Could you search “normal distribution” and find the explanation that you like? Paste here with the link of the webpage.
14 thoughts on “Day10 6/13/2019”
http://mathworld.wolfram.com/NormalDistribution.html
A normal distribution in a variate X with mean mu and variance sigma^2 is a statistic distribution with probability density function
P(x)=1/(sigmasqrt(2pi))e^(-(x-mu)^2/(2sigma^2))
(1)
on the domain x in (-infty,infty). While statisticians and mathematicians uniformly use the term “normal distribution” for this distribution, physicists sometimes call it a Gaussian distribution and, because of its curved flaring shape, social scientists refer to it as the “bell curve.” Feller (1968) uses the symbol phi(x) for P(x) in the above equation, but then switches to n(x) in Feller (1971).
A normal distribution, sometimes called the bell curve, is a distribution that occurs naturally in many situations. For example, the bell curve is seen in tests like the SAT and GRE. The bulk of students will score the average (C), while smaller numbers of students will score a B or D. An even smaller percentage of students score an F or an A. This creates a distribution that resembles a bell (hence the nickname). The bell curve is symmetrical. Half of the data will fall to the left of the mean; half will fall to the right.
The empirical rule tells you what percentage of your data falls within a certain number of standard deviations from the mean:
• 68% of the data falls within one standard deviation of the mean.
• 95% of the data falls within two standard deviations of the mean.
• 99.7% of the data falls within three standard deviations of the mean.
The normal distribution is the most important and most widely used distribution in statistics. It is sometimes called the “bell curve,” although the tonal qualities of such a bell would be less than pleasing. It is also called the “Gaussian curve” after the mathematician Karl Friedrich Gauss..
http://mathworld.wolfram.com/NormalDistribution.html
A normal distribution in a variate X with mean mu and variance sigma^2 is a statistic distribution with probability density function
P(x)=1/(sigmasqrt(2pi))e^(-(x-mu)^2/(2sigma^2))
(1)
on the domain x in (-infty,infty). While statisticians and mathematicians uniformly use the term “normal distribution” for this distribution, physicists sometimes call it a Gaussian distribution and, because of its curved flaring shape, social scientists refer to it as the “bell curve.” Feller (1968) uses the symbol phi(x) for P(x) in the above equation, but then switches to n(x) in Feller (1971).
This one seems cool.
https://www.mathsisfun.com/data/standard-normal-distribution-table.html
this might be better.
https://stattrek.com/probability-distributions/normal.aspx
https://www.investopedia.com/terms/n/normaldistribution.asp
This site breaks down Normal Distribution in a very understandable way.
https://www.mathsisfun.com/data/standard-normal-distribution.html
normal distribution is a function that represents the distribution of many random variables as a symmetrical bell-shaped graph.
http://wwwf.imperial.ac.uk/metric/metric_public/statistics/normal_distribution/normal_distribution.html
A normal distribution, sometimes called the bell curve, is a distribution that occurs naturally in many situations. For example, the bell curve is seen in tests like the SAT and GRE. The bulk of students will score the average (C), while smaller numbers of students will score a B or D. An even smaller percentage of students score an F or an A. This creates a distribution that resembles a bell (hence the nickname). The bell curve is symmetrical. Half of the data will fall to the left of the mean; half will fall to the right.
The empirical rule tells you what percentage of your data falls within a certain number of standard deviations from the mean:
• 68% of the data falls within one standard deviation of the mean.
• 95% of the data falls within two standard deviations of the mean.
• 99.7% of the data falls within three standard deviations of the mean.
https://www.statisticshowto.datasciencecentral.com/probability-and-statistics/normal-distributions/
https://statisticsbyjim.com/basics/normal-distribution/
http://onlinestatbook.com/2/normal_distribution/intro.html
http://onlinestatbook.com/2/normal_distribution/intro.html
I would say this site has a great understandable explanation on normal distribution.
http://mathworld.wolfram.com/NormalDistribution.html
https://365datascience.com/normal-distribution/
The normal distribution is the most important and most widely used distribution in statistics. It is sometimes called the “bell curve,” although the tonal qualities of such a bell would be less than pleasing. It is also called the “Gaussian curve” after the mathematician Karl Friedrich Gauss..
http://onlinestatbook.com/2/normal_distribution/intro.html