Normal Distribution or (Gaussian) distribution in Statistics?
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A Normal Distribution is also called?“Gaussian Distribution”?or more commonly known as?“Bell Curve”?as the probability distribution function plot of a normal distribution looks very like ?? bell-shaped.
A Normal Distribution is a?univariate probability distribution,?which means it is a distribution for only?one?random variable.?Note: Multivariate normal distributions do exist but in this article, we would be talking about only univariate normal distribution.
The normal distribution is an arrangement of data points in which most values form a cluster in the middle of the range and the rest taper off symmetrically toward either extreme ends.
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Normal distribution, also known as Gaussian distribution, is a continuous probability distribution that is symmetric around its mean, μ, and characterized by its standard deviation, σ. It's often referred to as the bell curve due to its shape.
In a normal distribution:
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In probability theory, a?normal?distribution?is a type of continuous probability distribution for a real-valued random variable (say X). The general form of its probability density function is
68–95–99.7 Rule?for Bell Curve!
Normal distributions are very important in?statistics?and often they are very naturally occurring. One of the main reasons for the popularity of the Normal Distribution curve is that it occurs very commonly in most of the things we see in nature around us.?For example: in?finance, like the?salary distribution?in an office,?healthcare,?hydrology,?height/weight?distributions,?grading?distribution,?Percentile?calculations, and much more. You name it and normal distribution owns it.
Source: Blitsnap, Normal Desktop?Probability Machine
To solve problems involving normal distribution, you typically follow these steps:
Where:
X is the value from the original distribution,
μ is the mean of the original distribution,
σ is the standard deviation of the original distribution.
Remember, there are various properties and formulas associated with the normal distribution, such as the 68-95-99.7 rule (Empirical Rule) which states that approximately 68%, 95%, and 99.7% of the data falls within 1, 2, and 3 standard deviations from the mean, respectively. Familiarizing yourself with these properties and formulas will help you solve normal distribution problems effectively.
Q1 ?Normal Distribution is symmetric is about ?
a) Variance?? b) Mean?? c) Standard Deviation? d)None
Q2 ?Area under the standard normal curve is
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a) zero????? b) 1.0????? c) 0.5??????? d) ∞
Q3 ?In case of Normal Distribution? which is true
a)Mean = Median ≠Mode?????
b)Mean ≠ Median ≠Mode?????
c)Mean = Median = Mode??
d)None of above??
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Profesional
12 个月Is always good to check and understand the core of the science.
Thanks for sharing!
Six Sigma Freelance Consultant at Sigma Solutions
1 年I once read... "A normal distribution? I have never seen one..." - Deming
Retired / Management Consulting
1 年Please at least read Wheeler & Chambers "Understanding Statistical Process Control". Once that info. sinks in continue to read the other works of Dr. Wheeler so come the end of the day you will understand the reality of industrial statistics and how FUBAR the six-sigma cult is.