Probability Distributions Report

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Applied Business Research and Statistics

QNT 531

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August 2, 2010

Abstract

Statistical functions that describe all the possible values and likelihoods that a random variable can take within a given range are referred to as probability distribution. While examining probability distribution I found there are two forms, cumulative, and uniform distribution. I used the example of flipping a coin to exhibit the cumulative probability if the coin flips would result in one or fewer heads. To show the uniform distribution I used tossing a die to demonstrate the probability that the die will land on the number six. Probability distribution can be discrete or continuous, the example I used for discrete was, if I tossed a coin six times, and the coin landed two heads or three heads but not two and one half. To demonstrate continuous probability, I examined a company’s investors or executives determining the possible returns that a stock may yield in the future. Basically this report is expressing on a day-to-day basis probability distribution is incorporated most decisions we make.

Probability distributions report

Probability distribution is defined as, “A statistical function that describes all the possible values and likelihoods that a random variable can take within a given range. This range will be between the minimum and maximum statistically possible values, but where the possible value is likely to be plotted on the probability distribution depends on a number of factors, including the distributions mean, standard deviation, skewness and kurtosis.” ("Statistics Tutorial: Probability Distributions", 2010, p. 2)

Probability distribution comes in two flavors cumulative and uniform distribution. A cumulative probability “refers to the probability that the value of a random variable falls within a specified range” ("Statistics Tutorial: Probability Distributions", 2010, para. 5)....

????????????

??????????

Applied Business Research and Statistics

QNT 531

???????????????

August 2, 2010

Abstract

Statistical functions that describe all the possible values and likelihoods that a random variable can take within a given range are referred to as probability distribution. While examining probability distribution I found there are two forms, cumulative, and uniform distribution. I used the example of flipping a coin to exhibit the cumulative probability if the coin flips would result in one or fewer heads. To show the uniform distribution I used tossing a die to demonstrate the probability that the die will land on the number six. Probability distribution can be discrete or continuous, the example I used for discrete was, if I tossed a coin six times, and the coin landed two heads or three heads but not two and one half. To demonstrate continuous probability, I examined a company’s investors or executives determining the possible returns that a stock may yield in the future. Basically this report is expressing on a day-to-day basis probability distribution is incorporated most decisions we make.

Probability distributions report

Probability distribution is defined as, “A statistical function that describes all the possible values and likelihoods that a random variable can take within a given range. This range will be between the minimum and maximum statistically possible values, but where the possible value is likely to be plotted on the probability distribution depends on a number of factors, including the distributions mean, standard deviation, skewness and kurtosis.” ("Statistics Tutorial: Probability Distributions", 2010, p. 2)

Probability distribution comes in two flavors cumulative and uniform distribution. A cumulative probability “refers to the probability that the value of a random variable falls within a specified range” ("Statistics Tutorial: Probability Distributions", 2010, para. 5)....