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Data Management CPT.
Transcript of Data Management CPT.
Weight: 113 kg
Birthday: December Some of the top 50 NBA players include: Kobe Bryant Culminating Project. I focused only on the top 50 NBA players for 2012. I chose to use only the current top 50 for both because I wanted to use the most recent data and I wanted to have enough information. i was only going to use the top 10 at first until i realized it was not enough data to support my thesis. Dwyane Wade. Height: 1.93 m
Weight: 100 kg
Birthday: January Lebron James Height: 1.98m
Weight: 93 kg
Birthday: August I based my data off of 4 things specifically: Weight
Total career points This graph shows in which month all of the 50 athletes are born.
For example, you can see that 6 people out of 50 are born in january.
The graph could be misleading because it looks like there is such a big difference in the intervals of the Y axis when really it is only going up by 1, This is a graph i made showing the probability of becoming a top 50 nba player depending on which month you were born in.
For example if you were born in january, there would be a 12% chance you would become an nba player.
Looking at this table you can tell that the results i got were different than what i stated i thought would happen in my thesis. This graph shows the average of being a top 50 nba player wiith 3 month intervals. So from January to March, April to June, July to September, and October to December.
January, February, and March are the 3 months i tried to focus on the most because after doing some research it turns out that those born in those 3 months were generally better athletes and more common than being born in July, August, or September. I thought using the point scores would be a good idea because it would help show the correlation between height and weight, height and points, and weight and points. The point scores will make it clearer to see which athlete was better than the other. The taller the athlete is, the higher the chance that he will have more points Point Scores The total career points ranked from 2591 to 46872. The table i created did show that in most cases the taller the athlete was, the more points he had. ( in most cases) Correlations. The Correlation for Height vs. Weight was 0.038155152 which demonstrates that there was no correlation between the two.
Points vs. Height: 0.080730244
This shows that there is a weak correlation between the players’ total career points and his height.
Points vs. Weight: -0.107652507
There is a medium correlation between the players’ total career points and his weight. I conducted a survey which was handed out randomly to a mixed grade 12 fitness class. The survey had the following questions on them:
1) are you born in the summer? ( june 21st-september 23rd)
2) do you play any sports?
3) what is your height?
4) how many sports do you play?
5) what is your gender?
6) do you plan on becoming a professional athlese? (NBA,NFL,NHL,etc)
7) do you like the winter or summer better? Are you born in the summer? 9 answered no and 5 answered yes.Do you play any sports? 4 answered no, 10 answered yes.Those who were born in the summer AND played sports: 5.Those who were born in the summer and didn’t play sports: 1.Those who were not born in the summer and play sports: 5.Those who were not born in the summer and don’t play sports: 3. After looking over the survey i was able to link a couple questions together so that they could relate to my topic directly. I thought the answers to my survey would be different In that, there would be more kids born in the summer who ALSO played sports but they both tied. This was when I realized that maybe my theory wasn’t as right as I thought it would be.
It was also only a class of 20 people and all guys for the exception of 2, so it could be bias in that sense. Analysis Outline For the green analysis I chose to show a couple graphs to make it easier to understand the data instead of simply using a table or a chart. I also showed the averages and a line graph to show whether or not it had a normal distribution. For the blue analysis I found the mode, median, mean, and the standard deviation using the formula bar on excel. It made the process faster and more precise. For the red analysis I added a points column to the table so that I could find the correlation and the variance between height and weight, height and points, and weight and points. For the yellow analysis I made a total points graph to show the different skills between the athletes and an average weight/height graph. For the white analysis I used my survey. I thought a survey would be a good source of information instead of simply relying on data found online. When I first started this project, I thought it would make sense that people who were born in the summer were better athletes because it meant they were older than the rest and usually, older means bigger and taller. Once I started my research, I found out that drafting (for the NBA anyways) was in June. Which meant that people born in the summer did not have as much practice and experience as others born in the winter. Final Analysis. The athletes born in the winter had from 5-6 months more of practicing and training. 5-6 months that athletes born in the winter never got. Also, the systems set up to determine who gets ahead in sports aren’t particularly efficient. Starting all star leagues etc, as early as possible is not fair to everyone because for example, if there were no players born in July, October, November, or December and only a few in august and September, those born in the last half of the year would’ve all been overlooked. After doing all my research and collecting all of my data, I realized that people born in the winter (December, January, February) had a bigger chance of becoming greater athletes and got training experience others wouldn’t. In the current top 50 NBA players, 28% of them were born in the first 3 months. Which is a higher percentage than any other months. "Dwyane Wade." Yahoo! Sports. STATS LLC, n.d. Web. 16 Jan. 2013."NBA & ABA Career Leaders and Records for Points." Basketball-Reference.com. Sports Reference LLC, n.d. Web. 16 Jan. 2013."NBA - CBSSports.com News, Scores, Stats, Fantasy Advice." CBSSports.com. Cbs Sports, n.d. Web. 16 Jan. 2013."NBA.com, Official Site of the National Basketball Association." NBA.com. NBA Media Venture, n.d. Web. 16 Jan. 2013. Bibliography.