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  • Level: GCSE
  • Subject: Maths
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Maths GCSE Handling Data Coursework Mayfield High School - Year 10 & 11

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Maths GCSE Handling Data Coursework Mayfield High School - Year 10 & 11 For my statistics coursework I am going to investigate the relationship between the height and the weight. I will use a wide range of mathematical techniques to present my data and findings in different ways. I will also focus to test my hypothesis which is: As the height increases the weight increases and also the relationship between these the height and the weight will become stronger as they get older. I also think that there will be a difference in this between the boys and the girls. The table below shows the number of boys and girls there are in each year group: Year Group Number Of Boys Number Of Girls Total 10 106 94 200 11 84 86 170 Total 190 180 370 For my project I will take a random sample of 30 students. I will use the random sample button on a calculator to do this. Year 10 No of boys: (106/370) x 30 = 8.5946 � 9 No of girls: (94/370) x 30 = 7.6216 � 8 Year 11 No of boys: (84/370) x 30 = 6.8108 � 7 No of girls: (86/370) ...read more.


= 18.98045 55.93333 - 18.98045 = 36.95288 55.93333 + 18.98045 = 74.91378 So anything less than 36.95288 and more than 74.91378 is an outlier. Hence, there is no outlier in my sample. Tally chart for the height Tally chart for the weight Height is a continuous data, so you Weight is also a continuous need to use class intervals. data; the class interval I've used I've used a class interval of 0.05 m. is 5kg Height (cm) Tally Frequency 1.50?H<1.55 I 1 1.55?H<1.60 IIII I 6 1.60?H<1.65 IIII II 7 1.65?H<1.70 IIII 4 1.70?H<1.75 IIII 5 1.75?H<1.80 III 3 1.80?H<1.85 IIII 4 Weight Tally Frequency 35?W<40 II 2 40?W<45 I 1 45?W<50 IIII 4 50?W<55 IIII I 6 55?W<60 IIII I 6 60?W<65 IIII I 6 65?W<70 II 2 70?W<75 III 3 The tally chart and the table of my sample are not very useful to compare my results. So you need to present them in different ways to compare the data. I've recorded my results on a histogram, cumulative frequency diagram and a scatter graph which will be helpful to compare my results and test my hypotheses. Histogram of heights Histogram of weights Cumulative frequency of height Cumulative frequency of weight The equation for my line of best fit is y=mx ...read more.


The mean for the height of boys sample is: (1.63+1.77+1.32+1.62+1.60+1.60+1.65+1.68+1.60+1.80+1.75+1.72+1.81+1.82+1.68+1.54+1.50+1.62+1.62+1.73+1.52+1.84+1.75+1.61+1.80+1.57+1.52+1.78+1.63+1.80) 30 Mean = 1.662667 Standard deviation = 0.11948 (2 x 0.11948) = 0.23896 1.662667 - 0.23896 = 1.429707 1.662667 + 0.23896 = 1.901627 Thus, anything less than 1.429707 and anything more than 1.901627 is an outlier. In my sample there is only one outlier. I am going to leave this as it is because sometimes you might have someone shorter and he is not that short he is just approximately 0.10m shorter than the outlier range. The mean for the weight of boys sample is: (40+57+45+52+38+47+54+59+51+72+68+54+54+57+72+76+35+72+50+50+38+78+57+56+63+54+45+37+50+68) 30 Mean = 54.96667 Standard Deviation = 11.98126 (2 x 11.98126) = 23.96252 54.96667 - 23.96252 = 31.00415 54.96667 + 23.96252 = 78.92919 Anything less than 31.00415 and more than 78.92919 is an outlier. There is no outlier The mean for the To compare my correlation I am going to use product momentum correlation coefficient. This is the accurate way to compare the correlation. It uses the mean of each set of data and looks at the distance away from the mean of each point. The Formula is Where and are the means of the x and y values respectively ?? ?? ?? ?? 1 ...read more.

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