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Math formulas for the field in calculas, useful formulas also techiniques during the calculations
Typology: Exercises
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(1) Methods of Assigning Probabilities:
(a) Classical Method: when the probabilities are assigned based on laws or rules. In this case, the probability of an event E in a sample space S is
P (E) =
Examples:
(b) Relative Frequency of Occurrence: when the assignment of probabilities is based on historical data. In this case, the probability of an event E in a sample space S is
P (E) =
Number of times the event E has occurred Total number of opportunities for the event to occur
Examples:
(c) Subjective probability: when the assignment of probabilities is based on feelings, knowledge and/or past experiences of a person or a group of people. Examples:
(2) Counting the Probabilities:
Different methods for computing the numbers of elements in E and S: P (E) =
(a) The mn-Counting Rule: Suppose a procedure can be realized in a sequence of k (independent) steps. If the i-th step offer ni possibilities, for all i = 1, 2 ,... , n, then the procedure can be realized in n 1 n 2 n 3 · · · nk different ways.
Example: In a certain country, all license plates consist of three letters (A to Z), followed by three digits (0 to 9). How many different plates are there? Solution: There are 26 ∗ 26 ∗ 26 ∗ 10 ∗ 10 ∗ 10 = 17, 576 , 000 different plates.
(b) Combinations: Suppose there are n different items. The number of ways to select r (with 0 ≤ r ≤ n) different items is
nCr =^
n! r!(n − r)!
Examples:
1
(c) Permutations: Suppose there are n different items. The number of ways to select and order r (with 0 ≤ r ≤ n) different items is
nPr =^
n! (n − r)!
Examples:
(3) Marginal, Joint, Union and Conditional Probabilities: Let S be a sample space and E, F be two events. (a) The marginal probability of E is P (E) = #(#(ES)). (b) The joint probability of E and F is
P (E ∩ F ) =
where E ∩ F = {x ∈ S | x ∈ E and x ∈ F }. (c) The union probability of E and F is
P (E ∪ F ) =
where E ∪ F = {x ∈ S | x ∈ E or x ∈ F }. (d) The (conditional) probability that E will occur given that F has occured is
P (E|F ) =
Definitions:
Some Laws:
Law of Addition: P (E ∪ F ) = P (E) + P (F ) − P (E ∩ F ), or P (E ∩ F ) = P (E) + P (F ) − P (E ∪ F )
Law of Multiplication: P (E|F )P (F ) = P (E ∩ F ) = P (F |E)P (E)
Law of Conditional Probability: P (E|F ) =
If E 1 , E 2 ,... , En and mutually exclusive and collectively exhaustive events, then
P (F ) = P (F ∩ E 1 ) + P (F ∩ E 2 ) + · · · + P (F ∩ En).