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Created: Oct 19, 2017
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Project report
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Report Introduction

This is the result of my analyses of the data that I collected for my project. Since I came to the U.S. I have noticed that a lot of students spent a great amount of time doing exercise, either going to the gym, practise a sport or just riding a bike. Also, since I work in the dining hall of the college, it called my attention how much some students eat. So, I thought that it was a good idea for my project to try to find if there was a connection between these two facts that I observed.

The data was collecting by sending my survey through Facebook to friends that are Baldwin Wallace students and I also had to do the survey asking people directly (face to face).

Data set 1. Responses to my Statistic Project   [Info]
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Data Set #1: qualitative.

The question I had to ask to collect this data was: “Which continent are you from?”

The variable name that describes my data is: “Continents”

Result 1: Continents frequency -bar graph-   [Info]
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Result 2: Continent Frequency table   [Info]
Frequency table results for Continent:
Count = 50
North America29
South America6


Result 3: Continent - pie chart-   [Info]
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These graphs are showing how many students are from each different continent in the world. According to the result for this variable, it is easy to recognize a large number of students who are from North America for obvious reason. 

Data Set #2: quantitative – univariate

The question I had to ask to collect this data was: “How many semesters have you been living at BW?”

The variable name that describes my data is: “Numbers of semesters”

Result 4: # of semesters - mean, median, mode,   [Info]
Summary statistics:
ColumnMeanMedianModeStd. dev.
# of Semesters3.1112.6438222

Result 5: # of semesters - table frequency   [Info]
Frequency table results for Bin(# of Semesters):
Count = 50
Bin(# of Semesters)Frequency
1 to 329
3 to 54
5 to 78
7 to 97
9 to 112

Result 6: # of semesters - histogram   [Info]
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Result 7: # of semesters - boxplot   [Info]
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Lower fences: Q1 – (1.5 * IQR) = -5

Upper fences: Q1 + (1.5 * IQR) = 11

My sample data is more approximate to a skewed right distribution. I determinate this because of the shape of the histogram (the tail to the right of the peak is longer than the tail to the left of the peak, that in this case, it does not exist). In my specific project, this means that I got more people who are at the beginning of their college life and, as the number semesters living in BW increase, there are fewer people.  

Data Set #3: quantitative – bivariate

The questions I had to ask to collect this data were: “How many hours do you spend working out per day?” and “How much money do you spend on food per day?”

I used for my explanatory variable, x, the variable name: “quantity of hours working out”. And, for my response variable, y, I used the variable name: “money spent on food”.

Result 8: Scatter Plot hours-money   [Info]
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Result 9: Correlation between hours-money   [Info]
Correlation between Hours working out and Money spend on food is:

The critical value for n= 50 is: 0.273So, because 0.512 › 0.273 I can say that does exist a positive linear relationship between the two variables. However, in the scatterplot, we cannot easily notice that.

The Regression Equation

𝑦̂ = b1.x + b0.

𝑦̂ = 5.6608 x + 12.1520

Using this equation as a model to make a prediction that is within the scope of my data, for instance, 2.5 hours working per day, I compute:

𝑦̂ = 5.6608 * (2.5) + 12.1520 = 26.3

In order to this, I can predict that a student that work out 2,5 hours per day, he/she is going to spend $26,3 dollars per day on food.

In conclusion, I would like to state that the more time a student spent working out per day, the more money he/she spent on food per day. The lurking variables that I can find in this relationship are the economy and health issues in general.

Report Summary/Feedback

By completing this report I learned how to create a survey and a report. also, I found this project very useful to refresh past content and it was very interesting to analyse actual data that I collect. 

The most difficult part completing this report I think it was collecting the data because a lot of people do not pay attention to social media survey, so I had to do it by myself interview people and that takes me some time. In addition, I find the format of the report in Statcruch a little bit tedious. 

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