1 since everything is a reflection of our minds, everything can be changed by our minds
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Since everything is a reflection of our minds, everything can be changed by our minds.
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Random Variables
Section 4.6-4.14Types of random variablesBinomial and Normal distributionsSampling distributions and Central limit theoremRandom samplingNormal probability plot
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What Is a Random Variable?
A random variable (r.v.) assigns a number to each outcome of a random circumstance.
Eg. Flip two coins: the # of heads
When an individual is randomly selected and observed from a population, the observed value (of a variable) is a random variable.
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Types of Random Variables
A continuous random variable can take any value in one or more intervals. We cannot list down (so uncountable) all possible values of a continuous random variable.
All possible values of a discrete random variable can be listed down (so countable).
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Distribution of a Discrete R.V.
X = a discrete r.v. x = a number X can take The probability distribution of X is:
P(x) = P(Y=x)
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How to Find P(x)
P(x) = P(X=x) = the sum of the probabilities for all outcomes for which X=x
Example: toss a coin 3 times
and x= # of heads
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Expected Value (Mean)
The expected value of X is the mean (average) value from an infinite # of observations of X.
X = a discrete r.v. ; { x1, x2, …} = all possible X valuespi is the probability X = xi where i = 1, 2, …The expected value of X is:
i
ii pxXE )(
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Variance & Standard Deviation
Variance of X:
Standard deviation (sd) of X:
i
ii pxXV 22 )()(
i
ii px 2)(
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Binomial Random Variables
Binomial experiments (analog: flip a coin n times):
Repeat the identical trial of two possible outcomes (success or failure) n times
independently The # of successes out of the n trials
(analog: # of heads) is called a binomial random variable
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Example
Is it a binomial experiment? Flip a coin 2 times The # of defective memory
chips of 50 chips The # of children
with colds in a family of 3 children
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Binomial Distribution
= the probability of success in a trial n = the # of trials repeated independently Y = the # of successes in the n trialsFor y = 0, 1, 2, …,n, P(y) = P(Y=y)=
Where
yny
yny
n
)1()!(!
!
1)...2)(1(! nnnn
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Example: Pass or Fail
Suppose that for some reason, you are not
prepared at all for the today’s quiz. (The quiz is
made of 5 multiple-choice questions; each
has 4 choices and counts 20 points.)
You are therefore forced to answer these
questions by guessing. What is the probability
that you will pass the quiz (at least 60)?
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Mean & Variance of a Binomial R.V.
Notations as before
Mean is
Variance is
n
)1(2 n
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Distribution of a Continuous R.V.
The probability distribution for a continuous r.v. Y is a curve such that
P(a < Y <b) = the area under the curve over the interval (a,b).
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Normal Distribution
The most common distribution of a continuous r.v.. The normal curve is like:
The r.v. following a normal distribution is called a normal r.v.
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Finding Probability
Y: a normal r.v. with mean and standard deviation
1. Finding z scores
2. Shade the required area under the standard normal curve
3. Use Z-Table (p. 1170) to find the answer
y
z
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Example
Suppose that the final scores of ST6304 students follow a normal distribution with = 80 and = 5. What is the probability that a ST6304 student has final score 90 or above (grade A)?
Between 75 and 90 (grade B)? Below 75 (Fail)?
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Sampling Distribution
A parameter is a numerical summary of a population, which is a constant.
A statistic is a numerical summary of a sample. Its value may differ for different samples.
The sampling distribution of a statistic is the distribution of possible values of the statistic for repeated random samples of the same size taken from a population.
Sampling Distribution of Sample Mean
Example: suppose the pdf of a r.v. X is as follows:
Its mean and variance
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x 0 1 3
f(x) 0.5 0.3 0.2
Sampling Distribution of Sample Mean
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All possible samples of n=2:
Sampling Distribution of Sample Mean
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Sampling Distribution of Sample Mean
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Central Limit Theorem
nyy / and
When n is large, the distribution of y is approximately normal.
24Central Limit Theorem Central Limit Theorem (uniform[0,1])(uniform[0,1])
Normal Approximation to Binomial Distribution
The binomial distribution is approximately normal when the sample size is large enough:
Continuity correction
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5;5 nn
Others
Random sampling and Normality checking are in Lab 2
Poisson Distribtion
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