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GCSE: IQ Correlation

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  1. Discuss the role of genetics & cultural differences in the development of intelligence.

    There was no difference adopted children and a control group who remained with biological parents. By adolescence both groups had come IQ correlation with biological parents. This study suggests that genetics have a strong influence on IQ. Bouchard & McGue (1981) carried out a study into the relationship between IQ and genetics by using a twin study. They reviewed 111 studies of twins and concluded that the mean correlation of intelligence in identical twins is 0.86, whereas for fraternal twins it is 0.6. A criticism of this study is that people who share genetic similarities tend to share environmental similarities, which makes it unclear whether it is genetics or the environmental that affects intelligence.

    • Word count: 657
  2. In this investigation I am going to look into the number of goals scored against the league position in the 2002/03 season.

    That should help me get accurate data. When I used the random number devise. Which was 'ran#' button on my calculator. They results where as follows- Number Team Goals 16 Aston Villa 42 4 Chelsea 68 1 Man Utd 74 18 West Ham 42 9 Man City 47 To find out if there is a link between league position and goals I will use a line graph. My Prediction is that they will be a strong coloration between the two.

    • Word count: 633
  3. Perform a statistical enquiry that will either prove or disapprove my hypothesis.

    It looks something like this. The sample you chose for the investigation should be representative of the population. It should take account of variation in the characteristics of the population. These variations should be represented in the sample in the same ratios as in the total population. That means that in the total population there are twice as many boys as girls, my sample should include twice as many boys than girls. Taking the samples in this way is called Stratified sampling.

    • Word count: 7151
  4. Comparison of SATs results to obtain statistical data on students.

    My raw data is on the following page; each year has been marked down, along with the number of students from each year group. The freak data can be seen along with all the other data. Hypotheses. 1. I believe that the male's modal group will be a higher level than the females. 2. I believe that the females will have a larger range of scores than males. 3. I believe that there may be stronger correlation between science and maths scores than there is with either subject with English.

    • Word count: 7646
  5. 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.

    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.

    • Word count: 2291
  6. Investigation to find any relationships between IQ and key stage 2 results.

    Shawn Cook - 116 - 6-6-6 - 6 8. Dennis Blake - 90 - 3-3-3 - 3 9. Donald Dunne - 104 - 4-5-4 - 4 10. Daniel Fisher - 90 - 3-3-3 - 3 11. Billy Glintode - 87 - 3-3-3 - 3 12. Stuart Gilroy - 110 - 5-5-5 - 5 13. Matt Hawk - 74 - 2-2-2 - 2 14. Malcolm Heath - 100 - 4-4-4 - 4 15. Gareth Hunt - 102 - 5-3- 4 - 4 16. Richard Johnson - 91 - 4-3-3 - 3 17. Kaz Kaura - 84 - 3-3-3 - 3 18. Joseph King - 110 - 4-5-4 - 4 19. Adam Little - 100 - 4-4-4 - 4 20.

    • Word count: 5601
  7. Are left-handed people more intelligent and creative than the right-handed in Mayfield High School?

    That means for my collected data, I should have 60x 21% from boys and 60x 17% from girls. I will take 32% from Year 8, 17% are from the boys and 15% from the girls. In my collected data, I should have 60x 17% boys and 60x 15% from girls. In Year 9, I will take 15% from boys and 15% from girls, so In the following table, I am going to calculate what the actual number of data that I am going to get Number of Boys should be chosen in my data Number of girls should be chosen

    • Word count: 10981
  8. Data Handling.

    My hypothesis is that males will watch TV more, and the higher the years will watch more TV. The two categories I have chosen (gender and year group) will be sufficient to show a correlation however, since it will show any trends between gender and/or year group towards their TV habits. The gender identifies trends between boys and girls on how much they like to watch TV as a gender, and the year group shows how peoples TV habits change as they get older. There should be no bias in this since there is 5% of the population and there is an equal amount of each gender, and only a small difference in amounts of people in the year group.

    • Word count: 1809
  9. My hypotheses are: -1. People's average SAT and average GCSE results will have a strong positive correlation between them.2. Girl's average GCSE result will generally be higher than boy's average GCSE result.

    I am going to use two cumulative frequency graphs. The first graph will be a cumulative frequency graph showing only boys average GCSE results. The second graph will be a cumulative frequency graph showing only girls average GCSE results. On these graphs I will plot a line of best-fit, upper and lower quartiles, inter-quartile ranges, box plots and a median point. I am also going to draw one histogram, on this histogram I am going to plot both boys and girls average GCSE result as a percentage. I am also going to draw three labeled pie charts.

