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In this video, we will discuss one of the problems we may face, if not more than this problem is called

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Petros Clandestinity.

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If you remember in the lecture in which we found out the confidence interval of Bita.

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I told you that this dumb sigma squared, which is the variance in the error term, is helping us find

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out the confidence interval of beta.

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There was one underlying assumption regarding the Sigma squared, based on which we calculated the standard

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errors.

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Confidence intervals, etc..

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The assumption is that this variance value, it's constant.

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Unfortunately, it is often the case that variances of the error terms are non constant.

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For instance, the variances of error terms may increase with the value of the response that is deemed

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by variable GraphicLy.

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We can plot Deidre's do against Y to identify the non constant variance of dieters.

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As you can see in this graph, this funnel like shape is suggesting non constant variance.

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This issue is known as a cadastre city.

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You can see that the magnitude of residuals tends to increase with defeated values.

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Why so variance at this point is limited within this range.

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Whereas variants here is larger as the boundaries are for the really.

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Now, how to handle heteros, cadastre city.

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When you faced this problem, where did variances increasing with larger footed values of why?

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We need to scale down these large values of a.

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So we can use functions like logo fly or under route way so that larger, well lives away are shut down.

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Thus leading to reduction in heteros congested city.

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You can see the graph of residuals against Longway.

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Do they do it now appear to have constant variants?

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Although there is some evidence of a slight non-linear relationship in the data.

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So this is how we identify, if that is heteros contested city in the model, and if there is, this

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is how we handle it.