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MATH 1401 Final Project Instructions
The goal of this project is for you to find a way of applying the things you learned in the
Probability and Statistics course during this term. The topic choice is yours, but it needs to be
feasible (able to be completed in eight weeks). It also should be interesting and fun! To earn the
full points you have to do the following requirements:
1 Choose a good and feasible question.
2 You have thirty-two days to decide on a topic for your project, then send me a
description of what you want to do by email or in the drop box Project First
Draft not later than July 10 by 11:59 PM. (You get 30 percentage points for this
part of the project - this is part of your grade!)
3 Design an appropriate survey, study or experiment.
4 Collect good data; they may come from a survey, observational study, experiment, or othe
sources such as publications or the internet.
5 Summarize your data using appropriate graphical displays, summary statistics, and ve
al
descriptions.
6 Make sound inferences/conclusions based on your data.
7 State your conclusions clearly.
8 Submit a complete written report to the Drop Box I created for the Final Project.
Note: It is critical to note that no extensions will be given for any of the project due dates
for any reason. Late days may not be used. Projects submitted after the final due date will
not be graded. If you anticipate any issues (e.g., due to business travel) you need to send me
an email at least one week in advance.
Proposal You start your project by forming your groups and letting me know what topic
you are interested in exploring by submitting
1. Your group member’s name if you are doing in group.
2. Background and Motivation
Discuss your motivations and reasons for choosing this project, especially any background
or research interests that may have influenced your decision.
3. Project Objectives What are the scientific and inferential goals for this project? What
would you like to learn and accomplish? List the benefits.
4. What Data? From where and how are you collecting your data?
5. Design Overview List the statistical and computational methods you plan to use.
Project due date
Your project proposal is due on July 10 and final project is due on July 26 (no extensions).
Project Proposal: Samantha Renz
This is what my paper has to be about
1. I will not be working in a group.
2. My question is “does height affect shooting percentage in basketball?” The reason I chose this topic is because I have
others and friends that play basketball and I think it would be fun to learn about.
3. I would like to learn if height has any effect on shooting percentage.
4. I will be collecting my data by using basketball players from the NBA and their shooting percentages vs height to create an answer.
5. I will randomly select different NBA players from different teams with different heights and compare my results.
Math 2600 Probability and Statistics
30 March 2015
Group Members:
Melanie Hammond
Sarah Hecke
Joley Neubert
Is There a Positive Co
elation Between Year and Distance Jumped?
Our group’s goal for this project is to investigate the data given to us by StatCrunch. Our overall goal is to decide if there is a positive trend of increasing the distance of long jumping over the years, and if the year influences the distance jumped. Through statistical devices, we will discover with an increase in year, if there is an increase in athletic ability in the event of long jumping. By understanding the data and what it means, we will be able to fully understand our conclusion. We will present our data in the format of a scatter plot to accurately depict our data in a visual format.
We received our data from StatCrunch. The data was titled “Long Jump”, and provided us with various years and a co
esponding long jump sample shown in meters. We decided our data will be more accurate if we take all of the provided data and organize it by chronological order from earliest year to latest year. Once we organized the data, we plan to find the mean, standard deviation, and co
elations for the entire set of data as a whole. The graphs will show the possible increase in distance jumped throughout the years. After our calculations are complete, we will conclude if there was a general trend of increasing long jump distances throughout the years.
Year
Distance Jumped (m)
1896
6.35
1900
7.185
1904
7.34
1906
7.2
1908
7.48
1912
7.6
1920
7.15
1924
7.44
1928
7.73
1932
7.64
1936
8.06
1948
7.82
1952
7.57
1956
7.83
1960
8.12
1964
8.07
1968
8.9
1972
8.24
1976
8.35
1980
8.54
1984
8.54
1988
8.72
1992
8.64
1996
8.5
2000
8.55
2004
8.59
This histogram graph shows the frequency of the meters jumped. By using the histogram, we can organize the data in an easier format to read, and understand. The graph shows that there are 8 years that have an 8.5-9 meter jump. The yellow line represents the mean of the data, which is easily represented on this graph.
After organizing our data, we found that the minimum distance jumped was 6.35 meters, and the maximum distance jumped was 8.9 meters. The average distance jumped was 7.93 meters, while the median was 7.95. The sample standard deviation was .623. This shows us that while the data is varied by jumper, all of the jumps fall within three standard deviations of the mean. We can prove this by the equation: XXXXXXXXXX), and XXXXXXXXXXThis equation shows that there are no outliers in the data, and that we have a relatively normal shaped distribution based on the XXXXXXXXXXrule. The graph below explains the line of best fit. We can use this equation to predict possible future jumps: Meters = XXXXXXXXXX XXXXXXXXXXYear. If we wanted to predict the distance that will be jumped in 2016, we use this formula and get the distance of 9.001 meters. This is the highest distance jumped, and supports our conclusion of distance being positively co
elated by the year.
After examining the scatter plot, there appears to be a positive co
elation shown between the year and the meters jumped. The co
elation for this data set is r = 0.9058, which is a very strong association. This tells us that there are other factors that contribute to the distance jumped, but the year is also a strong contributor. We have concluded that while distance jumped does depend on the year, the year is not the only contributing factor. Other factors could include age, new advances in technology, experience, etc.
The biggest issue we had during this project was with the data. For example, the data from StatCrunch was not in a consistent interval, so the years were not consistently represented. Also, the data was not specific. We were not given if the meters jumped were an average for the year, or even the highest jump of the year, so it was hard to decipher the data given. Overall, we were able to prove that there is a positive co
elation between the year and meters jumped.
The equation of the regression line in the above scatterplot is: Meters = XXXXXXXXXX XXXXXXXXXXYear).