The Data Handling Project

Introduction

I am investigating to find a relationship between ks2 results and IQ in children and to determine whether there is a significant difference between boys and girl's ks2 results and IQ.

My hypothesis is that there is a relationship between IQ and ks2 results, this means that is if a person's ks2 result is high their IQ should also be high.

My second hypothesis is that there is a difference between boys and girls intelligence.

I intend to do this by comparing them in stem and leaf diagrams, box and whisker diagrams and scatter diagrams. By doing this it will help me support my hypothesis.

I will investigate this theory by taking a sample from the Mayfield High School database. I have chosen this source because it will save me the time going to get the data. This means that the data is secondary so it could be biased so I will discard any information I believe to be unreliable because if the data is strongly biased and out off the correlation of the rest of my data it will affect my results. From this, I will extract 100 random people, 50 girls and 50 boys this is because we need an equal number so that we can compare them to each other. I will use 50 Girls and boys IQ and ks2 total scores because it is a big enough to represent the whole of the data but is not too big so that it would use up all of the time allocated. First I will have to collect the information that I need, that is each pupil's IQ, gender and ks2 results. I need these so I can put them in my diagrams and calculations. Then I will sort out this data so that there will be a proportionate sample of all the years and sexes, I will do this by doing a stratified sample this will also make it not biased. This means the number of people in each sample shall by decided by how many pupils there are in each year. A stratified sample is where a group of people is divided by their category, and chosen at random. The amount of people chosen at random in each category is decided to the proportion of the whole group.

Boys

Girls

Total

No. Of pupils in the sample

Actual No. For sample

Y7

51

31

282

282/1183*100=23.4

24

Y8

45

25

270

270/1183*100=22.8

23

Y9

18

43

261

261/1183*100=22

22

Y10

06

94

200

200/183*100=16.9

7

Y11

84

86

70

70/1183*100=14.4

4

Total

604

579

183

00

This table shows how to work out the proportion of children needed from each age group according to how many people there is in one year in relationship to the whole sample. From this table I found how many students I will need from each year they are 12 boys and 12 girls from year 7, 12 boys and 11 girls from year 8,11 boys and 11 girls from year 9, 8 boys and 9 girls from year 10,7 boys and 7 girls from year 11. The number of boys and girls in some of the years is not the same because the number of sample pupils for every year is not even, therefore we cannot have the same amount of boys and girls in certain years the same with the sample size that I have.

Introduction

I am investigating to find a relationship between ks2 results and IQ in children and to determine whether there is a significant difference between boys and girl's ks2 results and IQ.

My hypothesis is that there is a relationship between IQ and ks2 results, this means that is if a person's ks2 result is high their IQ should also be high.

My second hypothesis is that there is a difference between boys and girls intelligence.

I intend to do this by comparing them in stem and leaf diagrams, box and whisker diagrams and scatter diagrams. By doing this it will help me support my hypothesis.

I will investigate this theory by taking a sample from the Mayfield High School database. I have chosen this source because it will save me the time going to get the data. This means that the data is secondary so it could be biased so I will discard any information I believe to be unreliable because if the data is strongly biased and out off the correlation of the rest of my data it will affect my results. From this, I will extract 100 random people, 50 girls and 50 boys this is because we need an equal number so that we can compare them to each other. I will use 50 Girls and boys IQ and ks2 total scores because it is a big enough to represent the whole of the data but is not too big so that it would use up all of the time allocated. First I will have to collect the information that I need, that is each pupil's IQ, gender and ks2 results. I need these so I can put them in my diagrams and calculations. Then I will sort out this data so that there will be a proportionate sample of all the years and sexes, I will do this by doing a stratified sample this will also make it not biased. This means the number of people in each sample shall by decided by how many pupils there are in each year. A stratified sample is where a group of people is divided by their category, and chosen at random. The amount of people chosen at random in each category is decided to the proportion of the whole group.

Boys

Girls

Total

No. Of pupils in the sample

Actual No. For sample

Y7

51

31

282

282/1183*100=23.4

24

Y8

45

25

270

270/1183*100=22.8

23

Y9

18

43

261

261/1183*100=22

22

Y10

06

94

200

200/183*100=16.9

7

Y11

84

86

70

70/1183*100=14.4

4

Total

604

579

183

00

This table shows how to work out the proportion of children needed from each age group according to how many people there is in one year in relationship to the whole sample. From this table I found how many students I will need from each year they are 12 boys and 12 girls from year 7, 12 boys and 11 girls from year 8,11 boys and 11 girls from year 9, 8 boys and 9 girls from year 10,7 boys and 7 girls from year 11. The number of boys and girls in some of the years is not the same because the number of sample pupils for every year is not even, therefore we cannot have the same amount of boys and girls in certain years the same with the sample size that I have.