- Join over 1.2 million students every month
- Accelerate your learning by 29%
- Unlimited access from just £6.99 per month
Meet our team of inspirational teachers
I will start of by put the mice into groups. By doing this it will save me a lot of time and make my data my easier to compare. Also grouping the data enables me to draw the graphs which help me to compare the data. After I group the data I will also find the mean average for both sets of mice. Female Groups Frequency (f) Midpoint (X) f x X 0 ? w < 5 5 ? w < 10 10 ? w < 15 15 ?
- Word count: 539
Whereas for girls, I would agree with Gulliver. Age also has to be taken into consideration when dealing with the whole concept of the theory. A child and an adult have many differences; and to apply the theory to both groups would seem a bit irrational. An adult is fully matured in all physical aspects whereas a child still has to go through puberty. Another example is that a child hasn't fully developed a figure yet whereas an adult has; hence, affecting the waist measurements.
- Word count: 1816
This information is shown by: Abs: Absent DNR: Did Not Run Inj: Injured This data will help me in making my three hypotheses as well as help me produce sensible ones. For example, if a pupil is on a school team, he will be fitter than a pupil who is not, because the school team encourages training whereas a pupil not on a school team would be less fit. This example was sensible because it had logic behind it whereas something like - A pupil with a high musical instrument level would do well in the Pe house run - is not sensible and will lose marks.
- Word count: 4789
5n -number-7x9 How did I work out this and what can we do with this formula? The formula starts with 5 as there is a rise between the t-total of 5 each time. We then -63. I got this number by working out the difference between the t-number and the other numbers in the t-shape. E.g. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 Working out 32-13=19 32-14=18 32-15=17 32-23=9
- Word count: 1547
The sample must also be random so that my results take into account all the different drivers, instead of just the best or worst ones. I took random samples for 30 males but some of my data was incomplete. I could not use these incomplete pieces of data so I used ones with complete data instead. Male Mistakes 1 7 9 15 9 13 26 1 31 17 12 24 26 14 17 15 13 29 1 19 22 6 9 2 4 19 15 10 13 21 Female Mistakes 28 19 11 32 15 30 28 25 26 32
- Word count: 3657
But on the other hand some candidates may be a lot slower and not interpret information that they have learnt in their driving lessons as quicker. The natural learners will do a lot better then as they have understood and remembered what they have been taught. * The day and time a candidate takes their test- this may affect the number of mistakes mad as the candidate could take their test during rush hour or when there is hardly any traffic.
- Word count: 5299
The frequency shows how often certain data occurs. The inter-quartile range will help me in a similar way. Box helps explain data visually. Cumulative frequency will show in tables and in graphs. samples Level 2 Level 3 Level 4 Level 5 Level 6 IQ IQ IQ IQ IQ 1 68 78 90 100 107 2 69 85 91 103 108 3 69 87 94 103 108 4 71 88 97 104 110 5 72 88 98 105 111 6 72 88 99 105 114 7 74 89 99 106 116 8 74 90 100 106 116 9 74 90 100 107 117 10 76 90 100 108 117
- Word count: 1172
Differences Total 5 13 25 41 61 85 113 145 181 221 1st difference 8 12 16 20 24 28 32 36 40 2nd difference 4 4 4 4 4 4 4 4 From the quadratic sequence, we see that the main difference is 4. The first formula I will try to find is the formula for the surrounding white squares. Formula for white squares Pattern 'font-size:12.0pt; '>1 x 4 = 4 white squares 'font-size:12.0pt; '>2 x 4 = 8 white squares 'font-size:12.0pt; '>3 x 4 = 12 white squares N = pattern number D = dark squares W = white squares I believe that the formula is 4 x the pattern number or 4N.
