Test the following data to see if the mean high temperature (in Fahrenheit) in Japan is higher than the mean high temperature (in Fahrenheit) in China for randomly sampled days in summer. Use a = 0.01. 83 91 94 89 89 96 91 92 90 Japan China 78 82 81 77 79 81 80 81

Test the following data to see if the mean high temperature (in Fahrenheit) in Japan is higher than the mean high temperature (in Fahrenheit) in China for randomly sampled days in summer. Use a = 0.01. 83 91 94 89 89 96 91 92 90 Japan China 78 82 81 77 79 81 80 81

Name: Non-parametrics, Two Independent SamplesPlease use Wilcoxon Mann - Whi
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Description: Non-parametrics, Two Independent SamplesPlease use Wilcoxon Mann - Whitney Test Test the following data to see if the mean high temperature (in Fahrenheit) in Japan is higher than themean high temperature (in Fahrenheit) in China for randomly sampled

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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Dependent Variable: CO2 Method: Least Squares Date: 04/20/17 Time: 09:46 Sample: 1990 2013 Included observations: 24 Variable Coefficient Std. Error t-Statistic Prob. C 2.002813 6.458672 0.310097 0.7597 GDP 0.022114 0.011872 1.862670 0.0773 ENERGY -0.734352 0.328388 -2.236233 0.0369 POP 0.203927 0.293686 0.694371 0.4954 R-squared 0.825079 Mean dependent var 3.625982 Adjusted R-squared 0.798841 S.D. dependent var 0.108170 S.E. of regression 0.048515 Akaike info criterion -3.062883 Sum squared resid 0.047074 Schwarz criterion -2.866541 Log likelihood 40.75460 Hannan-Quinn criter. -3.010793 F-statistic 31.44583 Durbin-Watson stat 1.410912 Prob(F-statistic) 0.000000 c-Write down the econometric model for the above estimation by d-using the information obtained from above table d-Write down the least…
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Interperting Regression Results (Continuation of Exercise 8-39) The following table showsadditional regression results presented by the researchers in the study described in Exercise 8-39.There are two regressions. The right-hand column shows the results for all patients, including thosetreated with laparoscopic surgery. The left-hand column shows the results for the sample of patientswho were treated without the laparoscopic surgery.Nonlaparoscopic Patients Only All PatientsCoefficients for Independent VariablesRegression intercept $8,043 $3,719 Length of stay Coefficient* Not significant 861 Standard error for the coefficient Not applicable 80 Number of complications Coefficient 3,393 1,986 Standard error for the coefficient 1,239 406 Laparascopic Coefficient Not applicable 908 Standard error for the coefficient Not applicable 358 R-squared 0.11 0.53 *All independent variables are significant at the level of p = .05 (and t-value >2) except for the length of stay variable in the…
stion 4 of 14 > The scatterplot given compares data on the fuel consumption y of a car at various speeds x. Fuel consumption is measured in liters of gasoline per 100 kilometers driven, and speed is measured in kilometers per hour. A statistical software package gives the least-squares regression line ŷ = 11.058 +0.01466x. 10.0 - Use the residual plot to determine if this linear model is 75 appropriate. O No. There is an obvious positive- negative - positive 5.0 - pattern in the residual plot so a linear model is not appropriate for these data. O Yes. There is an obvious positive – negative – positive pattern in the residual plot so-a linear model is appropriate for these data: O No. The residuals do not have equal variability in the residual plot so a linear model is not appropriate for 2.5 - -2.5 -5.0 - 20 40 60 80 100 120 140 160 these data. Speed (km/h) O No. The residuals are not equally positive or negative in the residual plot so a linear model is not appropriate for these data.…
(f)Based on your output (results), what is the correlation between weight and height? (g)Is there a statistically significant linear relationship between weight and height? Report both the t-test and F-test results for this(include the test statistics, df, p-value, and conclusion).
Dependent Variable: MU Sample: 134 Variable b BETA t-Statistic P-value Constant 7.292206 6.666348 0.0000 COLLEGE -0.115133 -0.23 -4.437697 0.0000 GROWTH 0.018898 0.01 1.718810 0.0880 SENIORS -0.022380 -0.24 -0.731017 0.4661 WAGE -0.0000294 -0.001 -0.874836 0.3833 R-squared 0.192456 Mean dependent var 3.852239 Adjusted R-squared 0.167416 S.D. dependent var 1.928368 Where MU = metropolitan area unemployment rate, COLLEGE = percentage of the metropolitan area population with a college degree, GROWTH = growth rate of the metropolitan area population, SENIORS = percentage of the metropolitan area population that is elderly, and WAGE = average metropolitan area wage How much variation in MU is explained by the independent variables? Which IVs are statistically significant predictors of MU at the 95% C.L.? Tell me which direction you expect the sign of…
Fill the gap: Two samples that are paired, or related, may be compared using a nonparametric procedure calledthe ------------------------- or the ----------------- and the parametric equivalent to this test is known as the ---------------------- for matched pairs or ------- for dependent samples.
[Simple Regression on House Price] Consider the following model you wish to estimate by using the OLS, Price = α + β sqrft + ε, where price is the house price in thousands of dollars and sqrft is size of house in square feet. The OLS result from Excel is as follows (see attached). At the 0.05 level of significance, is there enough evidence that the true population regression coefficient β is different from zero (i.e., H1: β≠0)? (a) Yes (b) No (c) We don’t have enough information to decide
Dependent Variable: INTEREST Method: Least Squares Date: 08/08/20 Time: 17:38 Sample: 2010M01 2019M12 Included observations: 120 Variable с UNEMP R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) Coefficient 9.586787 -1.232859 Std. Error t-Statistic Prob. 1.478492 6.484167 0.0000 0.267930 -4.601419 0.0000 0.152135 Mean dependent var 0.144949 S.D. dependent var 1.105708 Akaike info criterion 144.2656 Schwarz criterion -181.3225 Hannan-Quinn criter. 21.17306 Durbin-Watson stat 0.000011 2.799500 1.195761 3.055375 3.101833 3.074242 0.028137 The above is the regression result between the Australian monthly unemployment rate (UNEMP, independent variable) and monthly interest rate (INTEREST, dependent variable). Both data series are in percentages. Which statement is correct? Select one: O a. The variable UNEMP is both economically and statistically insignificant for INTEREST O b. The variable UNEMP is economically as well as…
#5. What do the result tell us? #6. Are the result likely to be representative of all NFL players? 7# What do the result tell us about of all celebrities? What can be inferred about the precision? #8. What statistics is most relevant, other than the measure of center? #9. What important features of the data is not revealed by any of the measures of center? #10. Can the measure of center be obtained for these values? Do the result make sense?
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