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This section will help to understand how do you write the statements as a conditional. Conditionals are 'if-then' statements. More precisely, conditionals are statements that say if one thing happens, another will follow. E.g. Statements such as "I have a job." may be replaced by*p* and the conditional statement, "If I have a job, then I must work." might be replaced by *p**q*, where *q* in this case is equivalent to "I must work".

It is also explains with the help of some examples, how to identify the hypothesis and the conclusion of a conditional and write its truth value. To represent an if-then statement symbolically, let*p* represent the hypothesis and *q* represent the conclusion. Then we have the basic form of an if-then statement shown below:

If*p* then *q*

*p* = hypothesis *q* = Conclusion

For example, in the conditional “If point*D* is between point *C* and *E*, then *CD* + *DE* = *CE*”, the statement “point *D* is between point *C* and *E*” is a hypothesis and “*CD* + *DE* = *CE*” is a conclusion.

This section will help to understand how do you write the statements as a conditional. Conditionals are 'if-then' statements. More precisely, conditionals are statements that say if one thing happens, another will follow. E.g. Statements such as "I have a job." may be replaced by

It is also explains with the help of some examples, how to identify the hypothesis and the conclusion of a conditional and write its truth value. To represent an if-then statement symbolically, let

If

For example, in the conditional “If point

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Understand the conditional probability of A given B as P(A and B)/P(B), and interpret independence of A and B as saying that the conditional probability of A given B is the same as the probability of A, and the conditional probability of B given A is the same as the probability of B.

Construct and interpret two-way frequency tables of data when two categories are associated with each object being classified. Use the two-way table as a sample space to decide if events are independent and to approximate conditional probabilities.

Find the conditional probability of A given B as the fraction of B's outcomes that also belong to A, and interpret the answer in terms of the model.