The following is the ANOVA obtained from a regression analysis:ANOVASource of VariationREGRESSIONERRORTOTALDegree ofFreedom198Sum ofSquares76.0323.0===Mean Squares---F-calculatedYour are interested to evaluate the following hypothesis:Ho: B 1 B 2 ---against--- HA: B1 B21. How many observation were used in this experiment? (place your answer in the box below)2. Evaluate the F calculated for the regression, and indicate if you are going to reject(R) or fail to reject(FR) the null hypothesis using an a = 0.01 (R/FR; place youranswer right beside the calculated number of treatments)

The missing values of ANOVA obtained from a regression analysis is given.

Answered: The following is the ANOVA obtained…

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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I need help with these questions 1. do the covariates and factors interact? 2. can you conclude a homogeneity of regression slopes? 3. can you conclude homogeneity of variance?
For an ANOVA test of significance of a regression model with 10 regressor variables and 50 observations, what is the degree of freedom of the SSr? choices 11 10 39 49
5
Data on 12 randomly selected athletes was obtained concerning their cardiovascular fitness (measured by time to exhaustion running on a treadmill) and performance in a 20-km ski race. Both variables were measured in minutes and a regression analysis was performed. ski 85 2.5. treadmill = Coefficients Estimate (Intercept) Treadmill 85 - 2.5 Std. Error What is the test statistic? -2.5 0.45 1 Is there sufficient evidence to conclude that there is a linear relationship between cardiovascular fitness and ski race performance? Round your answers to three decimal places. Using your answer from the previous question, find the p-value. Part 2 o
Using the Excel output reported above, if we were to test to see whether "Attendance" is statistically significantly associated with "Score received on the exam," we would conclude that we should Regression Statistics Multiple R R Square Standard Error Observations Intercept Attendance 0.142620229 0.02034053 20.25979924 22 Coefficients Standard Error 39.39027309 37.24347659 0.340583573 0.52852452 T Stat 1.057642216 0.644404489 P-value 0.302826622 0.526635689 O Reject the null hypothesis and conclude that Attendance IS statistically significantly associated with "Score received on the exam" Reject the null hypothesis and conclude that Attendance is is NOT statistically significantly associated with "Score received on the exam" Accept the null hypothesis and conclude that Attendance IS statistically significantly associated with "Score received on the exam" Fail to reject the null hypothesis and conclude that we cannot say that Attendance is statistically significantly associated with…
Shown below is a portion of a computer output for regression analysis relating y (dependent variable) and x (independent variable). ANOVA df SS Regression 1 24.061 Residual 10 67.979 Coefficients Standard Error Intercept 11.064 2.049 x −0.566 0.301 (a) What has been the sample size for the above regression analysis? (b) Perform a t-test and determine whether or not x and y are related. Let ? = 0.05. State the null and alternative hypotheses. (Enter != for ≠ as needed.) H0: Ha: Find the value of the test statistic. (Round your answer to three decimal places.) Find the p-value. (Round your answer to four decimal places.) p-value = What is your conclusion? .
please answer 11 and 12 according to other picture
a. b. C. Name: 3. You have been provided the following regression summery output for the demand for Good X. F df Significance F 0.00000798529 53.29202 Regression Residual Total Intercept Price (Px) Income (1) Price Other (Py) 3 56 59 Coefficients 355.8130 -4.9835 0.8762 -1.4893 SS 824.897 209.662 1,034.559 Standard Error 0.632473 0.939474 0.487969 0.968633 MS 255.3953 4.792375 P-value 6.45E-05 0.001985 0.078924 0.027889 Using the estimates above, state the demand function rounded to two decimals (Format: Qx=do+a1 Px + a ₂1+ a 3Py): Is the product called X a normal or inferior good? (motivate) Is the "Price Other (Py)" referring to a complement or substitute good (motivate)
Use the following ANOVA table for regression to answer the questions. Response: Y Source DF Sum Sq Mean Sq F-value Pr(>F) Regression 1 351.47 351.47 13.32 0.000 Residual Error 359 9474.01 26.39 Total 360 9825.48 Give the F-statistic and p-value.Enter the exact answers.The F-statistic is .The p-value is .
The value obtained for the test statistic, z, in a one-mean z-test is given. Also given is whether the test is two tailed, left tailed, or right tailed. Also is given the P-value.A left-tailed test: z = -1.17 P-value: 0.1210 Use technology to create a scatter plot of the data from the previous question. Include the regression line. (Hand drawn graphs will not be accepted.) The explanatory (input) variable and the response (output) variable must be clearly labeled, within the context of this problem.
pls help answer b with some explanation
Need B please

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