Types of distributions in statistics:
Uniform distribution:
Uniform distribution is a type of probability distribution where all the outcomes are equally likely. It is also called as rectangular distribution.
There are two types of uniform distribution they are discrete uniform distribution and continuous uniform distribution.
Example for discrete uniform distribution is the probability of getting any number in a dice is 1/6. A vertical line is drawn at each finite value.
Example for continuous distribution is height, weight, depth of ocean. It has the values in a specified range or interval. It is drawn as a normal graph in a rectangular shape.
Binomial distribution:
It is a type of distribution in which there are only two outcomes like success or failure, yes or no, true or false. Example is getting head or tail when a coin is tossed.
Bernoulli distribution:
Bernoulli distribution is a type of discrete probability distribution. This distribution is also used where there are only two possible outcomes like success or failure, yes or no, true or false, 1 or 0 etc. Example is tossing the coin only once.
The difference between binomial and Bernoulli distribution is Bernoulli distribution is tossing the coin only once but binomial distribution is tossing the coin multiple times. The sum of the Bernoulli distribution is binomial distribution.
Normal distribution:
Normal distribution is also called as Gaussian distribution. It is type of continuous probability distribution. The graph of the normal distribution is bell shaped.
It is always symmetric about mean i.e. the values below and above the mean are equal. 68% of the values fall within one standard deviation from mean and 95% of the values fall within two standard deviation from mean.
Log normal distribution:
Log normal distribution is plotted by taking log values of the normal distribution. The graph is right skewed and has a long right tail.
Normal distribution has both positive and negative values but the log normal distribution has only positive values. So log normal distribution is used where negative values are not possible and needs conversion.
Negative and Positive skewed distribution:
When one tail is longer than the other tail and when the distribution is asymmetric it is said to be skewed distribution.
When the right tail is longer it is said to be positive skewed distribution and when the left tail is longer it is said to be negative skewed distribution.
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