# Difference between least squares and chi-squared

What is the difference between least squares and reduced chi-squared? For me they look nearly exactly the same, with the difference, that in chi-squared everything is divided by the variance. But despite from that, they are both identical?

• Check out this lecture; the direct answer to your question is on slide 5. In short: $\chi^2$ if $y$ are from a Gaussian distribution, if not – it's least squares (because then the resulting distribution is not $\chi^2$). – corey979 Mar 3 at 11:54
• Why the downvote? Seems a perfectly valid question to me. – corey979 Mar 3 at 11:55
• @corey979 Do I understand it right, that they use least squares to minimize chi-squared? I'm now even more confused as they also involve MLE there in the same context.. – Ben Mar 3 at 12:03

$$R^2$$ is used in order to understand the amount of variability in the data that is explained by your model. A $$R^2$$ of $$90\%$$ means that the $$90\%$$ of the variance of the data is explained by the model, that is a good value. On practice you cannot rely only on the $$R^2$$, but is a type of measure that you can find.