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  • Level: GCSE
  • Subject: Maths
  • Word count: 1826

Data handling - IQ correlation

Extracts from this document...

Introduction

COURSEWORK- MATHS DATA HANDLING MAYFIELD HIGH SCHOOL Mayfield High School is a fictitious high school. There are 1183 pupils in the database I have on the school. This data has come from the exam board, and so is made up. I am going to examine the many features of the data, and investigate the inter-effect they all have on each other. The data base has 27 features for each pupil: Year group, surname, forename 1 and 2, age in years and months, month of birthday, gender, hair colour, eye colour, whether the person is left or right handed, favourite colour, favourite sport, favourite lesson, favourite TV program, Average number of hours TV watched per week, IQ, height, weight, distance travelled to school every day, type of transport to school number of siblings, number of pets, and Key Stage Three results in maths, science and English. The data that I am most interested in examining is IQ. The IQ is an intelligence test, and I will be investigating how the IQ of a person relates to the other aspects of that person. I will investigate these three hypotheses from the data I have: 1. The lower the IQ of a person is, the more hours of TV that person will watch per week. 2. The older a person is, the higher that persons IQ is. ...read more.

Middle

Although it is possible for someone to get these scores, the average IQ is 100, so I have considered the evidence and decided to discount it, as the final result may be anomalous if I choose to include this data. Likewise, I have not only discounted data in the "hours of TV watched" column that claims over 168 hours (the number of hours in a week) of TV time but I have discounted data that is under 126 hours. The reason for this is that I have subtracted 6 hours of sleep for each night, as this is around the minimum sleep a person can survive on. Even if there is 168 hours in a week, no one could watch 168 hours of non-stop television. When the data has been discounted, felt that it would be appropriate to draw a scatter graph for this data. A scatter graph is more suitable, because grouping the data would be much too difficult, and consequently drawing a box and whisker diagram would be to difficult with this variety of data. I will be looking for negative correlation between IQ and number of hours of TV watched, as my hypothesis was... ... 1. The lower the IQ of a person is, the more hours of TV that person will watch per week. This is the graph that I have drawn. ...read more.

Conclusion

The 14 year olds have normal distribution, and the 15 year olds have a slight negative skew. My second hypothesis (The older a person is, the higher that persons IQ is) was wrong. The data above shows that there is no relation between age and IQ. I will now investigate my third hypothesis:- Males have higher IQs than females. I will again discount incorrect data and delete data that is incorrect. I will ignore any gender other than male or female. I will discount any data in the IQ column that is <30 or >170. I will now modify my data sheet again. To see if the data is skewed or un-skewed, I will draw a frequency curve. I am doing this to help me decide which measures of central tendency and dispersion to use. (If the data is skewed, I will use median and interquartile range, if not I will use mean and standard deviation). This data is not skewed, so I will use standard deviation and mean as my methods of measure. Gender Mean Standard Deviation Lowest IQ Highest IQ Male 101.3289 9.345069578 68 134 Female 101.9878 8.939091418 69 132 The female mean is slightly higher than the males, which proves my hypothesis wrong once again. Also, the males have a larger standard deviation, which means that the spread of data is bigger in the males, meaning that they are less consistent than their female counterparts. However, if you look at the above frequency graph, and also look at the ...read more.

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