Typically, it is better to remove this values, called outliers. But I would warn you not to use OLS regression in order to detect such outliers: you will probably construct the wrong model and the outliers will be probably wrong. Instead of it, use robust linear regression model and calculate standardized residuals for it (using robust estimator of standard deviation), and then remove everything that you can not expect by chance (so tune your threshold according to the sample size).

The explanation of outliers is that your data does not follow your theoretical assumptions. There can be several possible reasons: your theoretical assumptions are wrong or you have data points generated by random variable with other distribution. It can not be "diagnosed by photo", without full understanding of what are you trying to do.

Typically, it is better to remove this values, called outliers. But I would warn you not to use OLS regression in order to detect such outliers: you will probably construct the wrong model and the outliers will be probably wrong. Instead of it, use robust linear regression model and calculate standardized residuals for it (using robust estimator of standard deviation), and then remove everything that you can not expect by chance (so tune your threshold according to the sample size).

The explanation of outliers is that your data does not follow your theoretical assumptions. There can be several possible reasons: your theoretical assumptions are wrong or you have data points generated by random variable with other distribution (so you have mixture of two or more distributions with some proportion, typically, we call outliers everything that belong to the smallest proportion, so less than 50% of data are outliers). It can not be "diagnosed by photo", without full understanding of what are you trying to do.

Typically, it is better to remove this values, called outliers. But I would warn you not to use OLS regression in order to detect such outliers: you will probably construct the wrong model and the outliers will be probably wrong. Instead of it, use robust linear regression model and calculate standardized residuals for it (using robust estimations of standard deviation), and then remove everything that you can not expect by chance (so tune your threshold according to the sample size).

The explanation of outliers is that your data does not follow your theoretical assumptions. There can be several possible reasons: your theoretical assumptions are wrong or you have data points generated by random variable with other distribution. It can not be "diagnosed by photo", without full understanding of what are you trying to do.

Typically, it is better to remove this values, called outliers. But I would warn you not to use OLS regression in order to detect such outliers: you will probably construct the wrong model and the outliers will be probably wrong. Instead of it, use robust linear regression model and calculate standardized residuals for it (using robust estimator of standard deviation), and then remove everything that you can not expect by chance (so tune your threshold according to the sample size).

The explanation of outliers is that your data does not follow your theoretical assumptions. There can be several possible reasons: your theoretical assumptions are wrong or you have data points generated by random variable with other distribution. It can not be "diagnosed by photo", without full understanding of what are you trying to do.