    • Word count: 4581
  10. Predicted grades given by subject teachers at the time of Yellis.

    one's that will give a misrepresentative grade to stop student getting complacent. I would like to investigate the correlation between the achieved GCSE results and both the predictions by yellis and staff, I will be looking at Mathematics and English language because yellis test specify these two skills so it is more likely to have less deviation, then to a subject such as art which is far from the original type of skill tested, I will also look at the results for Science double award because this is a subject that uses both skills, so it should be a truer representation to the results achieved.

    • Word count: 2803
  11. The aim of my investigation is to investigate the factors IQ and KeyStage 2 Results for girls and boys of a fictitious secondary school. I am then going to see how the results differ between the sexes.

    Excel. See appendix 1.1 for my samples. If I am to come across any obvious anomalous results I shall exclude such datum from any further investigation to make my analysis as accurate and fair as possible. To see if my statements are correct I will first have to plot IQ and KeyStage 3 Results in separate tally and frequency charts for both girls and boys and use it to find the mean, mode, median and range to analyse. Because KeyStage 2 Results are in 3 different parts I will take an average of the 3, rounded up to the nearest whole number.

    • Word count: 2093
  12. Using the data from Mayfield School I am going to construct an investigation to see whether the number of hours of television a student watches per week directly influences their IQ and exam results.

    I predict that the students who watch a lot of television will have lower IQ's than those who find alternate means of entertainment. I predict this because I believe that watching television does not provide much stimulus for the brain in comparison to active entertainment and taking part in conversations. I also envisage that the students who watch limited hours of television will do better in their exams. I think this because unless the students are watching educational programs related to their work then they are using time in which they could be studying.

    • Word count: 2081
  13. Hypothesis - On average, year nine students that watch more television per week have a higher IQ.

    35 hours 34 43 107 16 hours 35 27 94 12 hours 36 51 109 12 hours 37 53 105 32 hours 38 2 100 18 hours 39 35 100 2 hours 40 25 113 18 hours 41 55 106 13 hours 42 106 113 16 hours 43 107 108 25 hours 44 105 110 14 hours 45 19 106 20 hours 46 15 103 4 hours 47 56 94 13 hours 48 57 98 9 hours 49 69 100 20 hours 50 68 109 13 hours Boy Number IQ Hours per week watching television 1 186 109 7 hours

    • Word count: 1109
  14. For my investigation, I am going to see if there is a similarity between the number of hours watched of television per week and their personal IQ for year 10's and 11's in key stage 4.

    The closer the integer in front of the 'x' is to zero, the less there is any correlation. I will then split up the data into males and females and repeat the process drawing scatter graphs for each and comparing the slopes hence the relationship. At this stage, I would like to look at the distribution of males and females in these years. As it is easier to compare the data visually, I will use the function facility on the spreadsheet to calculate the maximum, the minimum, the lower and upper quartiles and the median. I will use these values to draw box plots.

    • Word count: 1016
  15. The aim of my investigation is to use and apply my understanding of statistics and statistical techniques to investigate the two following hypothesis - There is a correlation between Key Stage 3 (KS3) and GCSE results.

    I will construct a frequency table (table 2) to condense the information and make it easier to read, understand and utilise. FREQUENCY TABLE (table 2) KS3 Level 3 Level 4 Level 5 Level 6 Level 7 TOTAL Boys 3 3 7 9 2 24 Girls 4 2 10 18 2 36 TOTAL 7 5 17 27 4 60 GCSE U G F E D C B A TOTAL Boys 4 1 1 4 10 0 2 2 24 Girls 1 1 2 7 6 12 5 2 36 TOTAL 5 2 3 11 16 12 7 4 60 To make

    • Word count: 1773
  16. To compare the change in number of goals scored home and away by Premiership teams in two seasons. I will use the 2000-2001 and 2001-2002 seasons.

    Comparing Means The mean of goals scored at home is 587 ? 20 = 29.35 The mean of goals scored away is 405 ? 20 = 20.25 This shows that the average team scores 9 (nearest whole number) more goals at home than it does away. Using this mean the standard deviation can be found. Standard deviation As well as having an idea of the 'average' of the data, it is useful to know how the data are distributed around the average. The standard deviation is known as a measure of dispersion as they say something about how the data are dispersed around the average (mean).

