Tag: random

Infrential Statistics – The Basics

muazzamLeave a comment

An Introduction to Inferential Statistics

“Inferences about a population from a random sample drawn from it is called inferential statistics.”

Inference is the reasoning involved in drawing a conclusion or making a logical judgment on the basis of evidence and prior conclusions rather than direct observation.”

Population:

The entire collection of items that is the focus of concern is our population.

Sample:

A small part of the population which is selected to investigate the properties of the population is known as a sample.

Sampling:

The part of statistical practice concerned with the selection of individual observations intended to concede some knowledge about a population of concern specially for the purpose of statistical inference.

Or simply the process of selecting a sample out of population is called Sampling.

Advantages of Sampling:

1. Time saving

2. Reduce cost

3. Greater Scope

4. Destructive Tests

5. May provide more reliable information

6. Reliability of estimates

Types of Sampling

  • Biased Sampling
  • Un-biased Sampling
  • Probability Sampling
  • Non-probability Sampling

1. Probability Sampling

If each unit in the population has known but not necessarily equal probability of selection in the sample, then this is known as probability or random sampling.

Types of Probability Sampling

  • Simple Random Sampling
  • Stratified Sampling
  • Systematic Random Sampling
  • Cluster Sampling or Multi-stage Sampling
  • Multi-phase sampling

2. Non-probability Sampling

If personal judgment is used to decide that which sampling units are to be included in the sample, then this is known as non-probability or non-random sampling.

Types of non-probability sampling

  • Purposive Sampling
  • Quota Sampling

Terms of Statistics

muazzamLeave a comment

Common Terms of Statistics

1. Population:
Totality of individuals or objects about which information is required is known as population.
e.g. population of patients of hepatitis , students of MBA classes at RYK campus etc…

2. Sample:
A small part of the population which is selected to investigate the properties of the population is known as sample.

3. Variable:
A characteristics which changes from individual to individual or object to object is known as variable.
e.g. height of students, income of people, weight of potatoes etc…

Variables are sub-divided into two categories:

3.1 Quantitative Variable:
Any variable which can be measured numerically is known as Quantitative variable.
e.g. income, speed, distance, temperature etc…

Quantitative variable are further divided into two types:

3.1.1 Discrete Variable:
A variable which can assume a finite number of values is known as discrete variable.
e.g. no. of leaves, no. of children etc…

3.1.2 Continuous Variable:
A variable which can have any value in a given interval [a,b] is known as continuous variable. Therefore the number of possible values of a continuous variable is infinite.
e.g. height, weight, distance etc…

3.2 Qualitative Variable:
Any variable which can’t be measured numerically is known as Qualitative variable. It is also known as “Attribute”.
e.g. smoking habit, religion, eye color, etc…

4. Constant:
A characteristic which does not change its value from individual to individual and object to object is called a constant.
OR “A variable which have only one value is called constant.”

5. Measurement Error:
The difference between the actual value (True) and the response we get (Recorded) is measurement error. It may be positive or negative.

5.1. Random Error:
If error is due to human mistake or the direction of error is not the same that it’s said to be a Random Error.
e.g. reading error, human mistake in measurement etc…

5.2 Systematic Error:
If the direction of the error in all data is same then it is called a systematic error.
e.g. Machine error, scale error etc…