Consider the following Stata regression output (IDTQ1)SourceSdfMSNumber of obs526%3DF(3, 522)76.87%3DModel2194.11163 731.370532Prob > F0.0000Residual4966.30269522 9.51398984 R-squared0.3064%3DAdj R-squared0.3024%3DTotal7160.41429525 13.6388844Root MSE3.0845wageCoef.Std. Err.tP>|t|[95% Conf. Interval]educ.5989651.051283511.680.000.4982176.6997126exper.0223395.01205681.850.064-.0013464.0460254tenure.1692687.02164467.820.000.1267474.2117899_cons-2.872735.7289643-3.940.000-4.304799-1.440671where wage is hourly wage in US$, exper is years of work experience, and tenure is years of tenure with the current employers.Assume that the classical linear model assumptions hold.Imagine you want to test whether the effect of experience is statistically significant. In contrast to what we typically do in the course, you want to conduct a one-sidedhypothesis test. The null hypothesis is that experience has no effect on wage. The alternative hypothesis is that the effect of experience on wage is positive.For this one-sided hypothesis test, is the estimated effect of experience on wage statistically significant at the 5% level? How do you know?Hint: start your answer with "Yes, because" or "No, because".IIII Consider the following Stata regression output (IDTQ1)SourceSdfMSNumber of obs%3D526F(3, 522)76.87Model2194.11163 731.370532Prob > F0.0000Residual4966.30269522 9.51398984R-squared0.3064Adj R-squared =0.3024Total7160.41429525 13.6388844Root MSE3.0845wageCoef.Std. Err.tP>|t|(95% Conf. Interval]educ.5989651.051283511.680.000.4982176.6997126еxper.0223395.01205681.850.064-.0013464.0460254tenure.1692687.02164467.820.000.1267474.2117899_cons-2.872735.7289643-3.940.000-4.304799-1.440671where wage is hourly wage in US$, exper is years of work experience, and tenure is years of tenure with the current employers.Assume that the classical linear model assumptions hold.Imagine you want to test whether the effect of experience is statistically significant. In contrast to what we typically do in the course, you want to conduct a one-sidedhypothesis test. The null hypothesis is that experience has no effect on wage. The alternative hypothesis is that the effect of experience on wage is positive.For this one-sided hypothesis test, is the estimated effect of experience on wage statistically significant at the 5% level? How do you know?Hint: start your answer with "Yes, because" or "No, because".II

Statistical significance rule: If p value is less than or equal to the level of significance 0.05…

