“The rapid growth of video game popularity has generated concern among practitioners, parents, scholars and politicians,” wrote researchers Hope M. Cummings and Elizabeth A. Vandewater. “Particularly during adolescence, when social interactions and academic success lay the groundwork for health in adulthood, there is concern that video games will interfere with the development of skills needed to make a successful transition to adulthood.” [Source: Cummings, H., & Vandewater, E. (2007). Relation of adolescent video game play to time spent in other activities. Archives of Pediatrics & Adolescent Medicine, 161(7), 684–689.] Cummings and Vandewater measured the time adolescents spent playing video games and the time they spent doing other activities, such as interacting with family and friends, reading or doing homework, or playing sports. Suppose you decide to conduct a similar study among a random sample of 102 teenage boys who play video games. You want to determine whether the amount of time boys spend playing video games is positively correlated with the amount of time they watch television, so you ask the boys to keep a log of their activities over a week’s time. Let ρ denote the population Pearson correlation coefficient between the amount of time boys spend playing video games and the amount of time they watch television. Your null hypothesis is and your alternative hypothesis is . Your hypothesis test will be test. The population Pearson correlation coefficient between the amount of time boys spend playing video games and the time they watch television in your sample is r = 0.44. The test statistic for your hypothesis test is t = . The value for the degrees of freedom you should use for your hypothesis test is . Use this value to set the Degrees of Freedom on the following Distributions tool to find the critical value(s). (Note: Do not use the t-table to calculate the critical score as the answer requires an exact df.) t Distribution Degrees of Freedom = 101 -3.0-2.0-1.00.01.02.03.0t At a significance level of α = 0.01, the critical value(s) for your hypothesis test is (are) . With this critical value you will the null hypothesis and conclude that there is a significant positive correlation between the amount of time boys spend playing video games and the amount of time they watch television. Given your conclusion, what is the most appropriate interpretation of your result? Playing video games makes boys want to watch more television. The more time boys spend playing video games, the less time they spend watching television. This study found no linear relationship between the time boys spend playing video games and time spent watching television. The more time boys spend playing video games, the more time they spend watching television.

“The rapid growth of video game popularity has generated concern among practitioners, parents, scholars and politicians,” wrote researchers Hope M. Cummings and Elizabeth A. Vandewater. “Particularly during adolescence, when social interactions and academic success lay the groundwork for health in adulthood, there is concern that video games will interfere with the development of skills needed to make a successful transition to adulthood.” [Source: Cummings, H., & Vandewater, E. (2007). Relation of adolescent video game play to time spent in other activities. Archives of Pediatrics & Adolescent Medicine, 161(7), 684–689.] Cummings and Vandewater measured the time adolescents spent playing video games and the time they spent doing other activities, such as interacting with family and friends, reading or doing homework, or playing sports. Suppose you decide to conduct a similar study among a random sample of 102 teenage boys who play video games. You want to determine whether the amount of time boys spend playing video games is positively correlated with the amount of time they watch television, so you ask the boys to keep a log of their activities over a week’s time. Let ρ denote the population Pearson correlation coefficient between the amount of time boys spend playing video games and the amount of time they watch television. Your null hypothesis is and your alternative hypothesis is . Your hypothesis test will be test. The population Pearson correlation coefficient between the amount of time boys spend playing video games and the time they watch television in your sample is r = 0.44. The test statistic for your hypothesis test is t = . The value for the degrees of freedom you should use for your hypothesis test is . Use this value to set the Degrees of Freedom on the following Distributions tool to find the critical value(s). (Note: Do not use the t-table to calculate the critical score as the answer requires an exact df.) t Distribution Degrees of Freedom = 101 -3.0-2.0-1.00.01.02.03.0t At a significance level of α = 0.01, the critical value(s) for your hypothesis test is (are) . With this critical value you will the null hypothesis and conclude that there is a significant positive correlation between the amount of time boys spend playing video games and the amount of time they watch television. Given your conclusion, what is the most appropriate interpretation of your result? Playing video games makes boys want to watch more television. The more time boys spend playing video games, the less time they spend watching television. This study found no linear relationship between the time boys spend playing video games and time spent watching television. The more time boys spend playing video games, the more time they spend watching television.

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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Cummings and Vandewater measured the time adolescents spent playing video games and the time they spent doing other activities, such as interacting with family and friends, reading or doing homework, or playing sports.

Suppose you decide to conduct a similar study among a random sample of 102 teenage boys who play video games. You want to determine whether the amount of time boys spend playing video games is positively correlated with the amount of time they watch television, so you ask the boys to keep a log of their activities over a week’s time.

Let ρ denote the population Pearson correlation coefficient between the amount of time boys spend playing video games and the amount of time they watch television. Your null hypothesis is and your alternative hypothesis is . Your hypothesis test will be test.

The population Pearson correlation coefficient between the amount of time boys spend playing video games and the time they watch television in your sample is r = 0.44.

The test statistic for your hypothesis test is t = .

The value for the degrees of freedom you should use for your hypothesis test is . Use this value to set the Degrees of Freedom on the following Distributions tool to find the critical value(s). (Note: Do not use the t-table to calculate the critical score as the answer requires an exact df.)

t Distribution

Degrees of Freedom = 101

-3.0-2.0-1.00.01.02.03.0t

At a significance level of α = 0.01, the critical value(s) for your hypothesis test is (are) . With this critical value you will the null hypothesis and conclude that there is a significant positive correlation between the amount of time boys spend playing video games and the amount of time they watch television.

Given your conclusion, what is the most appropriate interpretation of your result?

Playing video games makes boys want to watch more television.

The more time boys spend playing video games, the less time they spend watching television.

This study found no linear relationship between the time boys spend playing video games and time spent watching television.

The more time boys spend playing video games, the more time they spend watching television.

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