Titlebook: Introduction to Statistics Using R; Mustapha Akinkunmi Book 2019 Springer Nature Switzerland AG 2019

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Continuous Probability Distributions,iable takes only integer values or countably infinite number of possible outcomes. However, in the countinous probability distribution, the random variable takes any real value within a specified range. Typical examples of countinous random variables are weight, temperature, height, and some economi

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Confidence Intervals for Single Population Mean and Proportion,duce our risk of estimation by constructing a range in which the exact value of the population parameter lies. This process gives us the self-assurance at a certain percentage that the estimate is capable of predicting the real value of the population of interest. This concept describes the . or . I

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Poisson Distribution,stribution. It is used to approximate the count of events that occur randomly and independently. The Poisson distribution may calculate number of instances that should occur in a certain amount of time, distance, area, or volume. For instance, the random variable could be used to estimate the number

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https://doi.org/10.1007/978-3-662-10266-4Chemie; Mischen; Physikalische Chemie; Reaktionskinetik; Schadstoff; Schadstoffe; Transportprozess; Verbren

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