- Word count: 1497
Stratified Sampling Blonde KS3: 124 / 179= 0.69 0.69 x 18= 12.42 ==> 12 Blonde KS4: 55 / 179= 0.31 0.31 x 18= 5.58 ==> 6 Random Sampling Blonde KS3: Ran# x 100= 31 97 64 94 81 82 17 37 74 56 60 90 Names: Volly Chris Hartnett Sarah-Jane Hardy Ingrid Aston Luke Holliwell Claire Shane Paul Grey Elizabeth Frog Lyndsey Brooder Andrew Hanley Gemma Brown Caroline Crisely Pheonia Blonde KS4: 07 55 01 18 23 20 Names: Edd, Michael Lock, Lee Tazmer, Leigh Smith, Michelle Blashaw, Holly Scrannage, Ben Stratified Sampling Non blonde/blue eyed KS3: 690 / 983=
- Word count: 7924
To test my hypothesis, I will use primary data. I will collect information from same houses in Mepton Bridge. I will compare Mepton Bridge to the whole country using secondary data.
Plan of Action To test my hypothesis, I will use primary data. I will collect information from same houses in Mepton Bridge. I will compare Mepton Bridge to the whole country using secondary data. I will count people who are less than seventeen years old as children. Because the town is too big to fid out the number of children in every house, I have decided to choose five streets at random from the 876 streets on the list on the A-Z map of Mapton Bridge.
- Word count: 520
160 166 131 131 124 120 154 118 139 133 166 119 159 148 128 167 154 133 130 165 164 120 123 152 167 149 133 122 167 127 137 123 130 160 136 147 148 125 118 118 152 * The mean () is calculated using the equation below: = * The standard deviation () is calculated using the equation below: = 17.08731661 Adding 'a' to a set of data will result in an increase in the mean ()
- Word count: 2086
Rollercoasters. I will use the rollercoaster database to answer the following question: Is it true that the fastest rides are the most exciting?
and the thrill factor out of 10 for a selected rollercoaster. This is quantitative data as it is numerical. The data will be useful because I will be able to use it to answer the question - I can compare the max speed of the rollercoaster with the thrill factor. I will collect a sample of 30, so that I can obtain a decent, yet manageable amount of data. I feel that this sample number will be efficient, as I will collect enough results to hopefully get a non-biased answer. I will need to take a sample from the population, which is a list of all the rollercoasters.
- Word count: 1882
* The 100m times will improve over time throughout the year groups. * The 100m times will follow a normal distribution throughout the year groups. Plan For my first hypothesis, I will use a scatter graph to see if there is any relationship between the 100m times and the long jump distances. This will enable me to see if there is a correlation between the two. The correlation can be found by finding the double mean point, drawing a horizontal and a vertical line through it and examining the spread of data over the four quadrants.
- Word count: 2849
in my sample I will choose 30 boys and 20 girls and merge this two data together to form my sample of year 7. From these two separate stratified samples, I will draw 2 comparative box plots. The reason I chose year 7?s and 11?s is because they have the biggest age gap so would show the most difference if age affects estimation. The data that I shall I collect will be primary as I will personally supervise the collection of data and will not allow bias to enter my data. I will set rules for accepting a person?s estimate.
- Word count: 3398
For ease of calculation, I will be taking it as 0.071 grams or 0.0015 pounds 4. Ed Yardeni claims that we should wait before we melt the pennies because the price will keep rising and the metals in the penny will be worth more than its face value. To prove this, I will calculate the value of metals before and after the zinc price raised up by $551 per ton. Before price hike: Copper: $5,690 per ton or $2.703410526 per pound Zinc: $2,930 per ton or $1.329025645 per pound Value of Copper in a pound of pennies: 0.025 x 2.703410526 = $0.067585263 Value of Zinc in a pound of pennies: 0.975 x 1.329025645 = $1.295800004 Total
- Word count: 2711
Data handling. There are many different ways of collecting data. A common method of collecting data is to use a questionnaire.
The person interviewing is likely to be more consistent at recording the responses. Disadvantages This method of interviewing takes a long time and might be expensive than other data collection methods. The interviewed person is more likely to refuse answering the question or they might lie if they give an answer. Questionnaires can also include: yes or no answers ticking boxes numbered responses word responses questions which require a sentence to be written Whichever style of question is used, it is important that they are easy to understand, cover every possible answer, are unbiased and unambiguous. It is also important that you ensure that they are appropriate to your survey.
- Word count: 586