FOR TEEN AND YOUNG ADULT MALES (13 TO 29) IS AGE RELATED TO THE NUMBER OF HOURS SPENT PLAYING VIDEO/COMPUTER GAMES?

By Amanda Webster, Jennifer Burgoyne, Natalie Gibbs and Kimberly Ibarra (Group 8) July 28, 2012 Math 1040-090 Professor Trystgad

Age vs. Video/Computer Games

For teen and young adult males age 13-29 is age related to the number of hours spent playing video/computer games?

For our group project we want to see if these to variables were related to one another. In order to obtain our data we decided that we needed to gather data from different strata or different public locations that did not specialize or encourage gaming. This would eliminate bias from our sampling. We each went to different locations that fit our description and each gathered data from 20 different individuals. By asking them their age and how many hours per day they spent playing video/computer games we were able to get a sample of 100 people.

Group Presentation:

1. Data in table form from all group members.

2. Statistics for our 2 variables (age and # of hours spent playing video/computer games daily.)

3. Histograms for our 2 variables

4. Boxplots for our 2 variables

5. Statistics testing Correlation

6. Scatter diagram with and without line of regression

7. Analysis, interpretation, conclusions, and summary of data.

amanda jennifer kimberly natalie29 0 14 3 15 6 28 213 5 26 0 14 5 13 215 4 22 0 26 2 13 415 4 28 0.5 21 3 14 117 4 23 20 27 2 16 1.516 4 28 0.3 29 4 16 017 4 19 1 19 2 17 120 2 24 4 23 3 18 024 2 26 2 26 3 18 0.525 1 27 3 19 5 18 1.518 3 23 4 16 3 19 016 4 22 2 28 6 20 016 5 27 16 26 5 20 327 2 21 2 19 0 21 226 2 24 0 15 3 23 1.528 2 22 0 15 3 24 224 2 21 3.5 29 6 25 1.525 3 25 1 19 3 26 026 1 14 4 19 4 27 2.527 0 18 8 18 4 27 4.5

29 0.5

Data Collected from each Individual:

Statistics for Age:

Statistics for the first quantitative variable which is age are as follows: Mean: 21.61 Standard Deviation: 4.786 Min: 13 Q1: 18 Q2 (Med): 22 Q3: 26 Max: 29 Range: 16 Mode: 26 Outliers: None

Statistics for # of Hours Spent Playing Video Games:

Statistics for the second quantitative variable which is number of hours per day spent playing video/computer games are as follows:

Mean: 2.439 Standard Deviation: 2.890 Min: 0 Q1: 0.2 Q2 (Med): 2 Q3: 4 Max: 20 Range: 20 Mode: 0 Outliers: 16, 20

Frequency and Relative Frequency Histograms for Age:

Frequency and Relative Frequency Histogram for # of Hours Playing:

Boxplot for Both Variables:

Statistics Testing Correlation:

Statistics for correlation between age and # of Gaming Hours per day:

Linear Correlation Coefficient: r=-0.1144095879 Equation for line of Regression: y=-0.069x+3.932 Critical Vale: 0.205

Critical value found on: http://www.gifted.uconn.edu/siegle/research/Correlation/corrchrt.htm.

n=98 with level of significance= 0.05

Simple linear regression results: Dependent Variable: Hours of Video GamesIndependent Variable: Age Hours of Video Games = 4.5905023 - 0.11462304 Age Sample size: 98 R (correlation coefficient) = -0.3008 R-sq = 0.09046037 Estimate of error standard deviation: 1.75423

Statistics for Correlation without the Outliers:

12 14 16 18 20 22 24 26 28 300

5

10

15

20

25

Age of Participant

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pent

Gam

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Scatter Diagram without Line of Regression:

Scatter Diagram with Line of Regression:

Analysis:

As you can see from our histograms and boxplots our data for age has a symmetrical shape. Whereas the data for number of hours spent playing video/computer games is skewed to the right. The scatter plot indicates no linear relationship between the 2 variables. When looking at the correlation coefficient it is closer to 0 than it is to -1. This along with the correlation coefficient being smaller than the critical value for our sample concludes that there is no relationship between the variables. When looking at the correlation coefficient without the outliers you can see that it is closer to -1 and larger than our critical value indicating that without these 2 outliers included, there would be a relationship between age and the number of hours spent playing video games.

Analysis Continued:

There may be other lurking variables that could account for the lack of correlation. These would include: income, full or part time work, school, age limits, etc. In order to obtain more substantial data we could look again at the question while including ways to eliminate these lurking variables.

Summary

Our intended purpose of the study was to indicate a difference in hours in relation to age played on computer and video games. Our concluding results found that there was no significant difference in age versus hours played.

One concluding idea would be to broaden our research with a wider base and see if the age does in fact show a decrease with hours played on video/computer games.