A characteristic , usually unknown of the population.
Characteristic of the population , such as its mean ,variance and standard deviations are population Parameters .
An observed characteristic of a sample are known as statistics
Estimate the use of a sample characteristic (statistic) to guess or approximate a population characteristic (parameter)
Step-by-step explanation:
Introduction:-
The outcome of a statistical experiment may be recorded either as a numerical value or as a descriptive representation.
Example:-1
When a pair of dice are tossed and the sum of the numbers on the faces is the outcome of interest, we record a numerical value.
Example :-2
If the students of a certain school are given blood tests and the type of blood is of interest, then a descriptive representation.
Population:-
Population is consists of the total observation's with which we are concerned
The number of observations in the population is defined to be the size of the population
Sample:- A sample is a subset of population.
Samples are classified in two ways
Large sample : if the size of the sample n≥ 30 is called the large sample.
small sample: if the size of the sample n<30 is called the small sample.
Statistics uses different names and symbols to distinguish characteristics
of the population from those of a sample.
Parameters :-
A characteristic , usually unknown of the population.
Characteristic of the population , such as its mean ,variance and standard deviations are population Parameters .
In symbolically mean of the population parameter is denoted by μ
In symbolically variance of the population parameter is denoted by σ²
In symbolically standard deviation of the population parameter is denoted by σ
Statistic :-
An observed characteristic of a sample are known as statistics
Estimate the use of a sample characteristic (statistic) to guess or approximate a population characteristic (parameter)
In symbolically mean of the sample statistic is denoted by x⁻
In symbolically variance of the sample statistic is denoted by S²
In symbolically standard deviation of the statistic is denoted by S
