• Join over 1.2 million students every month
  • Accelerate your learning by 29%
  • Unlimited access from just £6.99 per month

Investigate the correlation between the field goals attempted (FGA) and field goals made (FGM) of 50 different basketball players in NBA.

Extracts from this document...

Introduction

Philip Ng                                                                                                        25/03/2003

Statistics 2 Coursework

Aim

In my coursework, I am going to investigate the correlation between the field goals attempted (FGA) and field goals made (FGM) of 50 different basketball players in NBA.

It is worth to do because it will prove if the players attempt more field goals, whether he will get more points or not. Furthermore, the accuracy of shooting is dependent on many factors, such as the performance of players, home and away match, the shooting distance, the player’s position. To consider these factors, the percentage of field goals should be different from each NBA players. Also, it is useful to discuss whether a player will get more points if he makes more shootings in the games. Because it is necessary for the coach to know whether a reliable player will keep his accuracy on shooting even if his field goals attempted is large, and to find out whether it is easier to get points inside rather than outside in the basketball court. This is the important factor to win the match.

Data Collecting

The data is collected from NBA 2003 league. There are totally 476 players in NBA, and 29 teams, 65 international players from 34 countries.

As I only need 50 sampling, so I choose my 50 sampling randomly from different teams. In my sampling, it contains Centre, Power Forward, Small Forward, Shooting Guard, and Point Guard. Field goals attempted (FGA)

...read more.

Middle

763

355

Chris Webber ( Sacramento Kings)

1,069

496

Antawn Jamison ( Golden State Warriors)

1,110

515

Donyell Marshall ( Chicago Bulls)

788

365

Amare Stoudemire ( Phoenix Suns)

650

301

Karl Malone ( Utah Jazz)

1,026

475

Kenyon Martin ( New Jersey Nets)

859

397

Mike Bibby ( Sacramento Kings)

520

240

Predrag Stojakovic ( Sacramento Kings)

804

371

Steve Nash ( Dallas Mavericks)

859

396

Vlade Divac ( Sacramento Kings)

554

255

Lorenzen Wright ( Memphis Grizzlies)

571

262

Kerry Kittles ( New Jersey Nets)

534

245

Tony Parker ( San Antonio Spurs)

802

367

Tracy McGrady ( Orlando Magic)

1,454

665

Drew Gooden ( Orlando Magic)

712

324

Richard Hamilton ( Detroit Pistons)

990

450

Eric Snow ( Philadelphia 76ers)

634

288

Kobe Bryant ( Los Angeles Lakers)

1,520

689

Corliss Williamson ( Detroit Pistons)

638

289

Scottie Pippen ( Portland Trail Blazers)

582

262

Juwan Howard ( Denver Nuggets)

992

446

Gary Payton ( Milwaukee Bucks)

1,197

537

Desmond Mason ( Milwaukee Bucks)

794

355

Gilbert Arenas ( Golden State Warriors)

895

398

Modelling procedures

In the case of the data in my sample, there are two variables, FGA and FGM. This is an example of bivariate data, where each item in the population requires the values of two variables. The best way I can do to present these data is to plot a scatter diagram. However, I have to decide which variable is independent and which is dependent. The independent one is going to be x-axis, and the dependent one is going to be y-axis. Anyway, it is very obvious in my sample that FGA must be independent, because the player has to attempt the field goal for the field goal made in the game. So FGA is my x-axis, and FGM is my y-axis.

...read more.

Conclusion

As a result, my data are in good quality.

However, there ae some possible sources of error, which may have affected my data.

image03.pngimage02.pngimage01.png

From the scatter diagram, it shows that there are outliers in my sample, we regard these as outlier because these two sample are far awasy from the group of data. These outlier may make the correlation becomes more positive. The correlation may get closer to +1.

