• Join over 1.2 million students every month
• Accelerate your learning by 29%
• Unlimited access from just £6.99 per month   • Level: GCSE
• Subject: Maths
• Word count: 3154

# Mayfield Maths Coursework

Extracts from this document...

Introduction

Maths

Statistics Coursework Yr10

Correlation between: The more hours spent in the gym the more amount of weight you will loose. Explanation on correlation

This is a scatter graph on the following correlation and is a positive correlation between the more hours you will spend in the gym the more weight you will loose. This shows that if people stay healthy then they would weight less.  I will now work out the mean, mode and median of certain numbers to prove the correlation and prove that the correlation is correct. I will also be having a stem and leaf diagram and a inter quartile range which will also prove my correlation.

Firstly to prove that my correlation is positive and is correct I will create a stem and leaf diagram which is a diagram that shows data in a systematic way and the mode median and mean can be found easily from this. Once the stem and leaf diagram is created I can then find the average, mean, mode and median of the correlation.

Stem and leaf Diagram

 Gym hours Weight 2468 0 024 1 2 3 4 5 04567 6 02345 7 02478 8 4689 9 048

This is the stem and leaf diagram for the correlation between gym hours and weight, finding the mean, mode and median should be much easier now.

Average of Gym hours (D2:D17) =-3.375

Average of weight (E2:E17) =63.25

That is one way of finding an average to support the correlation between gym hours and weight. Here is another way:

Mode number of gym hours: 14hrs

Mode number of weight: 57

This average is saying spending 14hrs at the gym will make you loose 57 pounds of weight which is supporting my correlation.

Middle

This is because the stem and leaf diagram has made it easier to fine the mean mode and median. I have also found the interquartal range I have now proven that the correlation between weight and sick days is correct.

Correlation between: The more amounts of sick days the more amount of salary lost Explanation on correlation

This is a following scatter graph on the following correlation and is a negative correlation between the more amount of sick days you will have the more amount of salary lost. The correlation is showing that if people have so many sick days then they will start to lose their salary. I am now going to show how I can prove that the following negative correlation is right or wrong by taking the mean median and mode and the average of the correlation. . I will be also having a stem and leaf diagram and an interquaritle range which will also prove my correlation.

Firstly to prove that my correlation is negative and correct I will create a stem and leaf diagram which is a diagram that shows data in a systematic way and the mode median and mean can be found easily from this. Once the stem and leaf diagram is created I can then find the average, mean, mode and median of the correlation.

Stem and leaf Diagram

 Sick days Salary 2345678 0 02 1 785928 2 051225344589965 3 4 3 5 6 7 8 9

This is the stem and leaf diagram for the correlation between sick days’s and weight, to find the mode, mean and median now should be easier

Average sick days: (F2:F51) = 3.16

Average of salary: (C2:C51) =28683.67

Conclusion

Stem and leaf Diagram

 Age Salary 0 89 1 785928 12346789 2 051225344589965 1335 3 4 3 5 6 7 8 9

This is the stem and leaf diagram for the correlation between sick day and weight, to find the mode, mean and median now should be easier

Average of age :( B2:B51) =36.98

Average of salary: (C2:C51) =28683.67

That is one way of finding an average to support the correlation between age and salary. Here is another way:

Mode number of age: 18

Mode number of salary:21000

This average is saying people that aged 18 only earn 21,000 which shows that my correlation is correct.

Mean of age: 17, 18, 19, 20=19

Mean of salary: 17, 000, 18, 000, 18, 500, 19,000=181500

Here it is also saying that people who are aged 19 earn 181500 this shows that my correlation is wrong by using the mean I cannot prove my correlation.

Median of age: (B2:B17) = 21

Median of salary: (C2:C17) =21000

Interquaritle range:

Lower quartile range on age:

¼(N+1) =

¼(11+1) =third term=19

Upper quartile range on age:

¾(N+1) =

¾(11+1) =nine term=32

Lower quartile range on salary:

¼(N+1) =

¼(11+1) =third term=2100

Upper quartile range on salary:

¾(N+1) =

¾(11+1) =nine term=2750

We know the average of the correlation and we can now prove that correlation between age and salary that people who are older do get more salary it is now proven that my correlation is correct.

This is because the stem and leaf diagram has made it easier to fine the mean mode and median. I have also found the interquartal range I have now proven that the correlation between gym hours and sick days is correct.

This student written piece of work is one of many that can be found in our GCSE IQ Correlation section.

## Found what you're looking for?

• Start learning 29% faster today
• 150,000+ documents available
• Just £6.99 a month

Not the one? Search for your essay title...
• Join over 1.2 million students every month
• Accelerate your learning by 29%
• Unlimited access from just £6.99 per month

# Related GCSE IQ Correlation essays

1. ## Mathematics Statistics Coursework

I am going to use stratified sampling as statistical calculations. I am going to take a sample from the population of Year 7 Boys. I will take the number of people, divided by Total number of people at the school, times by the sample size.

2. ## GCSE Statistics Coursework

The lines they have to estimate look like this: Actual lines which were estimated are at the back of the project. I got this trial data by doing this experiment and got these results: Trial data collection St line Non St 1st 2nd 3rd Final non St 260 300 300

1. ## Guestimate - Data handling coursework

I will show how we the results for mean. Median, LQ and UQ. Analysis Conclusion By gathering all my results together, I can agree on my hypothesise- People estimate the length of lines better than the size of angles & Males are better than female in estimating the angles and lines.

2. ## Mathematics coursework

As seen above there is positive correlation between the Maths results and the CAT results. I will know learn the strength of the two positive by using the Spearman's Rho Theorem. Spearman's Rho Theorem I will be explaining the method in steps to ensure clarity.

1. ## Maths Coursework: investigation into the correlation between IQ and KS2 results

the lower quartile), so the IQ is base around that general area, as we saw from the normal distribution in the histogram. After having analysed the cumulative frequency for the IQ, I can say that the IQ in the inter-quartile range, fits in with the scatter graphs, because if you

2. ## The 3 statements I am going to investigate are: -Does the gender of the ...

I will now make a statement for my third hypothesis, "How does the IQ of the students affect their results?" For this I am predicting that in general, the higher the IQ of the student, the higher their KS2 results as an average.

1. ## HYPOTHESIS Blonde girls are more intelligent than non blonde girls. Blonde girls that ...

fr IQ IQ GRAPHS 1+2. Both the Blonde and Non Blonde IQs do not have normal distribution. I know this because the mean (the thick blue lines along the x axis) is not in the central point of the frequency polygon.

2. ## This experiment will show that there is a significant positive correlation between males and ...

states that an intervening variable in a study such as this is personal expectations. Other aspects, which may cause an experimental error, may be the environments in which the participants fill in the questionnaire. Participants The target population is year 12 students at Rastrick High school who completed their GCSE examinations in the summer of 2003. • Over 160,000 pieces
of student written work
• Annotated by
experienced teachers
• Ideas and feedback to 