This is very important in experimental research.

Spurious regression is nonsense regression.

The dictionary meaning of spurious is " not being what it purports to be" That means it is fake or false or fabricated. In other words, Spurious regression will indicate non existing relationship as if existing. When actually there is no causal connection they could have been correlated with each other.

This happens when R2 is typically very high and t value is significant

We carry out regression analysis using stationary variables.The means variances, co- variances change with time .Here the coefficient will be zero or near zero that means the two series data are independent.

Spurious regression becomes possible when there are two local trends which are similar but it may not be true though they move together.

It is necessary to test the data for stationary variables and co-integration before establishing time series regression correlation.

In short, when regression analysis is carried out using non stationary variables, there could be possibility of false or nonsense or spurious regression. It is indispensable in such cases to check whether the residual is non stationary.

Regression models for non-stationary variables impart spurious results only exception is when the model avoids the stochastic trends.

Spurious Regression refers to a well-known case of a spurious relationship can be found in the time-series literature, where a spurious regression is a regression that provides misleading statistical evidence of a linear relationship between independent non-stationary variables.

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This is very important in experimental research.

Spurious regression is nonsense regression.

The dictionary meaning of spurious is " not being what it purports to be" That means it is fake or false or fabricated. In other words, Spurious regression will indicate non existing relationship as if existing. When actually there is no causal connection they could have been correlated with each other.

This happens when R2 is typically very high and t value is significant

We carry out regression analysis using stationary variables.The means variances, co- variances change with time .Here the coefficient will be zero or near zero that means the two series data are independent.

Spurious regression becomes possible when there are two local trends which are similar but it may not be true though they move together.

It is necessary to test the data for stationary variables and co-integration before establishing time series regression correlation.

In short, when regression analysis is carried out using non stationary variables, there could be possibility of false or nonsense or spurious regression. It is indispensable in such cases to check whether the residual is non stationary.

Regression models for non-stationary variables impart spurious results only exception is when the model avoids the stochastic trends.

By C. W.J. GRANGER"If a theory suggests that there is a linear relationship between a pair of random variables X and Y, then an obvious way to test the theory is to estimate a regression equation of form

Estimation could be by least-squares and the standard diagnostic statistics would be a t-statistic on β, the R2 value and possibly the Durbin-Watson statistic d. With such a procedure there is always the possibility of a type ii error, that is accepting the relationship as significant when, in fact, X and Y are uncorrelated. This possibility increases if the error term e is autocorrelated, as first pointed out by Yule (1926). As the autocorrelation structure of e is the same as that for Y, when the true β = 0, this problem of ‘nonsense correlations’ or ‘spurious regressions’ is most likely to occur when testing relationships between highly autocorrelated series."

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Thanks for your contributions sirs

I fully agree with Dr K N Sheth sir Wonderful answer.

same here. Thanks.let's hear from others

I fully agree with Dr K N Sheth sir Wonderful answer.

the basic way to know is my regression spurious or not is test for Durbin-Watson test. if the R2 for you model is higher than Durbin-Watson's value, your model is spurious if not it means there is not such a problem with your model.