MAT 243 Project One Summary Report Brandon Meadows
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Jun 10, 2024
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MAT 243 Project One Summary Report
Brandon Meadows
Southern New Hampshire University
The reason for this summary report is so we can analyze massive sets of
historical data and figure out the data patterns. For my summary report, I am going to use descriptive statistics blended with data visualization techniques to analyze important variables allocation associated with team performance. By doing so I will provide an analysis that is going to assist each team’s management to help implement improvements to team performance. My data sets that I will
be covering are the Chicago Bulls from 1996 to 1998, as well as the Los Angeles Clippers for the years of 2013 to 2015. I will be implementing data visualization that will form charts which will aid in comparing NBA teams, and I will also be using confidence intervals that will calculate the team's average relative skills for the previously mentioned years.
I have picked the Los Angeles Clippers for the years of 2013-2015 for analysis. The Chicago Bulls for the years of 1996 to 1998 is provided for analysis and comparison.
Table 1: Teams and years of analysis Team Name
Years for analysis
1.Picked team
Los Angeles Clippers
2013-2015
2.Provided team
Chicago Bulls
1993-1995
Data visualization can display information in the configuration of a table
or a chart. By displaying data this way, it allows for fast interpretation and viewing and is notable when it comes to large data sets. For my plot I have went with a Histogram. My reason for going with a Histogram is because this plot will show the frequency and spread of points for the Los Angeles Clippers as well as how frequently the points were scored.
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For the years of 2013 to 2015 this histogram displays every point scored
for those three years and how often the points were scored. While analyzing the histogram, it shows that over the three-year period the Los Angeles Clippers have scored 100 points 40 times.
For the Chicago Bulls I have decided to go with a histogram that will show points scored and how often these points are scored for the Chicago Bulls for the years of 1993-1995. It is my belief that this type of plot is easier to interpret and provides a better breakdown of points scored for the three-year period provided. The histogram displays a pattern of 104 points scored by the Chicago Bulls was achieved 35 times.
For comparing different data allocations, data visualization can be used to display the data overlapping each other or right next to each other which can enhance the differences in each set of data. My example to achieve this is with these two histograms will be used to differentiate the Chicago Bulls points scored for 1993-1995 with the Los Angeles Clippers points scored for the years of 2013-2015 and how often these points were scored.
This plot displays the data best way for comparison. While analyzing the two plots, I noticed the Los Angeles Clippers scored 100 points
more often than the Chicago Bulls but that the Chicago Bulls scored 110
points more often than the Los Angeles Clippers. Table 2: Descriptive Statistic for points scored of the Los Angeles
Clippers for Home games
Name of the Statistic
Value
Mean
106.97
Median
106.0
Variance
116.21
Standard Deviation
10.78
To best summarize the allocation of this data set we may use measures of central tendency and variability. To best achieve this, we should find the Mean, which is the average of the data set, the Median which is the
middle points scored of this data set, the variance which is the diversity in this data set, and the Standard deviation of this data set.
According to the data set for the Los Angeles Clippers, the mean for points scored during home games from 2013-2015 is 106.97. Points scored during home games median for this period was 106.0. For the
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Clippers, the standard deviation was 10.78, thus the allocation of data is bell shaped.
Table 3: Descriptive Statistic for points scored by the Los Angeles Clippers in away games from 2013-2015
Name of statistic
Value
Mean
103.52
Median
103
Variance
128.65
Standard deviation
11.34
To best summarize the allocation of this data set we may use measures of central tendency and variability. To best achieve this, we should find the Mean, which is the average of the data set, the Median which is the
middle points scored of this data set, the variance which is the diversity in this data set, and the Standard deviation of this data set.
According to the data set for the Los Angeles Clippers, the mean for points scored during away games from 2013-2015 is 103.52. Points scored during home games median for this period was 103.0. For the
Clippers, the standard deviation was 11.34, thus the allocation of data is bell shaped.
Table 4: Confidence Intervals for the Average Relative Skill of All Teams in 2013-2015
Level of Confidence (%)
Confidence Interval
95%
(1502.02, 1507.18)
We use confidence intervals to guess the upper and lower limits of the data set. The estimated numbers are the population perimeter that has a specific level of confidence (Confidence Interval, CI). For all teams from 2013-2015 the level of confidence is 95% that the team’s relative skills will range from 1502.02 to 1507.18. For example. If there was a different level of confidence such as 99% this would change the Confidence Intervals as well as the probability for the population mean is included in the confidence interval is 99%. Choice 2 is the correct answer.
Table 5: Confidence Intervals for the Average Relative Skill of All Teams in 1996 to 1998
Level of Confidence (%)
Confidence Interval
95%
(1487.66, 1493.65)
We use confidence intervals to guess the upper and lower limits of the data set. The estimated numbers are the population perimeter that has a specific level of confidence (Confidence Interval, CI). For all teams from 1996-1998 the level of confidence is 95% that the team’s relative skills will range from 1487.66 to 1493.65. For example. If there was a different level of confidence such as 99% this would change the Confidence Intervals as well as the probability for the population mean is included in the confidence interval is 99%. The chances of a team having relative skills less than the Chicago Bulls from 1996 is 0.9732 or 97%.
As the histograms of two plots are laid one over another it displays how
close to points and how often the points are scored for both teams. This also shows how often the points scored for each team are not remarkably close to one another. The teams of the Clippers and Bulls average 90 to 120 points per game for each team in the provided years
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with relativity equal repetition. When the number of points scored per game increases, Clippers score more points more often than the Bulls.
The relative skill of the Los Angeles Clippers in 2013-2015 is higher than the 1996-1998 Chicago Bulls. The Clippers achieved a lower variance and standard deviation than the Chicago Bulls had. This shows that Clippers had less irregularities and more frequently had better relative skills from 2013 to 2015 than the 1996-1998 Chicago Bulls had. Los Angeles Clippers mean, and median were also larger than the Chicago Bulls. By comparing both data sets to me it shows that the Los Angeles Clippers in 2013-2015 were a better team than the 1996-1998 Chicago Bulls. Even though the 1996-1998 Chicago Bulls won three straight NBA championships.