Mayfield Database Coursework

Mayfield Database Coursework

Data Handling Project

Introduction:

For the coursework, I will be using the Mayfield Database. MayfieldHigh School is a made up school, however, the data is real and based on students from KS3 to KS4 (Yrs7-11). The information gathered is a range of fields like height, weight, IQ, favourite etc. Mayfield contains 1,183 students:

Year Group / Boys / Girls / Total
7 / 151 / 131 / 282
8 / 145 / 125 / 270
9 / 118 / 143 / 251
10 / 105 / 94 / 200
11 / 84 / 86 / 170

Looking at the table, I can say:

• The school is growing, as the number of pupils in yr 7 is greater than the students in Yr 11.
• The amount of pupils in the school is not evenly distributed among the years.

For the coursework, I must take a sample from the database as to use all the data would take time and will be harder to use. Therefore, I will take a sample of 50 students, as it is small enough to manage but big enough to get a good range of the data.

Being secondary data, I have no need to make a survey and therefore the harder work has been done for me. The data I will use is numerical (i.e. quantitative), for I need to use IQ, KS2 results for English, Science and Maths. To collect my 50 students I will need to stratify so I can have the same proportion of students for each year and gender. For example:

The Method for Stratifying:

No. of Students in Year ' x Amount to be Sampled

Total No. of Students in school

E.g. Yr 7 boys:

151 ' x 50 =6.4 (to 1d.p.)

1183

= 6 (Cannot have 0.4 of a student)

E.g. Yr 11 girls:

86 ' x 50=3.6 (to 1d.p.)

1183

= 4 (Cannot have 0.6 so round up)

To make a fair but random collection I will use the calculator random button:

RAN# x No. of Students (In that required year group)

E.g. Yr 7 Boys

RAN# x 131=Random Number

These are my collections random numbers:

Year Group / Boys / Girls
7 / 120,57,124,101,54,38 / 78,59,69,104,89,46
8 / 44,79,116,37,11,43 / 118,102,58,13,43
9 / 37,31,58,38,89 / 44,17,46,18,8,20
10 / 67,5,56,105 / 40,35,5,89
11 / 10,18,13,70 / 33,81,54,52

Problems I may face while I do my collection is that some information may be missing. For example one student may not have an IQ or it wasn't written in the database. To solve this problem I will take another random student, using the RAN# button on my calculator.

Using these numbers, I can collect my sample:

Hypothesis:

• I believe there is a stronger correlation between IQ and Maths than IQ and English. This is because IQ tests are based on logical, analytical skills and maths. These questions do not relate with English.

Pre-test:

To see if these hypotheses are best to study I will make a pre-test. I will do this by taking 15 students at random and make a graph:

There seems to be a positive correlation for both Maths and IQ and Eng and IQ, therefore I will study this hypothesis.

Plan of Action:

For me to prove my hypothesis I will need to filter the sample with the data I need. For my hypothesis I need to use the IQs, English KS2 results and Maths KS2 results. This is what I am left with:

KS2 Results / KS2 Results
Year Group / Gender / IQ / English / Maths / Science / Year Group / Gender / IQ / English / Maths / Science
7 / Male / 112 / 5 / 5 / 5 / 9 / Male / 104 / 5 / 4 / 4
7 / Male / 102 / 4 / 4 / 5 / 9 / Male / 118 / 5 / 5 / 5
7 / Male / 103 / 4 / 5 / 4 / 9 / Male / 98 / 4 / 4 / 4
7 / Male / 103 / 5 / 4 / 4 / 9 / Male / 96 / 5 / 4 / 3
7 / Male / 102 / 3 / 3 / 5 / 9 / Male / 97 / 4 / 4 / 4
7 / Male / 104 / 4 / 3 / 4 / 9 / Female / 108 / 4 / 4 / 5
7 / Female / 98 / 4 / 4 / 4 / 9 / Female / 103 / 5 / 4 / 4
7 / Female / 105 / 4 / 3 / 4 / 9 / Female / 101 / 4 / 4 / 4
7 / Female / 100 / 4 / 3 / 3 / 9 / Female / 107 / 4 / 5 / 5
7 / Female / 101 / 4 / 4 / 4 / 9 / Female / 87 / 3 / 3 / 2
7 / Female / 91 / 3 / 3 / 4 / 9 / Female / 97 / 4 / 4 / 4
7 / Female / 128 / 5 / 5 / 5 / 10 / Male / 104 / 4 / 5 / 4
8 / Male / 101 / 4 / 4 / 4 / 10 / Male / 116 / 5 / 5 / 5
8 / Male / 100 / 4 / 4 / 4 / 10 / Male / 84 / 3 / 3 / 3
8 / Male / 127 / 5 / 6 / 6 / 10 / Male / 88 / 3 / 3 / 3
8 / Male / 112 / 5 / 5 / 5 / 10 / Female / 90 / 3 / 3 / 3
8 / Male / 104 / 5 / 4 / 4 / 10 / Female / 95 / 4 / 3 / 4
8 / Male / 101 / 4 / 4 / 4 / 10 / Female / 115 / 5 / 5 / 5
8 / Female / 96 / 4 / 3 / 4 / 10 / Female / 95 / 3 / 3 / 3
8 / Female / 122 / 5 / 5 / 5 / 11 / Male / 76 / 2 / 2 / 3
8 / Female / 94 / 3 / 4 / 4 / 11 / Male / 91 / 3 / 3 / 3
8 / Female / 102 / 5 / 4 / 4 / 11 / Male / 98 / 4 / 4 / 4
8 / Female / 111 / 5 / 5 / 5 / 11 / Male / 100 / 4 / 4 / 4
11 / Female / 92 / 3 / 3 / 4
11 / Female / 104 / 3 / 3 / 4
11 / Female / 100 / 4 / 4 / 4
11 / Female / 120 / 5 / 5 / 5

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