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  • Level: GCSE
  • Subject: Maths
  • Word count: 3750

Maths Coursework. Statistics

Extracts from this document...

Introduction

Introduction Aim: To express evidence to support my hypothesis. Proving my hypothesis I will use: > scatter diagram > bar charts > tables > frequency table What I will do: > stratified sample > random sample > standard deviation > Find the mean, mode, median to compare my first hypothesis. To do these I will use a scientific calculator Introduction: Using the information given before me I have decided with three hypotheses that I shall investigate. These are: 1. Females longer than males. 2. The richer the country the higher the average birth rate. 3. The higher the continents GDP-per capita (in my data) the greater it's highest average age. For me to prove these hypotheses I will need to collect a sample of male ad females ages in addition to that I will also need to get a sample of money to do my second hypothesis, which is to compare both together. For this to be more accurate and for me to get enough information to compare I will need a big sample of countries, so I have chosen a sample of 50. to get my data for my hypotheses I used a scientific calculator to do a stratified sample and a random sample did a stratifies sample because it would tell me how many countries I should use from each continent (it would give me the same percentage for each continent as they all have different amount of counties.) the formula that I used on the calculator to get this information was: Country = how many continents in the country � how much I need . The number of continents in the world Asia = 54/235 �50 = 11.4893617 (12 rounded up) Africa = 57/235 �50 = 12.12765957 (12) Europe = 48/235 �50 =10.21276596 (10) Oceania = 25/235 �50 = 5.319148936 (5) North America = 37/235 �50 = 7.872340426 (8) South America = 14/235 �50 = 2.978723404 (3) ...read more.

Middle

Lowest average age: 50.52 (51) Life expectancy (years) Frequency (f) Mid-point (x) (fx) Fx2 Frequency density 35 � 45 0 40 0 0 0 45 � 55 1 50 50 2500 0.1 55 � 65 2 60 120 7200 0.2 65 � 75 5 70 350 24,500 0.5 Total: 8 520 34,200 Mean: ?fx =520/8=65 ? f Mode: 65 � 75 Median: 65 � 75 Standard deviation: 7.07 (2 d.p.) North America female Highest average age: 82.45(83) Lowest average age: 53.12(53) Life expectancy (years) Frequency (f) Mid-point (x) (fx) Fx2 Frequency density 35 � 45 0 40 0 0 0 45 � 55 1 50 50 2500 0.1 55 � 65 0 60 60 0 0 65 � 75 7 70 490 34,300 0.7 Total: 8 600 36,800 Mean: ?fx =600/8=75 ? f Mode: 65 � 75 Median: 65 � 75 Standard deviation: 3.20 (2.d.p.) Europe male Highest average age: 78.12(78) Lowest average age: 60.88(61) Life expectancy (years) Frequency (f) Mid-point (x) (fx) Fx2 Frequency density 35 � 45 0 40 0 0 0 45 � 55 0 50 0 0 0 55 � 65 1 60 60 3600 0.1 65 � 75 9 70 630 44,100 0.9 Total: 10 690 47,700 Mean: ?fx = 690/10= 69 ? f Mode: 65 � 75 Median: 65 � 75 Standard deviation: 3 Europe female Highest average age: 85.34(85) Lowest average age: 69.39(69) Life expectancy (years) Frequency (f) Mid-point (x) (fx) Fx2 Frequency density 35 � 45 0 40 0 0 0 45 � 55 0 50 0 0 0 55 � 65 0 60 0 0 0 65 � 75 10 70 700 49,000 1 Total: 10 700 49,000 Mean: ?fx = 700/10 = 70 ? f Mode: 65 < x ? 75 Median: 65 � 75 Standard deviation: 0 I have noticed that when I did the mode and median they are mostly the same for both male and female. ...read more.

Conclusion

I have also shown as my second hypothesis that 'the higher the G.D.P the longer the life expectancy.' I have shown this with three different sets of data, one for males, one for females and the other one for the G.D.P. I have done different graphs to compare the data and get an actual result to prove that my hypothesis is correct. I have also put a table to show my actual data that I had used to develop the graphs. I have highlighted the countries on the tables to show, which countries data did not confirm my hypothesis. I have done this for each continent to show the differences. I have also done each continent so I could get a more varied result to actually see if my hypothesis is true on all the continents. By doing this I have found out that not all the countries in different continents have a high G.D.P and a longer life expectancy. Finally for my third hypothesis I have shown that the higher the continents GDP-per capita (in my data) the greater it's highest average age. I used a table to compare the data to get the result that my hypothesis was correct. Overall I thought that the amount of data I used was enough to clearly interpret my data and come out with a final conclusion, which was that in most countries females live longer than males and also that the G.D.P had no effect on the age because in some countries the G.D.P was low and the age was high or in some it was that the G.D.P was high and the age was low. For example in Swaziland the G.D.P was high and the average life expectancy was low and also in Somalia the G.D.P was low and the average life expectancy was high. From my third hypothesis I found out that the higher the total amount of money in the whole continent the higher the average life expectancy. ...read more.

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