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Contingency Table
A contingency table can be defined as the visual representation of the relationship between two or more categorical variables that can be evaluated and registered. It is a categorical version of the scatterplot, which is used to investigate the linear relationship between two variables. A contingency table is indeed a type of frequency distribution table that displays two variables at the same time.
Binomial Distribution
Binomial is an algebraic expression of the sum or the difference of two terms. Before knowing about binomial distribution, we must know about the binomial theorem.

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According to the sum, A newspaper article reported that people spend a mean of 6.5 hours per day watching TV with an s.d. of 1.8 hours.
Let X be a random variable representing the hours spent watching TV.
So,
Z=
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