Notes for Bayesian conditional probability
Notes for the expected value of a game
Notes for converting between classic and modern pari-mutuel statements of profit and risk.
Thursday, September 26, 2019
Thursday, September 19, 2019
Friday, September 13, 2019
Notes for Homework 4, due September 16
Notes on contingency tables
In Excel: the sum of a row will be of the form
=sum(a1:c1) where the letters are the changing indices
The sum of a column will be
=sum(a1:a5) where the numbers are the changing indices
Notes on the Central Limit Theorem
In Excel: In my version of Excel, the function that gives you the proportion less than a z-score is
=norm.dist(x, mean, standard_dev, cumulative)
The cumulative value should be 1 always.
If we are doing a Central Limit z-score based on the average of a sample, the instruction is changed as follows.
=norm.dist(x, mean, standard_dev/sqrt(n), cumulative)
where n is the size of the sample.
Notes on probabilities of independent trials
In Excel: Again, different versions of Excel have different spelling of functions, and in my version the function is
=binom.dist(r, n, p, cumulative)
If cumulative = 0, you get the value of exactly r successes in n trials.
If cumulative = 1, you get the value of at least r successes in n trials.
In Excel: the sum of a row will be of the form
=sum(a1:c1) where the letters are the changing indices
The sum of a column will be
=sum(a1:a5) where the numbers are the changing indices
Notes on the Central Limit Theorem
In Excel: In my version of Excel, the function that gives you the proportion less than a z-score is
=norm.dist(x, mean, standard_dev, cumulative)
The cumulative value should be 1 always.
If we are doing a Central Limit z-score based on the average of a sample, the instruction is changed as follows.
=norm.dist(x, mean, standard_dev/sqrt(n), cumulative)
where n is the size of the sample.
Notes on probabilities of independent trials
In Excel: Again, different versions of Excel have different spelling of functions, and in my version the function is
=binom.dist(r, n, p, cumulative)
If cumulative = 0, you get the value of exactly r successes in n trials.
If cumulative = 1, you get the value of at least r successes in n trials.
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