04 April 2018

Data Science: Normal Distribution (Definitions)

"A frequency distribution for a continuous variable, which exhibits a bell-shaped curve." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"The symmetric distribution of data about an average point. The normal distribution takes on the form of a bell-shaped curve. It is a graphic illustration of how randomly selected data points from a product or process response will mostly fall close to the average response, with fewer and fewer data points falling farther and farther away from the mean. The normal distribution can also be expressed as a mathematical function and is often called a Gaussian distribution." (Clyde M Creveling, "Six Sigma for Technical Processes: An Overview for R Executives, Technical Leaders, and Engineering Managers", 2006)

"A probability distribution forming a symmetrical bell-shaped curve." (Peter Oakander et al, "CPM Scheduling for Construction: Best Practices and Guidelines", 2014)

"Distribution of scores that are characterised by a bell-shaped curve in which the probability of a score drops off rapidly from the midpoint to the tails of the distribution. A true normal curve is defined by a mathematical equation and is a function of two variables (the mean and variance of the distribution)." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

"Also known as a bell-shaped curve or Gaussian curve, this is a distribution of data that is symmetrical around the mean: The mean, median, and mode are all equal, with more density in the center and less in the tails." (Jonathan Ferrar et al, "The Power of People: Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance", 2017)

"Also known as normal or the bell curve, is a type of continuous probability distribution which is defined by two parameters, the mean µ, and the standard deviation s." (Accenture)

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