Test the hypothesis ₁ = ₂ = 3 = μ₁ = μ5 at 0.05 level ofsignificance for the data of Table 13.1 on absorption of moistureby various types of cement aggregates.Absorption of Moisture in Concrete AggregatesAggregate1345563631TotalMean5514574507314996323320553.332595580508583633517639417615449511 517 522573613648656677555 679341636632791 3664569.33 610.50 465.17 610.6743841516854561.80

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Answered: Test the hypothesis ₁ = ₂ = ₂ = M₁ = μs…

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

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3
A student is interested in knowing if there is a relationship between the age of a child and the amount of time spent on homework. He asks 7 people their ages and the number of hours they spend on homework. Is there a relationship between age and the amount of time spent doing homework? Test at α = .05 level of significance. Age of Child (yrs) Time doing HW (hrs) 11 5 15 7 4 0 7 1 9 2 5 0 13 3
7.1-8
11. 2.3 help correct the last question
280.8 BA, are Exercise 2. Consider the setting of Exercise 1. The residuals corresponding to the LSQ line, HR = 133.72 plotted in the graph below. Residuals Versus the Order of the Data (response is HR) 30 20 10 -10 -20 -30 3 4 6. 8 10 11 12 13 14 Observation Order (a) Using the residual graph only, estimate how far the fitted/predicted number of home runs (fitted ŷ5) is from the observed number of home runs (observed y, value) for the 5th observation in the data set. (b) Give an approximate 95% prediction interval for the number of homeruns hit by an MVP who has a batting average of 0.350. Explain your reasoning. Residual
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2. The x values and standardized residuals for the chlorine flow/etch rate data of Exercise 52 (Section 12.4) are displayed in the accompanying table. Construct a stan- dardized residual plot and comment on its appearance. 1.50 2.00 2.50 x e* x e* 1.50 .31 3.00 .73 1.02 3.50 -1.36 -1.15 3.50 1.53 -1.23 4.00 .07 2.50 .23
"1ÄUZ☺ 8:17 K/s You Just now 1. Consider the data below. This data represents the number of miles per gallon that 30 selected four-wheel drive sports utility vehicles obtained in city driving 12 17 16 | 14 16 18 16 18 17 16 17 15 15 16 16 15 16 19 10 14 15 11 15 15 19 13 16 |18 16 20 i) Calculate mean (Using Direct and Indirect method), median and mode of ungrouped data.
1. Model 1: OLS, using observations 1-706 Dependent variable: RST Coefficient Std. Error t-ratio p-value const 3586.38 38.9124 92.17 <0.0001 *** TOTWRK −0.150746 0.0167403 −9.005 <0.0001 *** Mean dependent var 3266.356 S.D. dependent var 444.4134 Sum squared resid 1.25e+08 S.E. of regression 421.1357 R-squared 0.103287 Adjusted R-squared 0.102014 F(1, 704) 81.08987 P-value(F) 1.99e-1810538.19 Log-likelihood −5267.096 Akaike criterion 10538.19 Schwarz criterion 10547.31 Hannan-Quinn 10541.71 RSTi =3586.38−0.150746 x TOTWRKi , R2=0.103287,SER=421.1357 (38.9124) (0.0167403) Question? could you please help with this question below. 3) By observing the GRETL output in Part (1) above, provide a detailed explanation of the coefficient of determination. Based on your analysis, is this a good model? Why or why not?
need with bell shape curve please
Data on weekday exercise time for 20 females, consistent with summary quantities given in the paper "An Ecological Momentary Assessment of the Physical Activity and Sedentary Behavior Patterns of University Students,"† are shown below. Female—Weekday 10.0 90.6 48.5 50.4 57.4 99.6 0.0 5.0 0.0 0.0 5.0 2.0 10.5 5.0 47.0 0.0 5.0 54.0 0.0 48.6 Calculate the values of the median and interquartile range. median: interquartile range: Interpret the values of the median and interquartile range. The median exercise time of indicates that half of the exercise times were below , and the remaining half were above . The interquartile range tells us that the middle fifty percent of exercise times had a range of .
1-What these statistics tell you about the kurtosis of the variables:

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