Suppose the following regression equation was generated from the sample data of 50 cities relating number of cigarette packs sold per 1000residents in one week to tax in dollars on one pack of cigarettes and if smoking is allowed in bars:PACKS, = 59682.369232-1108.267218TAX + 176.926159BARS; + eį.BARS 1 if city i allows smoking in bars and BARS; = 0 if city i does not allow smoking in bars. This equation has an R² value of 0.347972, andthe coefficient of BARS, has a P-value of 0.133971. Which of the following conclusions is valid?Answer 2 PointsKeypadKeyboard ShortcutsAccording to the regression equation, cities that allow smoking in bars have lower cigarette sales than cities that do not allow smoking in bars.Regardless of whether or not there is a smoking ban, if a city increases its cigarette tax by one dollar cigarette sales will increase by approximately1108 packs.○ If there is no cigarette tax in a city that allows smoking in bars, the approximate number of cigarette packs sold per 1000 people is 59682.There is not enough evidence at the 0.05 level of significance to support the claim that cities that allow smoking in bars have higher cigarette salesthan cities that do not allow smoking in bars.

The objective of the question is to interpret the regression equation and determine which of the…

Answered: Suppose the following 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 researcher is interested in testing the relationship between smoking and BMI (kg/m2) in adults aged 30-45. In order to test this association, the researcher divides smoking into currently more than a pack a day, currently less than a pack a day, and never smokers. The following table represents the BMIs for each participant enrolled by their respective smoking category. Current Smoker (≥1pack/day) Current Smoker (<1 pack/day Never Smoked 26.7 29.4 22.1 29.4 28.6 30.4 24.3 27.4 21.3 28.4 23.2 26.4 21.6 20.1 19.7 27.4 20.6 19.8 26.8 19.7 21.6 36.4 19.6 22.3 31.5 21.6 24.3 27.4 21.5 *Continue as though all assumptions for ANOVA are met. A) Calculate the MSW and MSB for the data represented above. B) Carry out a formal test for a one-way analysis of variance among the groups and interpret your results.
A random sample of n1 = 20 winter days in Denver gave a sample mean pollution index x1 = 43. Previous studies show that ?1 = 11. For Englewood (a suburb of Denver), a random sample of n2 = 18 winter days gave a sample mean pollution index of x2 = 36. Previous studies show that ?2 = 15. Assume the pollution index is normally distributed in both Englewood and Denver. Do these data indicate that the mean population pollution index of Englewood is different (either way) from that of Denver in the winter? Use a 1% level of significance. a) What is the value of the sample test statistic? (Test the difference ?1 − ?2. Round your answer to two decimal places.)b) Find (or estimate) the P-value. (Round your answer to four decimal places.)
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According to the National Health and Nutrition Examination Survey (NHANES) sponsored by the U.S. government, a random sample of 712 males between 20 and 29 years of age and a random sample of 1,001 males over the age of 75 were chosen and the weight of each of the males were recorded (in kg). Do the data provide evidence that the younger male population weighs more (on average) than the older male population? Use “Y” for ages 20-29 and “S” for ages 75+. It was found that x̅Y=83.4, sY=18.7, x̅S=78.5, and sS=19.0. A) What are the correct null and alternative hypotheses? B)What is the parameter of interest? The difference between the mean weight of all males ages 20-29 and the mean weight of all males ages 75+ The mean weight (in kg) The difference between the mean weight of the sample of males ages 20-29 and the mean weight of the sample of males ages 75+ The mean difference between the weights of males ages 20-29 and males ages 75+ c)Is the randomness condition met for…
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