Prob > F 0.0354 2 G 10 < Estimate Std Error t Ratio Prob>lt] Lower 95% Upper 95% 7.39 0.0051" 1.1381895 2 0.270801 2.8618105 -0.7 0.191485 -3.66 0.0354" -1.309392 -0.090608 0.
Expert Solution
Step 1

Dependent variable (y) = oxidation thickness

Independent variable (x) = temperature

Level of significance (α) = 0.05

The linear regression output is given.

 

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