Answered: Consider the following Stata regression…

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 sample of n = 120 scores were presented using the Tenacity (authority) scores to the following predictors: age gender, SES, tone of voice, and clothing. Using a two-tailed test at the 0.05 level of significance, a multiple regression analysis was computed: 1. tenacity and age (pvalue = 0.043); 2. tenacity and gender (pvalue = 0.102); 3. tenacity and SES (pvalue = 0.40); 4. tenacity and voice (pvalue = 0.001); 5. tenacity and clothing (value = 0.017); SOrs Using the pvalue, provide your DECISION, whether:Accept Ho or Reject Ho, Accept Ha
A sample of n = 120 scores were presented using the Tenacity (authority) scores to the following predictors: age gender, SES, tone of voice, and clothing. Using a two-tailed test at the 0.05 level of significance, a multiple regression analysis was computed: 5. tenacity and clothing (value = 0.017); SOrs Using the pvalue, provide your DECISION, whether: Accept Ho or Reject Ho, Accept Ha
A sample of n = 120 scores were presented using the Tenacity (authority) scores to the following predictors: age gender, SES, tone of voice, and clothing. Using a two-tailed test at the 0.05 level of significance, a multiple regression analysis was computed: 1. tenacity and age (pvalue = 0.043); 2. tenacity and gender (pvalue = 0.102); 3. tenacity and SES (pvalue = 0.40); Using the pvalue, provide your DECISION, whether: Accept Ho or Reject Ho, Accept Ha
A sample of n = 120 scores were presented using the Tenacity (authority) scores to the following predictors: age gender, SES, tone of voice, and clothing. Using a two-tailed test at the 0.05 level of significance, a multiple regression analysis was computed: 1. tenacity and age (pvalue = 0.043) Using the pvalue, provide your DECISION, whether: Accept Ho or Reject Ho, Accept Ha
A multiple regression analysis produced the following tables. Predictor Coefficients StandardErrort Statistic p-valueIntercept -139.609 2548.989 -0.05477 0.957154x 24.24619 22.25267 1.089586 0.295682x 32.10171 17.44559 1.840105 0.08869Source df SS MS F p-valueRegression 2 302689 151344.5 1.705942 0.219838Residual 13 1153309 88716.07Total 15 1455998Using = 0.01 to test the null hypothesis H :?1 = ?2 = 0, the critical F value is ____.6.701.964.845.995.70
A sample of n = 120 scores were presented using the Tenacity (authority) scores to the following predictors: age gender, SES, tone of voice, and clothing. Using a two-tailed test at the 0.05 level of significance, a multiple regression analysis was computed: 2. tenacity and gender (pvalue = 0.102); Using the pvalue, provide your DECISION, whether: Accept Ho or Reject Ho, Accept Ha
The following measurements correspond to air velocity and the evaporation coefficient of burned fuel droplets in a boost engine. Construct a 95% confidence interval for the regression coefficient alpha.
A multiple regression analysis produced the following tables. Predictor Coefficients StandardErrort Statistic p-valueIntercept 624.5369 78.49712 7.956176 6.88E-06x 8.569122 1.652255 5.186319 0.000301x 4.736515 0.699194 6.774248 3.06E-05Source df SS MS F p-valueRegression2 1660914 830457.1 58.319561.4E06Residual 11 156637.5 14239.77Total 13 1817552The adjusted R is ____________.0.91380.88910.88510.8981
Which of the following scatterplots provides evidence that the condition of equal variance for inference for the slope of a regression line has not been met?
Consider the following ANOVA table for a multiple regression model relating housing prices (in thousands of dollars) to the number of bedrooms in the house and the size of the lot on which the house was built (in square feet). There were 7979 total observations. Estimated Price=20,206.07+2567.28(Bedrooms)+0.2110(Lot Size)Estimated Price=20,206.07+2567.28(Bedrooms)+0.2110(Lot Size) ANOVA dfdf SSSS MSMS F� Significance F� Regression 22 347,853.9414347,853.9414 173,926.9707173,926.9707 19.665619.6656 1.3090E-071.3090E-07 Residual 7676 672,159.9040672,159.9040 8844.20938844.2093 Total 7878 1,020,013.84541,020,013.8454 Compute the adjusted coefficient of determination for this regression model. Round your answer to four decimal places
A sample of n= 120 scores were presented using the tendency(authority) scores to the following predictors age gender, SES, tone of voice, and clothing. Using a two-tailed at the 0.05 level of significance, a multiple regression analysis was computed Using the pvalue, provide your DECISION, whether ACCEPT Ho or REJECT HO,ACCEPT Ha 1.) Tenacity and SES(pvalue=0.40) 2.) Tenancity and voice(pvalue=0.001) 3.) Tenancity and clothing(pvalue=0.017)
The following output was obtained from a multiple regression analysis. Analysis of variance SOURCE DF SS MS Regression 5 100 20 Residual Error 20 40 2 Total 25 140 Predictor Coefficient SE Coefficient t Constant 3.00 1.50 2.00 x1 4.00 3.00 1.33 x2 3.00 0.20 15.00 x3 0.20 0.05 4.00 x4 −2.50 1.00 −2.50 x5 3.00 4.00 0.75 Conduct a global test of hypothesis H0: β1 = β2 = β3 = β4 = β5 = 0; H1: Not all β's are 0 at the 0.05 significance level. d-1. State the decision rule. (Round your answer to 2 decimal places.) Reject H0 if F> d-2. What is the computed value of F? (Round your answer to 1 decimal place.) Value of F d-3. Determine whether any of the regression coefficients are significant. ----------------Reject H0 Atleast One regression Coefficient is---------------- Accept Zero…

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