A regression line is used to satisfactorily describe the relationship between the width of tires in inches and lifespan (in months) of 14 tires in a tire performance study. The regression analysis is shown.VariableConstantCoeff SE Coefft Ratio p-Value7.3985 0.5638 13.12Width of tires 3.95713.9571 0.7382R squared = 88.5%O 7.3985 ± 2.624(0.5638)O 3.9571±2.65(0.7382)O 3.9571±2.624(0.7382)5.36Which expression should be used to compute a 98 percent confidence interval for the slope of the regression line?O 7.3985 +2.681(0.5638)O 3.9571 +2.681(0.7382)0.0340.005R squared (adj) = 87.9%

Here the given regression analysis table is : We have to find the 98% confidence interval for the…

Answered: A regression line is used to…

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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Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05. Overhead Width (cm) 7.9 8.4 9.7 7.4 9.6 7.1 Weight (kg) 162 214 263 140 254 155 Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y = + x. (Round to one decimal place as needed.) The best predicted weight for an overhead width of 2 cm is kg. (Round to one decimal place as needed.) Can the prediction be correct? What is wrong with predicting the weight in this case? O A. The prediction cannot be correct because a negative weight does not make sense. The width in this case is beyond the scope of the available sample data. O B. The prediction cannot be correct because there is not sufficient evidence of a linear correlation. The…
Use the value of the linear correlation coefficient to calculate the coefficient of determination. What does this tell you about the explained variation of the data about the regression line? About the unexplained variation? r= -0.438 please dont copy content from other sources
I need help finding the answers for A,B,and C
The null hypothesis: H, :0 H :0 The alternative hypothesis: Н The type of test statistic: (Choose one) v The value of the test statistic: (Round to at least three decimal places.) The critical value at the 0.05 level of significance: (Round to at least three decimal places.) Can we conclude that houses in Sun Beach have higher appreciation over the past three years than houses in North Arden? Yes No
only HANDWRITTEN answer needed ( NOT TYPED)
The table shows a part of an output of a linear regression model predicting the average fare on different flight routes. Data Table Regression Table Coefficient Constant 95.80976147 COUPON −9.61654124 DISTANCE 0.080733811 PAX −0.000167343 What is the difference in prediction of the following two routes? Route A that is 3,000 miles, with COUPON=1.5 and PAX=6,000 Route B that is 3,000 miles, with COUPON=1.2 and PAX=6,000.
Use the value of the linear correlation coefficient to calculate the coefficient of determination. What does this tell you about the explained variation of the data about the regression line? About the unexplained variation? r=0.926
The best predicted circumference for a diameter 1.2 cm is?
The regression equation for predicting a woman's muscle mass (a quantitative metric, Y) from age (in years, X) is given as ý = 156.35 - 1.19. The ANOVA table for the linear regression of Y vs X is given below. Source Df SS MS p-value Age 11627.5 11627,5 174.06 <,0001 Error 58 3874.4 66.8 Total 59 15501.9 Fill in the blanks below that demonstrate how to construct an interval estimate for the muscle mass of an individual 60-year-old woman. Use a confidence level of 95%; note that the multiplier is t = 2.002 and the standard error of the mean response is s(i,) = 1.055. 95% Interval: ( Select) + ( Select ) [ Select]
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-1.271 b=39.101 ²-0.407044 r=-0.638 Assume the correlation is significant, and use this to predict the number of situps a person who watches 14 hours of TV can do (to one decimal place) Aft
Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2.1 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05. Overhead Width (cm) 8.1 7.6 8.9 7.7 8.6 9.8 Weight (kg) 156 166 213 138 197 251 Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y = (Round to one decimal place as needed.)
A sales manager for an advertising agency believes there is a relationship between the number of contacts that a salesperson makes and the amount of sales dollars earned. A regression analysis shows the following results. Coefficients Standard Error t-Stat p-value Intercept −12.201 6.560 −1.860 0.100 Number of contacts 2.195 0.176 12.505 0.000 What is the regression equation? Multiple Choice Ŷ = 2.195 − 12.201X Ŷ = −12.201 + 2.195X Ŷ = 12.201 + 2.195X Ŷ = 2.195 + 12.201X

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