Do college students enjoy playing sports more than watching sports? A researcher randomly selected ten college students and asked them to rate playing sports and watching sports on a scale from 1 to 10 with 1 meaning they have no interest and 10 meaning they absolutely love it. The results of the study are shown below. Playing Vs. Watching Sports Play 7 4 6 6 7 6 3 10 3 3 Watch 5 1 6 6 3 6 1 10 2 2 Assume a Normal distribution. What can be concluded at the the αα = 0.10 level of significance level of significance? For this study, we should use The null and alternative hypotheses would be: H0:H0: (please enter a decimal) H1:H1: (Please enter a decimal) The test statistic = (please show your answer to 3 decimal places.) The p-value = (Please show your answer to 4 decimal places.) The p-value is αα Based on this, we should the null hypothesis. Thus, the final conclusion is that ... The results are statistically insignificant at αα = 0.10, so there is insufficient evidence to conclude that the population mean rating for playing sports is greater than the population mean rating for watching sports. The results are statistically significant at αα = 0.10, so there is sufficient evidence to conclude that the population mean rating for playing sports is greater than the population mean rating for watching sports. The results are statistically significant at αα = 0.10, so there is sufficient evidence to conclude that the ten students that were surveyed rated playing sports higher than watching sports on average. The results are statistically insignificant at αα = 0.10, so there is statistically significant evidence to conclude that the population mean rating for playing sports is equal to the population mean rating for watching sports.

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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Do college students enjoy playing sports more than watching sports? A researcher randomly selected ten college students and asked them to rate playing sports and watching sports on a scale from 1 to 10 with 1 meaning they have no interest and 10 meaning they absolutely love it. The results of the study are shown below.

Playing Vs. Watching Sports

Play	7	4	6	6	7	6	3	10	3	3
Watch	5	1	6	6	3	6	1	10	2	2

Assume a Normal distribution. What can be concluded at the the αα = 0.10 level of significance level of significance?

For this study, we should use

The null and alternative hypotheses would be:

H0:H0: (please enter a decimal)

H1:H1: (Please enter a decimal)

The test statistic = (please show your answer to 3 decimal places.)
The p-value = (Please show your answer to 4 decimal places.)
The p-value is αα
Based on this, we should the null hypothesis.
Thus, the final conclusion is that ...
- The results are statistically insignificant at αα = 0.10, so there is insufficient evidence to conclude that the population mean rating for playing sports is greater than the population mean rating for watching sports.
- The results are statistically significant at αα = 0.10, so there is sufficient evidence to conclude that the population mean rating for playing sports is greater than the population mean rating for watching sports.
- The results are statistically significant at αα = 0.10, so there is sufficient evidence to conclude that the ten students that were surveyed rated playing sports higher than watching sports on average.
- The results are statistically insignificant at αα = 0.10, so there is statistically significant evidence to conclude that the population mean rating for playing sports is equal to the population mean rating for watching sports.

Features Features Normal distribution is characterized by two parameters, mean (µ) and standard deviation (σ). When graphed, the mean represents the center of the bell curve and the graph is perfectly symmetric about the center. The mean, median, and mode are all equal for a normal distribution. The standard deviation measures the data's spread from the center. The higher the standard deviation, the more the data is spread out and the flatter the bell curve looks. Variance is another commonly used measure of the spread of the distribution and is equal to the square of the standard deviation.

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