• Join over 1.2 million students every month
  • Accelerate your learning by 29%
  • Unlimited access from just £6.99 per month
Page
  1. 1
    1
  2. 2
    2
  3. 3
    3
  4. 4
    4
  5. 5
    5
  6. 6
    6
  7. 7
    7
  8. 8
    8
  9. 9
    9
  10. 10
    10
  11. 11
    11
  12. 12
    12
  13. 13
    13
  14. 14
    14
  15. 15
    15
  16. 16
    16
  17. 17
    17
  18. 18
    18
  19. 19
    19

Find the median body mass index (BMI) for females in the United States of America.

Extracts from this document...

Introduction

image00.png


The table below shows the median body mass index (BMI) for females in the United States of America (USA) for the year 2000 (source www.cdc.gov).

Age (yrs)

BMI

2

16.4

3

15.7

4

15.3

5

15.2

6

15.21

7

15.4

8

15.8

9

16.3

10

16.8

11

17.5

12

18.18

13

18.7

14

19.36

15

19.88

16

20.4

17

20.85

18

21.22

19

21.6

20

21.65

Using the two variables above in the table, the independent variable (age), and the dependent variable BMI which has the units kg.m-2. The graph below was generated using Microsoft Excel 2007.

image01.png

Using a GDC, the data was fitted to a set of parameters, a screen shot of these parameters can be seen below:

image02.png

It can be seenfrom the plot that one possible mathematical fit for the graph is a sine function. This is apparent because it has the same periodic nature (it appears to be ¾ of a period of a sine curve) as a sine function. The next step in modelling a generic sine function to the data in the plot above, and applying a number of changes to f(x) = sin(x) curve. There are quite a number of calculations that are needed to acquire an accurate model of the data. Each aspect of the graph is investigated separately, for example vertical shift.

The graph below shows you the general function of a sine equation.

y=Sin(x)

image11.png

...read more.

Middle

image21.png

image22.png

Now the model becomes, f(x) = image23.pngimage23.png

image24.png

The model function image25.pngimage25.pngis now compared to the plot of the original data table (BMI).

The graph below shows the model compared to the actual data using the GDC.

image26.png

image27.png

This can also be seen using excel, the model was applied to the same independent variable values (ages), the results can be seen in the following diagram.

Age (yrs)

BMI

Model

2

16.4

15.82473

3

15.7

15.48645

4

15.3

15.27683

5

15.2

15.20501

6

15.21

15.27413

7

15.4

15.48119

8

15.8

15.81712

9

16.3

16.26725

10

16.8

16.8119

11

17.5

17.42726

12

18.18

18.08646

13

18.7

18.76066

14

19.36

19.42042

15

19.88

20.03689

16

20.4

20.58313

17

20.85

21.03527

18

21.22

21.37354

19

21.6

21.58317

20

21.65

21.65499

image29.png

As you can see there is a slight difference between the model graph and the original data graph.

They both have similar curve nature. The model function has a constant up and down curve while the original data graph has one curve and then it remains constant.

By using excel we were able to find another graph that fits the data. This is a polynomial function.

The equation for the function is X5+X4+X3 +X2+X1+X by adding your data in the graph we were able to find that the polynomial function has the best fit to the graph.

image30.png

The graph below shows the function compared to the model:

image31.png

The difference between the sin function and the polynomial function was that the sin function has constant curve movement; however the polynomial function had the best fit.

...read more.

Conclusion

Calculating the different aspects of the sine function will help to obtain best fit for the data above:

Vertical shift

image34.png

image35.png

d = 18.4

f(x)= sin(x)+18.4

Amplitude

image36.png

image37.png

 a = 3.2

f(x)=3.2sin(x)+18.4

Period

image38.png

image08.png

B =image40.pngimage40.png

F(x)=3.2sin(image17.pngimage17.pngx)+18.4

Horizontal shift

image41.png

image42.png

image43.png

 b is known to be image40.pngimage40.png

image45.pngimage45.png=12.5

-c = 12.5 x image40.pngimage40.png

-c =2.61

C= -2.61

F(x)=3.2sin(image17.pngimage17.pngx-2.61)+18.4

Using excel the model was applied to the same independent variable values (ages), the results can be seen in the following diagram.

