# L5 - Continuous Random Variables and Normal Distribution Flashcards

What are Discrete Variables?

- It takes specific, finite values within a range.
- P(X = a) is clearly identified and read on the vertical axis.
- Cumulative probabilities are calculated as sums.

What are Continuous Variables?

- It takes any value within a range.
- P(X = a) = 0.
- We identify the variable in

ranges and look at cumulative probabilities:

P(a < X < b)

P(X > a)

P(X < b)

What is the Probability distribution for continuous variables called?

- The probability distribution for a continuous variable X is called a probability density function (pdf), and it is denoted as p(X) or f(X).
- The pdf gives us the shape of the distribution, however remember that

for a continuous random variable the probability is NOT read on the vertical axis any longer. - It is a cumulative probability and it is the shaded area under the pdf (shaped like a bell curve). This is calculated as the integral of the function p(X)

evaluated between a and b.(Or between - and b, or a and + ). - The whole area under the pdf must integrate to 1

What is Normal Distribution?

- Perhaps the most famous, represents many physical phenomena.
- First proposed by Karl Frederick Gauss, as a model for the errors of

measurement that occur in calculating the paths of stellar bodies, hence

its alternative names, the Gaussian distribution and the ‘normal curve of

error’. - Characterised by two parameters, its mean µ and variance σ^2

so

X~N(µ,σ^2) - It is bell shaped and symmetric about µ: mean, mode and median are the

same. This shape is shown Mathematically:

p(X)= (1/sqrt(2πσ^2)) x exp[(-(X-µ)^2)/(“σ^2)]

Why can we use a table for Normal Distribution?

-0 The normal distribution is invariant to scale transformation: if

X~N(µ, σ^2 ) and W = c+dX then W~N(c+dµ, d^2 σ^2).

- Thus if we set c = -µ/ σ and d = 1/ σ we can define this new variable:

- Z= X-µ/σ ~ N(0,1)

This is called a standard normal and its pdf p(Z) is defined as:

p(Z)= 1/sqrt(2π) x exp (-Z^2/2)

- Its probabilities have been tabulated by statisticians.

What are some common formulae of Normal Distribution Statistical Table?

- P(Z > z ) = 1-P(Z < z)
- P(a < Z < b) = P(Z < b) - P(Z < a)

-Due to the symmetry of the normal distribution

P(Z < -z) = P(Z > z) = 1- P(Z < z)

How can you calculate probabilities for any Normally Distributed Variable?

- Calculating probabilities for any normally distributed X can be easily done by transforming X into Z. We know that: Z= X-µ/σ then P(X < a) is the same as; - P(Z < a-µ/σ) We find the value: z{a} = a-µ/σ and then use the Z-probabilities to find P(Z < z{a})

What is the critical Value?

the value of Z that defines a certain probability i.e. the area in a certain tail

- in the critical value table the values of Z associated with different probabilities in the format P(Z > z) = α.

What is the process for calculating the value of X from a given probability?

- Using the Critical Value find the value of z for that given probability
- to then find the value of X use the formula Z= a-µ/σ where X=a –> a=z{a}σ+µ