MAT 243 Project One Summary Report (1)
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Southern New Hampshire University *
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243
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Mathematics
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Feb 20, 2024
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MAT 243 Project One Summary Rep
Ambrosia Miller
ambrosia.miller1@snhu.edu
Southern New Hampshire University
Notes:
Replace the bracketed text on page one (the cover page) with your personal information.
You will use your selected team for all three projects 1.
Introduction: Problem Statement
I am a basketball team’s new data analyst and I have found the largest set of historical data for multiple teams and now I am working to analyze and find any and all patterns in the data. My management and the coach are wanting to use this data to improve on the team, but only the people that will see and use this report that I have are not data analysts and is why I need to interpret my results and explain their implications. This analysis will revolve around the probabilities that the team can do worse or better at any given period rather than a chosen reference period and team. 2.
Introduction: Your Team and the Assigned Team
In this project, you picked a team and you were assigned a team to do comparative analysis. See Steps 1 and 2 in the Python script to address the following items:
The team that I chose do my analysis on is the Hawks and the years that are available to me are 2013-2015, for my comparison I will be choosing to incorporate statistical methods like data
visualization with examples like using a chart to compare the teams, meaning my team stated above the Bulls from 1996-1998. Table 1. Information on the Teams
Name of Team
Assigned Years
1. Yours
Hawks
2013-2015
2. Assigned
Bulls
1996-1998
3.
Data Visualization: Points Scored by Your Team
In the Python script, you created a visualization for the distribution of points scored by your team. The data visualization that I have reviewed is used to show information and is shown either in the format of either a table or chart. Displaying the data in this particular way allows for a quick viewing and easy interpretation, especially if there are large data sets on display. The plot I chose is the Histogram, and the reason I chose this particular plot is because it shows the overall spread of points for the Hawks as well as the frequency and how often those points were scored, to me. From the Histogram I can plainly see that
The Hawks scored a total points of 108 with a frequency of 48.
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4.
Data Visualization: Points Scored by the Assigned Team
Since I chose the Histogram for my team, I figured I might as well choose the Histogram for the Bulls as well. Between 1996-1998, the Bulls have scored a total of 102 points with
a frequency of 34. Which reading this Histogram is so much easier to read.
5.
Data Visualization: Comparing the Two Teams
I must keep my Histogram them e rolling, so here as well we can compare the two teams, I chose the Histogram and with data visualization can be done many ways such as being overlayed or side by side, here on Histogram it is overlayed. With it being overlayed we can observe both teams’ comparison more easily, in this Histogram you can see where the teams were very similar (almost identical) and then you can see the differences in areas.
6.
Descriptive Statistics: Points Scored By Your Team in Home Games
Table 2. Descriptive Statistics for Points Scored by Your Team in Home Games
Statistic
Value
Mean
Median
Variance
Standard Deviation
101.19
102.00
138.47
11.77
Here we can use the measures of central variability and tendency to summarize the distribution of the data set, this will be done by finding the average or mean of the data set, the median score for the set, as well as the variance in a distribution and we can not forget the standard deviation.
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So, for the Hawks you can plainly see the mean of the their score for their ‘home’ games and that result was 101.19, the median was 102.00, the variance was 138.47 and the standard deviation was 11.77. This does show a bell shape of the distribution. 7.
Descriptive Statistics: Points Scored By Your Team in Away Games
Table 3. Descriptive Statistics for Points Scored by Your Team in Away Games
Statistic Name
Value
Mean
Median
Variance
Standard Deviation
99.74
101.00
130.77
11.44
The ‘Away’ games were very similar as in comparison to the ‘Home’ games above, from reviewing the data set, I can safely say that it is not skewed, the Hawks scored in a home game of
the total points showing as 101.19 for the mean, and the standard deviation being 11.77, which is only a few points off. The variance is slightly off by a few points as well when you compare it to their ‘home’ points chart, now we will look at the median and this shows it being one point off from the home. Overall, the ‘home’ games are slightly higher than their away games for the Hawks.
8.
Confidence Intervals for the Average Relative Skill of All Teams in Your Team’s Years
Table 4. Confidence Interval for Average Relative Skill of Teams in Your Team’s Years
Confidence Level (%)
Confidence Interval
95%
(1502.02, 1507.18)
The data set with the confidence level is 95%, giving a likeliness that any given teams average relative skill within the era of 2013-2015. If we had used a different confidence level, such as 90% than the circumstances would be different, showing that there would be a decrease and the upper and lower limits from the confidence interval. 9.
Confidence Intervals for the Average Relative Skill of All Teams in the Assigned Team’s Years
Table 5. Confidence Interval for Average Relative Skill of Teams in Assigned Team’s Years
Confidence Level (%)
Confidence Interval
95%
(1487.66, 1493.65)
The confidence level that used for this data set was 95%, this shows that there is a 95% likeliness
that any of the team’s average relative skill within the time frame of the years 1996-1998 would, fall between upper and lower limits of the confident interval of 1487.66-1493.65 compared to 1502.02 -1507.18. Using another confidence level would change the intervals depending on the level. Comparing the confidences levels of the teams in the 2013-2015 data set and the 1996-
1998 data set is much lower than 2013-2015 teams intervals. 10. Conclusion
The very importance of the data analysis that was performed was designed to help the basketball’s team manager make educated and nearly perfect calls on how they need to improve their team’s importance. Using the confidence levels, inferences, and intervals can be made for how well a certain team’s performance can be compared to for overall relative skills of the entirety of the league. The information above helped the Hawks improve.
11. Citations
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