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Title: The effects of sex, and age, and lifestyle on fast food consumption among students at UIndy.
Source of data:Survey
Problem: We predict that sex, age, and lifestyle effect the amount of fast food consumption among students at UIndy.
Accomplish: We want to see if the amount of fast food people eat is based on age, gender, or lifestyle.We will create a survey and distribute it to students and try to figure out how often college students eat fast food. We will then enter this information into Excel and compare the results from the different students.
How do eating habits compare to people who work out more and people who work out less?
Is there a correlation between gender and eating out?
Is there a correlation between working out and eating fast food?
1.) Are you a male or a female?
2.) How often do you exercise per week? (ex. Run, Swim, Lift, Play a sport)
B.) 1-2 times
C.) 3-5 times
D.) More than 5 times
3.) What year are you?
4.)Do you have a meal plan at UIndy?
5.) How often do you eat fast food per week? (ex. Taco bell, McDonalds, Burger King)
A.) 0 times
B.) 1-3 times
C.) 4-7 times
D.) more than 7 times
Above you will see the survey that we constructed to answer the questions about students at our university. We each handed out surveys to people on our campus to figure out if there was effects of sex, age, and lifestyle on fast food consumption of students at Uindy. We assumed that the consumption of fast food would be impacted by these 3 variables. At this point in the project we have surveyed 54 students at Uindy. In this study 32 of the participants were male and 22 were female.In this survey, we have looked at numerous variables including; gender, how many times they exercise, what year they are, if they have a meal plan, and how many times they work out per week. Our hopes from this survey is to find a correlation between both men and women, their age, their lifestyle, and how often they eat out. We then want to split these two gender groups up and see what the correlation of the 2 separate groups are. This far into the study it seems as if an individual does not have a meal plan they typically eat fast food more often. It also seems that, the more frequent people work out, the more they tend to eat out.
Exercise vs Fast Food Consumption:
27% of people answered A, 31% of people answered B, 16.4% of people answered C, and 5.45% of people answered D. (A= 0 times, B= 1-3 times, C= 4-7 times, and D=more than 7 times.)
|Fast food per week||Count of Fast food per week|
12.7% of people answered A, 23.6% of people answered B, 18.2% of people answered C, and 43.6% of people answered D. (A= 0 times, B= 1-2 times, C= 3-5 Times, and D= more than 5 times.)
|Exercise per week||Count of Exercise per week|
Fast.food.per.week A B C D
A 1 4 4 6
B 2 8 5 12
C 1 1 1 6
D 3 0 0 0
From this information, you can see, how many times people exercise a week and how many times they consume fast food and how those variables compare together.Per this, exactly half the sample eats out 1-3 times and the majority of people eat out 3 times or less. You can also see that the people that work out more than 5 times a week typically eat out 1-3 times per week.
Fast.food.per.week A B C D Total
A 6.7 26.7 26.7 40.0 100.1 15
B 7.4 29.6 18.5 44.4 99.9 27
C 11.1 11.1 11.1 66.7 100.0 9
D 100.0 0.0 0.0 0.0 100.0 3
Comparison of two variables by calculating the expected count of each:
Pearson's Chi-squared test
Null: No correlation between exercise and fast food
Alternative: There is correlation between exercise and fast food
X-squared = 24.156, df = 9, p-value = 0.004062
> .Test$expected # Expected Counts
1 2 3 4
1 1.9444444 3.6111111 2.7777778 6.666667
2 3.5000000 6.5000000 5.0000000 12.000000
3 1.1666667 2.1666667 1.6666667 4.000000
4 0.3888889 0.7222222 0.5555556 1.333333
The chi-squared stat tells you the difference between your observed count and the count you’d expect if there was no relationship. After looking at the expected values above and then looking at the values we observed, you can tell that for most of them are pretty close in range in terms of their value. A p-value of .004062 is a very low value and means that the null can be rejected, which means that there is significant correlation between the amount of exercise and the amount of fast food consumer by our sample on Uindy.
Gender vs Fast Food Consumption:
*The graph and count for fast food consumption is listed above^
*32 are Male, 22 are Female, Total = 54
*Reminder: Fast Food: (A= 0, B= 1-3, C= 4-7,D= 7+)
Male..FemaleA B C D
Female 6 13 1 2
Male 9 14 8 1
Male..Female A B C D
Female 40 48.1 11.1 66.7
Male 60 51.9 88.9 33.3
Total 100 100.0 100.0 100.0
Count 15 27.0 9.0 3.0
Percent of each individual gender, and how they fit into each fast food frequency:
Fast food per week
Frequency:(0) (1-3) (4-7) (7+)
Male/Female A B C D
Female 27.27% 59.09% 4.55% 9.09%
Male 28.13% 43.75% 25% 3.13%
After observing the data, it’s important to keep in mind that we surveyed more men than females, so looking at the frequency table alone will not help us determine if there is a correlation, we must focus on the percentage of each gender as a whole, and how those percentages are allocated across each frequency (A,B,C,D). Doing this will allow us to have an idea if there’s a correlation between gender and how often you eat fast food, prior to the Chi Squared test. From the data, you can see that, when it comes to the higher percentage of what gender eats out the most frequently (7+), that would be females, roughly 9% of the females surveyed eat out more than 7 times a week, compared to only 3% of the men. The percentages of male and female for fast food zero times a week is roughly the same at around 28%. From just right now, it appears that gender is not a direct factor when it comes to eating fast food.
Chi Squared Test for Gender v Fast Food:
Null: No correlation between Gender and fast food
Alternative: There is correlation between Gender and fast food
X-squared = 5.25002, df = 3, p-value = .05
> .Test$expected # Expected Counts
1 2 3 4
1 6.11 11 3.67 1.22
2 8.89 16 5.33 1.78
The chi-squared stat tells you the difference between your observed count and the count you’d expect if there was no relationship. Knowing this, and looking at values such as the p-value (.05) it can be concluded that there is significant evidence from our survey that can claim that gender and the amount of fast food consumed are correlated. The null can be rejected, if you look at differences between the expected and observed values there is not much of a difference, further proving why there is correlation.