And from my data source, they are collected from NBA league. However, I think that I can improve the data, by collecting the sample which is not only from NBA league, but also in the other countries, like Britain or China. Because NBA, the league in Britain or China are at different level, it is clear that NBA players are much better than the players in China. So ensure that FGM(field goals made) is based on FGA(field goals attempted), without considering the ability of players, the best way to do is to collect the data from more different leagues. Also, to take even more sample to ensure that the sample is really large enough to represent its parent population. Finally, the data should be collected from the professional players only, this is also the restriction. Because only the professional players can keep his accuracy from time to time. We should not collect the data from the junior basketball match, like inter-house basketball in the school, but the large league like NBA or universities league.

...read more.

This student written piece of work is one of many that can be found in our AS and A Level Probability & Statistics section.

Found what you're looking for?

  • Start learning 29% faster today
  • 150,000+ documents available
  • Just £6.99 a month

Not the one? Search for your essay title...
  • Join over 1.2 million students every month
  • Accelerate your learning by 29%
  • Unlimited access from just £6.99 per month

See related essaysSee related essays

Related AS and A Level Probability & Statistics essays

  1. The mathematical genii apply their Statistical Wizardry to Basketball

    population from the sample enabling me to predict population parameters with greater confidence. Using the parameters I should be able to compare the populations by considering sample parameters. I have chosen a geometric model because it is an infinite distribution requiring discrete random variables and is able to accommodate the

  2. Statistics coursework

    IQ Frequency Cumulative Frequency Percentage of total 60<IQ<70 1 1 1.49 70<IQ<80 1 2 2.99 80<IQ<90 4 6 8.96 90<IQ<100 15 21 31.34 100<IQ<110 40 61 91.04 110<IQ<120 6 67 100 120<IQ<130 0 67 100 130<IQ<140 0 67 100 - IQ of girls in year 11 (Table 5)

  1. Aim: in this task, you will investigate the different functions that best model the ...

    Logarithmic trend line: * The logarithmic trend line (f(x) = 30561.48 In(x) - 230995.52) appears to fit the data almost identically to the linear line, with it missing the data points at 1950 and 1965. However, as it is a logarithmic line, it is likely that the curve will level out in the future - which isn't likely to actually happen in the real world.

  2. Statistics. The purpose of this coursework is to investigate the comparative relationships between the ...

    The last hypothesis is proved to be correct in this graph, though it is not as strong as I would have liked: it has a weak correlation of R2 = 0.2645. As the gradient is 16,257, every additional owner adds approximately 16250 miles to the car.

  1. Differences in wealth and life expectancy of the countries of the world

    23,200 72.51 77.60 74.99 West Bank 800 71.14 74.72 72.88 Mean 8,621 68.45 72.46 69.30 Data Tables Africa Countries GDP - per capita ($) Male Life Expectancy Female Life Expectancy Population Life Expectancy (years) Burundi 600 42.73 44.00 43.36 Cape Verde 1,400 66.83 73.54 70.14 Cote d'Ivoire 1,400 40.27 44.76

  2. Investigate if there is any correlation between the GDP per capita ($) of a ...

    The aim of my investigation was to see if there is any correlation between the GDP per capita ($) of a country and the life expectancy at birth (years). I can now confidently say that I have achieved my aim as there is a positive correlation as predicted.

  1. Is there a Correlation between GCSE Mathematics and English Literature scores?

    ( y - y ) ( x - x )� ( y - y )� (x - x )( y - y ) 2 1 -1.52 -2.08 2.3104 4.3264 3.1616 4 4 0.48 0.92 0.2304 0.8464 0.4416 5 4 1.48 0.92 2.1904 0.8464 1.3616 4 2 0.48 -1.08 0.2304 1.1664

  2. Design an investigation to see if there is a significant relationship between the number ...

    I felt that this would be difficult to do, as the shelved structure of the bay would mean a grid would inevitably incorporate the ledges and gullies caused by this shelved structure. However, a line transect would avoid these gullies.

  • Over 160,000 pieces
    of student written work
  • Annotated by
    experienced teachers
  • Ideas and feedback to
    improve your own work