Presentation on theme: "Normal distribution Learn about the properties of a normal distribution Solve problems using tables of the normal distribution Meet some other examples."— Presentation transcript:
1 Normal distributionLearn about the properties of a normal distributionSolve problems using tables of the normal distributionMeet some other examples of continuous probability distributions
2 Types of variables Discrete & Continuous Describe what types of data could be described as continuous random variable X=xArm lengthsEye heights
4 The Normal Distribution (Bell Curve) Average contents 50Mean = μ = 50Standard deviation = σ = 5
5 The normal distribution is a theoretical probability the area under the curve adds up to one
6 The normal distribution is a theoretical probability the area under the curve adds up to one A Normal distribution is a theoretical model of the whole population. It is perfectly symmetrical about the central value; the mean μ represented by zero.
7 As well as the mean the standard deviation (σ) must also be known. The X axis is divided up into deviations from the mean. Below the shaded area is one deviation from the mean.As well as the mean the standard deviation (σ) must also be known.
10 A handy estimate – known as the Imperial Rule for a set of normal data: 68% of data will fall within 1σ of the μP(-1<z1)=0.68368.3%
11 95% of data fits within 2σ of the μ P(-2<z2)=0.95495.4%
12 99.7% of data fits within 3σ of the μ P(-3<z3)=0.99799.7%
13 Simple problems solved using the imperial rule - firstly, make a table out of the rule <-3-3 to -2-2 to -1-1 to 00 to 11 to 22 to 3>30%2%14%34%The heights of students at a college were found to follow a bell-shaped distribution with μ of 165cm and σ of 8 cm.What proportion of students are smaller than 157 cm16%
14 Simple problems solved using the imperial rule - firstly, make a table out of the rule <-3-3 to -2-2 to -1-1 to 00 to 11 to 22 to 3>30%2%14%34%The heights of students at a college were found to follow a bell-shaped distribution with μ of 165cm and σ of 8 cm.Above roughly what height are the tallest 2% of the students?x 8 = 181 cm
15 Task – class 10 minutes finish for homework Exercise A Page 76
16 Sometimes you will see it using phi =. The Bell shape curve happens so when recording continuous random variables that an equation is used to model the shape exactly.Put it into your calculator and use the graph function.Sometimes you will see it using phi =.
17 There are normal distribution tables Luckily you don’t have to use the equation each time and you don’t have to integrate it every time you need to work out the area under the curve – the normal distribution probabilityThere are normal distribution tables
18 How to read the Normal distribution table Φ(z) means the area under the curve on the left of z
19 How to read the Normal distribution table Φ(0.24) means the area under the curve on the left of 0.24 and is this value here:
24 Solving Problems using the tables NORMAL DISTRIBUTIONThe area under the curve is the probability of getting less than the z score. The total area is 1.The tables give the probability for z-scores in the distribution X~N(0,1), that is mean =0, s.d. = 1.ALWAYS SKETCH A DIAGRAMRead the question carefully and shade the area you want to find. If the shaded area is more than half then you can read the probability directly from the table, if it is less than half, then you need to subtract it from 1.NB If your z-score is negative then you would look up the positive from the table. The rule for the shaded area is the same as above: more than half – read from the table, less than half subtract the reading from 1.
25 You will have to standardise if the mean is not zero and the standard deviation is not one
27 Normal distribution problems in reverse Percentage points table on page 155Work through examples on page 84 and do questions Exercise D on page 85
28 Key chapter pointsThe probability distribution of a continuous random variable is represented by a curve. The area under the curve in a given interval gives the probability of the value lying in that interval.If a variable X follows a normal probability distribution, with mean μ and standard deviation σ, we write X ̴ N (μ, σ2)The variable Z= is called the standard normal variable corresponding to X
29 Key chapter points cont. If Z is a continuous random variable such that Z ̴ N (0, 1) then Φ(z)=P(Z<z)The percentage points table shows, for probability p, the value of z such that P(Z<z)=p