QUESTION 4Match the following examples should be one-tailed or two-tailed tests.A. One-tailed testComparing the variance in the repeatedobservations of a distance against themanufacturer's specified accuracy for an EDM.B.Two-tailed testComparing the reference variances ofconstrained and minimally constrainedadjustments.Comparing a calibration baseline length against avalue measured using an EDM.The reference variance of a least-squaresadjustment against its a priori value of one.Click Save and Submit to save and submit. Click Save All Answvers to save all answers.

One tailed test:A hypothesis test where the possibility of the effect is one directional either to…

Answered: QUESTION 4 Match the following examples…

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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A multiple regression was estimated using the SEK method and the results are given below. Based on these estimates alone, multiple Explain the level of linearity, varying variance and cascading dependency problems. Bi: 4,1234 B2: -0,2189 B3: -1,7862 sh:3,1223 R?: 0,9820 R*: 0,9750 F: 10,89 F tablo:3,07 sh:4,4420 sh:0,3179 D.W.: 0,2558 d1:0,385 dy:1,685
In a production process, the time (in minutes) taken (run time) for a production run and the number of items produced (run size) for 15 randomly selected orders are analyzed using Minitab statistical software. The Minitab output is as follows: Regression Analysis: Run time versus Run size Analysis of Variance Source DF Adj SS Adj MS F-Value Critical value Regression 1 8737.1 8737.1 29.35 ……… Error 13 3870.5 297.7 Total 14 12607.6 Model Summary S R-sq R-sq(adj) R-sq(pred) 17.2549 ……. 66.94% 61.32% Coefficients Term Coef SE Coef T-Value P-Value VIF Constant 148.4 11.3 13.13 0.000 Run size 0.2627 0.0485 ……. ……. 1.00 1- Write down the least square regression equation to predict the run time for run size. 2- Interpret the coefficients of the fitted model. 3- At 5% significance level, test if run size is a good…
Chapter 9, Section 2, Exercise 030 Use the following ANOVA table for regression to answer the questions. Analysis of Variance Source DF SS MS F P Regression 1 304.5 304.5 2.01 0.158 Residual Error 174 26361.0 151.5 Total 175 26665.5 Give the F-statistic and p-value.Enter the exact answers.The F-statistic is=The p-value is=
The variance in a production process is an important measure of the quality of the process. A large variance often signals an opportunity for improvement in the process by finding ways to reduce the process variance. Jelly Belly Candy Company is testing two machines that use different technologies to fill three pound bags of jelly beans. The following tables contain a sample of data on the weights of bags (in pounds) filled by each machine. Machine 1 Machine 2 Ho: 01 2 Ho: 01 State the null and alternative hypotheses. 2 2 Ha: 01 Ha: 01 2.95 3.45 3.50 3.75 3.48 3.26 3.33 2 Ha: 01 ≤0₂ 3.16 3.20 3.22 3.38 3.90 3.36 3.25 2 3.20 2 3.12 Ho: 01 Conduct a statistical test to determine whether there is a significant difference between the variances in the bag weights for two machines. Use a 0.05 level of significance. What is your conclusion? = 2 Ho: 01 #0 2 02 = 2 2 02 02 2 2 2 ≤02 3.20 2 2 H₂: 01 > 02 3.28 3.22 2.98 3.45 3.70 3.34 3.18 3.35 3.22 3.30 3.34 3.28 3.29 3.25 3.30 3.27 3.38 3.34…
The output table below represents the results of the estimation of household expenditures (Y) and income (X) in thousand dollars. Considering the output table below, answer the following questions. Dependent Variable: Y Method: Least Squares Date: 12/28/16 Time: 15:29 Sample: 2000 2010 Included observations: 11 Variable Coefficient Std. Error t-Statistic Prob. C 0.229334 2.938536 0.078044 0.9395 X 0.354833 0.024783 14.31783 0.0000 R-squared 0.957944 Mean dependent var 37.45455 Adjusted R-squared 0.953271 S.D. dependent var 21.01125 S.E. of regression 4.541976 Akaike info criterion 6.027567 Sum squared resid 185.6659 Schwarz criterion 6.099911 Log likelihood -31.15162 Hannan-Quinn criter. 5.981964…
The output table below represents the results of the estimation of household expenditures (Y) and income (X) in thousand dollars. Considering the output table below, answer the following questions. Dependent Variable: Y Method: Least Squares Date: 12/28/16 Time: 15:29 Sample: 2000 2010 Included observations: 11 Variable Coefficient Std. Error t-Statistic Prob. C 0.229334 2.938536 0.078044 0.9395 X 0.354833 0.024783 14.31783 0.0000 R-squared 0.957944 Mean dependent var 37.45455 Adjusted R-squared 0.953271 S.D. dependent var 21.01125 S.E. of regression 4.541976 Akaike info criterion 6.027567 Sum squared resid 185.6659 Schwarz criterion 6.099911 Log likelihood -31.15162 Hannan-Quinn criter. 5.981964…
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Let's study the relationship between brand, camera resolution, and internal storage capacity on the price of smartphones. Use α = .05 to perform a regression analysis of the Smartphones01CS dataset, and then answer the following questions. When you copy and paste output from MegaStat to answer a question, remember to choose to "Keep Formatting" to paste the text. a. Did you find any evidence of multicollinearity and variance inflation among the predictors. Explain your answer using a VIF analysis. b. Copy and paste the normal probability plot for your analysis. Is there any evidence that the errors are not normally distributed? Explain. c. Copy and paste the Residuals vs. Predicted Y-values. Does the pattern support the null hypothesis of constant variance for the errors? Explain. d. Study the residuals analysis. Which observations, if any, have unusual residuals? e. Study the residuals analysis. Calculate the leverage statistic. Which observations, if any, are high leverage…
A researcher is interested in whether an approved supplement enhances the performance of baseball players. The research compares two different baseball teams on the number of home runs they hit for the season. Which test is most appropriate to conduct? Chi-square Independent t-test Correlation/Regression Dependent t-test One-Way ANOVA
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.
Police sometimes use footprint evidence to estimate the height of a suspect. Data was collected from 36 randomly selected men in Nebraska in 2001. The regression model assumptions have been checked, and they are all satisfied. We want to test if there is a positive linear association between shoe print size and average height. The p-value of the appropriate test was found to be 0.0006. (a) Give your brief conclusion. Note that no significance level is provided. (b) Give your conclusion in the context of the problem. (This question will be marked manually by your instructor).
Which one(s) are true? I. Combination of high adjusted R2 and no statistically significant variables is an indication of multicollinearity II. Multicollinearity exists only in some regression models. III. With multicollinearity, it is possible for an F-test of overall significance to reject the null even though none of the individual t-tests do. IV. Correlation coefficient and VIF are two methods to detect multicollinearity. a. Il and III b. Il and IV c. I and III d. None e. I, III and IV

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