e events E and T, are defined as E = the event that someone who is out of work and actively looking for work will find a job within the next month and T, = the event that someone who is currently it of work has been out of work for / months. For example, T, is the event that someone who is out of work has been out of work for 2 months. he following conditional probabilities are approximate and were read from a graph in a paper. P(E|T,) = 0.29 P(E|T) = 0.21 P(E|T5) = 0.19 P(E|T,) = 0.18 P(E|T,) = 0.17 P(E|T1) = 0.17 P(E|T,) = 0.28 P(E|T) = 0.20 P(E|T6) = 0.18 P(E|T) = 0.17 P(E|T10) = 0.17 P(E|T,2) = 0.17 a) Interpret the following two probabilities. (1) P(E|T,) = 0.29 O This is the probability that a person who has been out of work for a month will find work within the next month. O This is the probability that a person who has been out of work for three months will find work within the next three months. O This is the probability that a person who has been out of work for six months will find work within the next month. O This is the probability that a person who has been out of work for nine months will find work within the next three month. O This is the probability that a person who has been out of work for a year will find work within the next month. (ii) P(E|T) = 0.18 O This is the probability that a person who has been out of work for a month will find work within the next month. O This is the probability that a person who has been out of work for three months will find work within the next three months. O This is the probability that a person who has been out of work for six months will find work within the next month. O This is the probability that a person who has been out of work for nine months will find work within the next three month. O This is the probability that a person who has been out of work for a year will find work within the next month.
e events E and T, are defined as E = the event that someone who is out of work and actively looking for work will find a job within the next month and T, = the event that someone who is currently it of work has been out of work for / months. For example, T, is the event that someone who is out of work has been out of work for 2 months. he following conditional probabilities are approximate and were read from a graph in a paper. P(E|T,) = 0.29 P(E|T) = 0.21 P(E|T5) = 0.19 P(E|T,) = 0.18 P(E|T,) = 0.17 P(E|T1) = 0.17 P(E|T,) = 0.28 P(E|T) = 0.20 P(E|T6) = 0.18 P(E|T) = 0.17 P(E|T10) = 0.17 P(E|T,2) = 0.17 a) Interpret the following two probabilities. (1) P(E|T,) = 0.29 O This is the probability that a person who has been out of work for a month will find work within the next month. O This is the probability that a person who has been out of work for three months will find work within the next three months. O This is the probability that a person who has been out of work for six months will find work within the next month. O This is the probability that a person who has been out of work for nine months will find work within the next three month. O This is the probability that a person who has been out of work for a year will find work within the next month. (ii) P(E|T) = 0.18 O This is the probability that a person who has been out of work for a month will find work within the next month. O This is the probability that a person who has been out of work for three months will find work within the next three months. O This is the probability that a person who has been out of work for six months will find work within the next month. O This is the probability that a person who has been out of work for nine months will find work within the next three month. O This is the probability that a person who has been out of work for a year will find work within the next month.
MATLAB: An Introduction with Applications
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ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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Addition Rule of Probability
It simply refers to the likelihood of an event taking place whenever the occurrence of an event is uncertain. The probability of a single event can be calculated by dividing the number of successful trials of that event by the total number of trials.
Expected Value
When a large number of trials are performed for any random variable ‘X’, the predicted result is most likely the mean of all the outcomes for the random variable and it is known as expected value also known as expectation. The expected value, also known as the expectation, is denoted by: E(X).
Probability Distributions
Understanding probability is necessary to know the probability distributions. In statistics, probability is how the uncertainty of an event is measured. This event can be anything. The most common examples include tossing a coin, rolling a die, or choosing a card. Each of these events has multiple possibilities. Every such possibility is measured with the help of probability. To be more precise, the probability is used for calculating the occurrence of events that may or may not happen. Probability does not give sure results. Unless the probability of any event is 1, the different outcomes may or may not happen in real life, regardless of how less or how more their probability is.
Basic Probability
The simple definition of probability it is a chance of the occurrence of an event. It is defined in numerical form and the probability value is between 0 to 1. The probability value 0 indicates that there is no chance of that event occurring and the probability value 1 indicates that the event will occur. Sum of the probability value must be 1. The probability value is never a negative number. If it happens, then recheck the calculation.
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Transcribed Image Text:The events E and T, are defined as E = the event that someone who is out of work and actively looking for work will find a job within the next month and T = the event that someone who is currently
out of work has been out of work for i months. For example, T, is the event that someone who is out of work has been out of work for 2 months.
The following conditional probabilities are approximate and were read from a graph in a paper.
P(E|T,) = 0.29
P(E|T3) = 0.21
P(E|T5) = 0.19
P(E|T,) = 0.18
P(E|T,) = 0.17
P(E|T11) = 0.17
P(E|T,) = 0.28
P(E|T) = 0.20
P(E|T) = 0.18
P(E|TR) = 0.17
P(E|T10) = 0.17
P(E|T,2) = 0.17
(a) Interpret the following two probabilities.
(1) P(E|T,) = 0.29
O This is the probability that a person who has been out of work for a month will find work within the next month.
O This is the probability that a person who has been out of work for three months will find work within the next three months.
O This is the probability that a person who has been out of work for six months will find work within the next month.
O This is the probability that a person who has been out of work for nine months will find work within the next three month.
O This is the probability that a person who has been out of work for a year will find work within the next month.
(#) P(티To)=D 0.18
O This is the probability that a person who has been out of work for a month will find work within the next month.
O This is the probability that a person who has been out of work for three months will find work within the next three months.
O This is the probability that a person who has been out of work for six months will find work within the next month.
O This is the probability that a person who has been out of work for nine months will find work within the next three month.
O This is the probability that a person who has been out of work for a year will find work within the next month.
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