agra,ahmedabad,ajmer,akola,aligarh,ambala,amravati,amritsar,aurangabad,ayodhya,bangalore,bareilly,bathinda,bhagalpur,bhilai,bhiwani,bhopal,bhubaneswar,bikaner,bilaspur,bokaro,chandigarh,chennai,coimbatore,cuttack,dehradun,delhi ncr,dhanbad,dibrugarh,durgapur,faridabad,ferozpur,gandhinagar,gaya,ghaziabad,goa,gorakhpur,greater noida,gurugram,guwahati,gwalior,haldwani,haridwar,hisar,hyderabad,indore,jabalpur,jaipur,jalandhar,jammu,jamshedpur,jhansi,jodhpur,jorhat,kaithal,kanpur,karimnagar,karnal,kashipur,khammam,kharagpur,kochi,kolhapur,kolkata,kota,kottayam,kozhikode,kurnool,kurukshetra,latur,lucknow,ludhiana,madurai,mangaluru,mathura,meerut,moradabad,mumbai,muzaffarpur,mysore,nagpur,nanded,narnaul,nashik,nellore,noida,palwal,panchkula,panipat,pathankot,patiala,patna,prayagraj,puducherry,pune,raipur,rajahmundry,ranchi,rewa,rewari,rohtak,rudrapur,saharanpur,salem,secunderabad,silchar,siliguri,sirsa,solapur,sri-ganganagar,srinagar,surat,thrissur,tinsukia,tiruchirapalli,tirupati,trivandrum,udaipur,udhampur,ujjain,vadodara,vapi,varanasi,vellore,vijayawada,visakhapatnam,warangal,yamuna-nagar

Correlation - Definition, Variables and Types


Correlation is defined as the method of creating a relationship or link between two or more than two variables. This method is useful in measuring quantities at a higher or ordinal level. The relation between x and y, or a and b, is known as correlation. It is one of the customary methods approached to calculate the measurements. It incorporates the use of a scattering graph or ‘scatter plot’ to create the relationship between two distinct variables.

Correlation Coefficient: This is a single-valued constant that sums up the relation between any two variables. It is generally denoted by the letter ‘r’. In statistics, the value of the correlation coefficient can reach a minimum of -1 and a maximum of 1, -1 < r < 1. Important results have been deduced due to the range of correlation.

  • If the correlation coefficient is close to zero (middle), either negative to zero, positive to zero, or zero implies that there is no relationship between the variables.
  • The correlation coefficient close to +1 means there exists a positive relationship between the two variables.
  • A correlation coefficient reaching -1 implies the relationship between the two variables to be negative.
  • Spearman’s Rho is used to calculate the coefficient for ordinal scales, while Pearson’s r is the standard method to find out the coefficient in the case of interval or ratio scales.

The two variables used in making a correlation are generalized as ‘X’ and ‘Y’. They are first related with the help of Pearson’s r method and then represented graphically with the help of a scatter diagram.

Scatter diagram: A scatter diagram is a graphic showing the values of two variables X and Y, and the relationship between these two variables. Variable X values are displayed along the horizontal axis, with Y values displayed on the vertical axis.

Later, one of the parameters is established as an independent parameter when a regression model is employed, and one is specified as the dependent variable. The independent variable X is thought to have some impact or influence on the dependent component Y in regression. The correlation techniques for both variables are symmetric, with no evidence of cause or effect being included in the statistical analysis.

Types of Correlation:

With the help of 'scatter diagram', mathematicians have deduced three types of correlations, which are explained below:

Positive Correlation: Positive correlation occurs due to the values of two parameters moving in the same direction so that a rise or decrease in one variable is followed by an increase or decrease in the other variable. It is further of two types: Strong positive correlation where the points are close to the linear graph, and weak positive correlation where the points are far away from the linear graph.

Negative Correlation: Negative correlation occurs due to the values of two components moving in the opposite direction from each other so that a rise or decrease in one variable is followed by a rise or decrease in the other variable. Negative correlations are also classified into two types. Similar to positive correlation, the negative correlation has a strong negative correlation where the points are close to the linear graph and a weak negative correlation where the points are far away from the linear graph.

No Correlation: when the two variables are not linear or related, it is known as no correlation.

Talk to our expert
Resend OTP Timer =
By submitting up, I agree to receive all the Whatsapp communication on my registered number and Aakash terms and conditions and privacy policy