The Binomial Distribution
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Transcript of The Binomial Distribution
The Binomial Distribution
For the very common case of “Either-Or” experiments with only
two possible outcomes
Recognize Binomial Situations
• Only two possible outcomes in each trial.– Probability for one of the outcomes.– Probability for the other outcome.
• Some definite number of trials, .– They’re independent trials. don’t change.
• We’re interested in , probability of a certain count of how many times event happens in those trials.
A special kind of probability distribution
• It’s the familiar probability distribution• But only two rows for the two outcomes.• Note that – Because probabilities must always sum to 1.00000– And this leads to .
Outcomes Probabilities
One of the events
The other event
Total Exactly 1
The Binomial Probability Formula
• Question: If we have trials, what is the probability of occurrences of the “success” event (the one with probability )
• Answer:
Practice with the Formula
• Experiment: Roll two dice• Event of interest: “I rolled a 7 or an 11”• Probability of success: (from • Probability of failure: • Number of trials
Practice with the Formula
• Find P(2) successes in the seven/eleven game
• How about 3 successes?
Compute them all
Summary of the 7-11 experiment
X successes P(X successes)
0 times (no sevens or elevens)
1 time
2 times
3 times
4 times
5 times (all sevens and elevens)
Total (must equal 1.000000 !!)
Sometimes you add probabilities
• Probability of at least three wins in five trials– P(X≥3) = P(X=3) + P(X=4) + P(X=5) add them up!
• Probability of more than three wins– P(X>3) = P(X=4) + P(X=5)
• Probability of at most three wins – P(X≤3) = P(X=0) + P(X=1) + P(X=2) + P(X=3)
• Probability of fewer than three wins– P(X<3) = P(X=0) + P(X=1) + P(X=2)
Use the Complement to save time
• Example: • “Probability of at least 3 wins”• P(X≥3) =
P(X=3) + P(X=4) + … + P(X=49) + P(X=50)• This means 48 calculations and sum results.• EASIER: The complement is “fewer than 3”• Take 1 – [ P(X=0) + P(X=1) + P(X=2) ]
TI-84 Computations
• binompdf(n, p, X) = probability of X successes in n trials
• Recompute the table and make sure we get the same results as the by-hand calculations.
• The “pdf” in “binompdf” stands for “probability distribution function”
TI-84 Computations
• binomcdf(n, p, x) = P(X=0) + P(X=1) + … P(X=x) successes in n trials
• binomcdf(n, p, x) does lots of little binompdf() for you for x = 0, x = 1, etc. up to the x you told it, and it adds up the results
• The “cdf” in “binomcdf” stands for “cumulative distribution function”
Try and verify binomcdf(n,p,x)
X successes P(X successes)Using binompdf
P(0 thru X) successesUsing binomcdf
0 times (no sevens or elevens)1 time
2 times
3 times
4 times
5 times (all sevens and elevens)Total (must equal 1.000000 !!)
binomcdf() and complements
• Sevens or elevens, n = 50 trials again• P(no more than 10 successes)– binomcdf(50, 8/36, 10)
• P(fewer than 10 successes)– binomcdf(50, 8/36, 9)
• P(more than 10 successes) – use complement!– 1 minus binomcdf(50, 8/36, 10)
• P(at least 10 successes) – use complement!– 1 minus binomcdf(50, 8/36, 9)
Mean, Variance, and Standard Deviation
• We had formulas and methods for probability distributions in general.
• The special case of the Binomial Probability Distribution has special shortcut formulas– Mean = – Variance = – Standard deviation =
Mean, Variance, and Standard Deviation
• Compute these for the seven-eleven experiment with n = 5 trials– Mean = – Variance = – Standard deviation =
Mean, Variance, and Standard Deviation
• Compute these for the seven-eleven experiment with n = 50 trials– Mean = – Variance = – Standard deviation =
Mean, Variance, and Standard Deviation
• Compute these for the seven-eleven experiment with n = 100 trials– Mean = and Standard deviation =
• “Expected value” – in 100 tosses of two dice, how many seven-elevens are expected?– Remember, the mean of a probability distribution
is also called the “expected value”
Standard Deviation
• What happens to the standard deviation in the seven-eleven experiment as the number of trials, n, increases?
trials Standard deviation
5
50
100
Advanced TI-84 Exercise
• Y1=binompdf(20,8/36,X)
• seq(X,X,0,20) STO> L1
• seq(Y1(X),X,0,20) STO> L2
• STAT PLOT for these two lists, histogram• WINDOW
Xmin=0, Xmax=20, Ymin=-0.1,Ymax=0.6• ZOOM 9:ZoomStat