{"id":6473,"date":"2021-10-30T07:40:40","date_gmt":"2021-10-30T07:40:40","guid":{"rendered":"https:\/\/www.goseeko.com\/blog\/?p=2923"},"modified":"2025-12-14T09:29:55","modified_gmt":"2025-12-14T03:59:55","slug":"what-is-correlation-why-it-is-so-important","status":"publish","type":"post","link":"https:\/\/www.goseeko.com\/blog\/what-is-correlation-why-it-is-so-important\/","title":{"rendered":"What is Correlation? Why it is so important?"},"content":{"rendered":"\n

Overview<\/strong><\/h2>\n\n\n\n

Correlation is the measurement of the strength of a linear relationship between two variables say x and y.<\/p>\n\n\n\n

In other words, we define it as if the change in one variable affects a change in other variables, then these two variables are said to be correlated.<\/p>\n\n\n\n

For example:<\/p>\n\n\n\n

1.     The correlation between a person\u2019s income and expenditures.<\/p>\n\n\n\n

2.     As the temperature goes up, the demand for ice cream also goes up.<\/p>\n\n\n\n

Types of correlation<\/strong><\/h2>\n\n\n
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Positive correlation-<\/strong> When both variables move in the same direction, or if the increase in one variable results in a corresponding increase in the other one is called positive correlation.<\/p>\n\n\n

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Negative correlation- <\/strong>When one variable increases and other decreases or vise-versa, then the variables said to be negatively correlated.<\/p>\n\n\n

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No correlation- <\/strong>When two variables are independent and do not affect each other then there will be no correlation between the two and said to be uncorrelated.<\/p>\n\n\n

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Note- (Perfect correlation)- <\/strong>When a variable changes constantly with the other variable, then these two variables are said to be perfectly correlated.<\/p>\n\n\n\n

Scatter plots or dot diagrams<\/strong><\/h2>\n\n\n\n

A scatter or dot diagram is used to check the correlation between the two variables.<\/p>\n\n\n\n

It is the simplest method to represent bivariate data.<\/p>\n\n\n\n

When the dots in the diagram are very close to each other, then we can say that there is a fairly good correlation.<\/p>\n\n\n\n

If the dots are scattered then we get a poor correlation.<\/p>\n\n\n\n

Karl Pearson\u2019s coefficient of correlation<\/strong><\/h2>\n\n\n\n

Karl Person\u2019s coefficient of correlation is also called product-moment correlation coefficient.<\/p>\n\n\n\n

It is denoted by \u2018r\u2019, and defined as-<\/p>\n\n\n

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Here and are the standard deviations of these series.<\/p>\n\n\n\n

Alternate formula-<\/p>\n\n\n

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Note-<\/p>\n\n\n\n