## 5 differences between correlation and regression

Last time we have discussed the concept of regression analysis. Let us discuss the difference between correlation and regression.

 CORRELATION 1 ) correlation means relationship between two or more variables.     2) It is only a tool of ascertaining the degree of the relationship between two variables therefore we cannot say that one variable is the cause and other the effect. 3) There may be non sense correlation between two variables. Like correlation between income and weight. 4) It is independent of change of scale and origin. 5) It is confined only two the study of linear relationship between variables. REGRESSION 1) It means stepping back and returning to average value. It is the measure of average relationship between two or more variables in terms of the original units of data. 2) It indicates the cause and effect relationship between the variables as one variable is taken as dependent while other as independent.   3) There is no such thing like non sense regression.   4) It is independent of change of origin not scale. 5) It studies linear as well as non linear relationship between the variables.

These are the 5 differences between correlation and regression

## Explanation of Regression analysis with regression equation and regression line

Meaning of Regression analysis:

It means stepping back and returning to average value. This is propounded by Sir Francis Galton. It is the measure of average relationship between two or more variables in terms of the original units of data.

Yon X Continue reading “Explanation of Regression analysis with regression equation and regression line”

## Null hypothesis and Alternative hypothesis

Let us discuss the difference between null and alternative hypothesis:

Null hypothesis

The hypothesis we want to test or a hypothesis of no difference is called null hypothesis. It is denoted by Ho. for example if we want to test that the population mean is equal to 400, we can write HO: µ : 400 and we read it as “ the null hypothesis is that the population mean is equal to 400.

1)      Null hypothesis is expressed in equality like HO: µ = µO Continue reading “Null hypothesis and Alternative hypothesis”

## Meaning, testing and types of hypothesis

Meaning of hypothesis:

It is a quantitative statement about population. It is some assumption or statement which may or may not be true about a population, which we want to test on the basis of evidence of random sample.

Testing  of hypothesis:

There is an element of risk of taking wrong decisions at the time of taking of decision. For example when a company launched a new product then they are interested to find out that it is going to be a big product in the market or not. It is here the modern theory of probability play an important role in decision-making which helps in arriving at a particular decision which is known as a testing of hypothesis. The theory of testing of hypothesis was initiated by J. Neyman and E. S. Pearson.  They use statistical techniques to arrive at a particular decision; in this sample size is fixed in advance.

There is another technique derived by Abhram Wald called Sequential Testing in which sample size is not fixed.

Types of hypothesis:

There are 2 types of hypothesis:

a)      Simple hypothesis

b)      Composite hypothesis

If the hypothesis completely specifies the population then it is simple otherwise it is composite.

I hope you understood the meaning, testing and types of hypothesis.

## Properties and importance of normal distribution

Meaning of Normal distribution or law of error

Binomial and passion distribution are discrete probability distribution. But normal probability distribution commonly called normal distribution. It is theoretical distribution for the continuous variable. The normal distribution was first discovered by English mathematician De Moivre in 1733.later it was rediscovered by Karl Gauss in 1809 and in 1812 by Laplace. Normal distribution is also called Gaussian distribution or Gaussian law of error as this theory describes the accidental error of measurements. Continue reading “Properties and importance of normal distribution”

## Types of sampling

Sampling is mainly divided into two types; probability or random sampling and non probability or non random sampling. In other words, Sampling methods are broadly classified into probability or random sampling and non probability or non random sampling. Let us discuss them one by one:

1)      Probability or random sampling: Under the probability or random sampling, each and every unit of the universe has an equal chance of being selected. It is the best technique based on the law of statistical regularity. Continue reading “Types of sampling”

## Types of errors in statistics

Errors in statistics or any statistical investigation can be broadly classified in two types:

a)      Sampling errors and b) non sampling errors

a)      Sampling errors: Even after taking care in selecting sample, there may be chances that true value is not equal to the observed value because estimation is based upon a part of the population not on the whole. Hence sampling give rise to certain errors known as sampling errors. Continue reading “Types of errors in statistics”

A Finite subset of population, selected from it, with the objective of investigating its properties is called ‘sample’. In sample method we select a part of the universe and conclusions are drawn on the basis of entire universe. R. A. Fisher define sample in four words—speed, economy, adaptability and scientific approach. In this article, we will discuss the advantages and disadvantages of sample method. Continue reading “Advantages and disadvantages of sample method”