{"id":290,"date":"2017-04-15T03:20:47","date_gmt":"2017-04-15T03:20:47","guid":{"rendered":"https:\/\/courses.lumenlearning.com\/conceptstest1\/chapter\/wrap-up-probability-and-probability-distributions\/"},"modified":"2019-09-30T05:40:08","modified_gmt":"2019-09-30T05:40:08","slug":"introduction-continuous-probability-distribution","status":"publish","type":"chapter","link":"https:\/\/courses.lumenlearning.com\/wm-concepts-statistics\/chapter\/introduction-continuous-probability-distribution\/","title":{"raw":"Introduction to Continuous Probability Distribution","rendered":"Introduction to Continuous Probability Distribution"},"content":{"raw":"

What you'll learn to do: Use a probability distribution for a continuous random variable to estimate probabilities and identify unusual events.<\/h2>\r\n\"A\r\n\r\nIn the last section, we studied discrete (listable) random variables and their distributions. Now we explore continuous (decimal valued) random variables that can take on values anywhere in an interval. For example, a person\u2019s exact weight without rounding is a continuous random variable. If rounded to the nearest pound, weight is a discrete random variable. Decimal valued numbers arise often in real life, often in measuring things such as weight or length. To best study real life data that has values lying all over an interval, we need to build a solid foundation in continuous probability distributions.\r\n

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What you’ll learn to do: Use a probability distribution for a continuous random variable to estimate probabilities and identify unusual events.<\/h2>\n

\"A<\/p>\n

In the last section, we studied discrete (listable) random variables and their distributions. Now we explore continuous (decimal valued) random variables that can take on values anywhere in an interval. For example, a person\u2019s exact weight without rounding is a continuous random variable. If rounded to the nearest pound, weight is a discrete random variable. Decimal valued numbers arise often in real life, often in measuring things such as weight or length. To best study real life data that has values lying all over an interval, we need to build a solid foundation in continuous probability distributions.<\/p>\n

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Did you have an idea for improving this content? We\u2019d love your input.<\/div>\n

Improve this page<\/a>Learn More<\/a><\/p>\n\n\t\t\t

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