    • Word count: 3726
  17. The relationship between IQ and Key Stage 2 results

    I will do this by using my calculator. I have given each page a number, and then the names on that page also have numbers. I will then press 'shift' and then 'RAN#' on my calculator, and it will display a number between 0 and 1. I will then multiply this number by 34 each time, and using the unit, I will find the page, and using the decimal, I will find the name. E.g. 0.283 x 34 = 9.622 9 is the page number and 6 is the number of names I must scroll down.

    • Word count: 2184
  18. In this project I am going to prove that the results of boys are above the results of girls, as they have relatively small brains!

    I will then conclude each section and state whether boys/girls are above or below average! Section One For this section of the project I have decided to choose three core subjects: * English * Maths * Dbl. Science As well as two others - one that more intelligent people take * French And one that the not so able pupils take: * Home Economics The first graph I am going to draw shows the percentage pass rate for boys in these five subjects compared to the percentage pass rate for girls. Below is a table of the averages over the 3 years 1999 -2001: English Maths Dbl.

    • Word count: 1949
  19. To analyse the distribution of the number of hours spent watching TV per week and IQ's for students in year's 7 and year's 9 and to see if there is a relationship between them.

    This meant my samples had to range from the beginning of the alphabet to the end of it. I used my calculator to get my samples. My different graphs were as follows: * Cumulative Frequency Curves * Box Plots * Scatter graphs * Histograms * Mean, Mode, Standard Deviation * Inter Quartile Ranges. Comments Cumulative Frequency Curves My cumulative frequency curve for number of hours spent watching TV by girls in Years 7 and 9 show that in year 9, the number of hours increases much more rapidly than it does in year 7. The year 7's data produces a much more curved line than the year 9's does which shows that the year 7's increases at a much more steady rate.

    • Word count: 1809
  20. Critically consider the role of genetic factors in the development of measured intelligence.

    Therefore, to overcome this problem of determining whether is was the environment or genetic factors that influenced the person's IQ score, research has been conducted in comparing the IQ score of MZ twins that are reared together in the same environment and those raised separately in different environments. Mz twins reared together showed a greater correlation of IQ than those reared apart, which shows that the environment plays an important role in influencing intelligence. However the fact that Mz's reared separately have a higher correlation of IQ scores than DZ (non identical) twins reared together proves a strong genetic influence.

    • Word count: 1371
  21. Data handling - Hypotheses- Is there any correlation between the children's favourite sport and favourite subject in yr 8.?

    Do girls have a higher IQ than boys? The graph I am going to use to show this piece of data is the frequency polygon. In order to do this I will need to construct 2 tally charts to represent boys and girls. To Boys Girls Here is my graph Result: from the graph I can conclude that the boys and girls have the same IQ so the group is average.

    • Word count: 492
  22. I am going to investigate the relationship between the IQ and the Key Stage 2 results within this school. This will help me make and prove different hypothesis using the data I have obtained.

    Year Group Boys Girls Total 7 13 11 24 8 12 11 23 9 10 12 22 10 9 8 17 11 7 7 14 Total 51 49 100 I will now randomly select the correct number of boys/girls from each year group. I will use the random number button on my calculator. Total of KS2 Results (Mixed) Total of KS2 Results Frequency 7 0 8 1 9 5 10 10 11 10 12 26 13 12 14 13 15 19 16 1 17 1 18 2 Total 100 Total of KS2 Results (Boys)

    • Word count: 3434
  23. Bivariate Data

    The Population (presented in table 1) shows last year's groups, KS3 and GCSE point score averages. There are a total of 90 pieces of data. This is a fairly small population but it was the only set easily available. From the 90 I will randomly sample 50 pieces to investigate. The way I randomly sampled the population, was to number each set one to ninety (1-90), while also numbering ninety pieces of paper. I placed those pieces of data into a hat, mixed them around and then picked out fifty from it. To make sure I didn't accidentally "see" a numbered piece of paper, I folded the paper twice and blinded folded myself so I would be oblivious as to what I chose.

    • Word count: 1520
  24. My hypothesis is that people with siblings have a lower IQ than people without siblings. Also that people with lower IQ's have lower KS2 results.

    The number of pupils in certain sibling groups from KS4 was in some cases very low (11 in one case). I had to use KS3 pupils to the number to 90 so each graph was a fair comparison and the results would be more accurate. The last group of 6+ siblings only had 61 children in it. These pupils were important because my hypothesis needs input from this high group. The pupil count of this group was increased by 1/2 so there were 90 pupils in that group so they could be compared.

    • Word count: 1057

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