Age (yrs)

BMI

model

2

16.5

15.82473

3

15.6

15.48645

4

15.4

15.27683

5

15.2

15.20501

6

15.4

15.27413

7

15.5

15.48119

8

15.75

15.81712

9

16.4

16.26725

10

16.8

16.8119

11

17.5

17.42726

12

18.1

18.08646

13

18.6

18.76066

14

19.3

19.42042

15

20

20.03689

16

20.4

20.58313

17

20.9

21.03527

18

21.3

21.37354

19

21.5

21.58317

20

21.6

21.65499

image46.png

The graph below shows the model compared to the actual data using the GDC.

image47.png

image48.png

-There is a slight difference between the model graph and body mass index (BMI) of young Australian woman. They have a similar curve nature for a specific period before the sine function changes.

-The sine function has a constant up and down curve while the original data graph has one curve and then it remains constant.

-The BMI of the Australian females is very similar to the BMI of US females from the age of 2 -20.

-Until this point the sine function has been able to calculate the BMI of different woman in different countries, however if you apply more tests to the function with different countries and ages then you will see slight limitations to the graph which would require you to either recalculate the equation or use other functions.

...read more.

This student written piece of work is one of many that can be found in our International Baccalaureate Maths 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 International Baccalaureate Maths essays

  1. This portfolio is an investigation into how the median Body Mass Index of a ...

    If this data was a record of the extremes rather than the median BMI of a population, the y values could lower than the window setting of 12 for this graph, as well as much higher than the window setting of 24.

  2. A logistic model

    65000 60000 55000 50000 45000 40000 35000 30000 25000 20000 15000 10000 5000 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Year Figure 4.2. Growth factor r=2.3.

  1. Body Mass Index

    This final number is then the period of the function. The calculations are represented below: --> Therefore, the period or B for this function is 0.225. Next, to find the horizontal translation (C) of the function one must find the peak and trough of the function.

  2. Maths BMI

    so therefore, I would need to find three equations. I will be using simultaneous equations to find the values of the unknown variables. (1) x=5 is the line of symmetry as x= = 5 Using data from the graph, I let the x value be 3 and therefore, the y value is 15.7, (2)

  1. Virus Modelling

    Using this information I was then able to produce plot the points on a graph (using Autograph 3.2). Note: In the graph it uses E+n. This is equivalent to �10n. Again I chose the function y = a�10nx and using the Constant Controller option in Autograph I altered the constants a and n.

  2. Shady Areas. In this investigation you will attempt to find a rule to approximate ...

    dx = (xn - x0)/(2n) (g(x0) + g(xn) + 2[? g(xi)] ?31(9x)/ (Vx3 + 9) dx =(2)/(16) (g(3) + g(1) + 2[g(1.25) + g(1.5) + g(1.75) + g(2) + g(2.25) + g(2.5) + g(2.75)] = (2)/(16)[2.85 + 4.5 + 2 (3.40 + 3.84 + 4.16 + 4.37 + 4.48 + 4.53 + 4.54)] = 8.24625 ?ca (9x)/ (Vx3 + 9)

  1. Creating a logistic model

    18 60000 1 19 60000 1 20 60000 1 As we have done before, we should also use the GDC to find an estimate for the logistic function: However, the function that the calculator gives is just an estimate, so it is safe for us to round off this function

  2. Investigating Sin Functions

    Now, from these graphs, one would likely assume that as the Amplitude of the graph increases, the shape vertically stretches, and as the Amplitude decreases, the graph shrinks. However, one must consider possible limitations to this statement. Let me pose a question, what of negative numbers??

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