Introduction: The purpose of this exercise was to use the information from the Stats Lab book to determine the following:
Time: numbers of hours played week prior to survey
Like to Play: 1=Never played, 2=Very much, 3=Somewhat, 4=Not really, 5=Not at all
Where they Play:
1=Arcade
2=Home on a system
3=Home on a computer
4=Home on computer and system
5=Arcade and Home(system or computer)
6=Arcade and home (both system and computer)
Frequency of Play: 1=Daily, 2=Weekly, 3=Monthly, 4=Semesterly
Play if Busy: 0=no, 1=yes
Are Video Games Educational?: 0=no, 1=yes
Sex: 0=Female, 1=Male
Age
Computer at Home: 0=no, 1=yes
Hate Math: 0=no, 1=yes
#of hours worked in week prior to survey
Own a PC: 0=no, 1=yes
PC has a CDROM: 0=no, 1=yes
Have an Email: 0=no, 1=yes
Grade expect in course: 4=A, 3=B, 2=C, 1=D, 0=F
The population provided for us is 95 students out of 314. They were the only ones to complete the survey, however only data from 91 of them were used due to errors in the survey or because they were not totally completed.
The survey asked the question , “How much time(hrs.) did you spend playing video games last week?” This graph shows that there is a significant comparison to how much they played the week earlier and how frequently they said they played. The average student plays 1.45 hrs. of video games a week, however if theses students admit to playing daily their average jumps to 3 times this to around 4.5 hrs. a week. Subsequently, as the student admits to playing least frequently, the average amount of hours played the week before substantially decreases. However, each category has 1 or 2 students who play substatially more then rest the rest in the category, which significantly increased averages. I broke this information down into the following table.

Mean 
Mean* 
Total Mean 
Total Mean* 
Percent 
Daily 
4.44444444 
1.71428571 
1.24285714 
0.62613636 
9.89% 
Weekly 
2.53928571 
1.52222222 
– 
– 
30.77% 
Monthly 
0.05555556 
– 
– 
– 
19.78% 
Semesterly 
0.04347826 
– 
– 
– 
25.27% 
In statistics, interval estimation is the use of sample data to calculate an interval of values for an unknown population, rather than point estimates which is simply a set population.
A confidence level is the extent to which an assumption or number is likely to be true. Confidence intervals are used to indicate the reliability of an estimate and how likely the interval is to contain the parameter. Increasing the desired confidence level will widen the confidence interval. Below is work done in class on Mathematica to determine a mean given a confidence level of 0.99.
An estimaton of the number of students who played a video game the week before the survey was taken by dividing the total number of students surveyed, 91, by the number of student who played video games prior, 34, approximately 37%. From here we were asked to calculate a 95% confidence interval for the students who claimed to have played a video game the week prior. For this Mathematica was used (like above) and reported the following interval (1.35992, 5.29302).
The interval estimate for the data below shows that the average amount of time playing video games vary greatly. Based on Mathematics interval output the data can’t be used to accurately represent the total population.
In[2]:= <<HypothesisTesting`
MeanCI[Time,ConfidenceLevel>.95]
Out[20]= {1.35992, 5.29302}
Playing Video Games While You are Busy:
The largest group of people who played video games are those that were busy, which seems backwards. However, personally I find that I will always find something else to do rather than what I know has to be done. Procrastination is the students best friend.Below is the number of students expecting to achieve an A in their classes and their associated game play in relation to grading. Showing that of students who play video games those who restrict their game play to around three hours or less weekly have a better chance of still attaining an A.
The above information is the number of students who are expecting to get an A in their classes. This is compared to the students frequency of game play.
Frequency of Play: 1=Daily, 2=Weekly, 3=Monthly, 4=Semesterly
An analysis of the number of students who work and do not work for pay shows that those who work and those who don’t work and play video games is split 50/50.
This pie graph shows the percentage of students that find video games educational. More people regard video games as noneducational, but it is nearly 50/50.
Legned:
1=Arcade
2=Home on a system
3=Home on a computer
4=Home on computer and system
5=Arcade and Home(system or computer)
6=Arcade and home (both system and computer)
The largest group of people who play games are home computer users.
Legend(BlueBoys RedGirls):
1=Never played, 2=Very much, 3=Somewhat, 4=Not really, 5=Not at all
Suprisingly the data between girls and boys is very similiar in this case. However, more girls do not like video games at all or “not really.”
Conclusion: There is a substatial amount of information in this Stats Lab, however a lot of this data is uncorrelated. Also the data can’t represent a total population because it’s confidence level is not high enough. Lastly, here are some pie charts that show a few of the random questions asked on this survey.
This pie chart shows the types of video games the students in the survey like to play
This pie charts shows the reasons why people don’t like to play video games.
This chart shows the reasons why people play video games.
Worked with – Jeff Richards & Jason Motta
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