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I have no idea why you would like that, but this should contain what you are looking for.

Are there a lot of fitted values $< 0$? If not then I do not think that you model is wrong (per se), it's just that the estimator (OLS?) can't fit the data (around 0) well. Most likely because there is not a lot of test scores at 0?

You could do exponential regression if indeed $Y$ is count variable; the tobit model is another way to go if you are looking for corner solution. But both of these models are harder (than OLS) to interpret - because they are non-linear - OLS is often the easy choice.

EDIT: Please note that when you force the intercept, there is no agreed upon way of calculating $R^2$ - and some might say that you cannot know what it's actually measure of.

I have no idea why you would like that, but this should contain what you are looking for.

Are there a lot of fitted values $< 0$? If not then I do not think that you model is wrong (per se), it's just that the estimator (OLS?) can't fit the data (around 0) well. Most likely because there is not a lot of test scores at 0?

You could do exponential regression if indeed $Y$ is count variable; the tobit model is another way to go if you are looking for corner solution. But both of these models are harder (than OLS) to interpret - because they are non-linear - OLS is often the easy choice.

EDIT: Please note that when you force the intercept, there is no agreed upon way of calculating $R^2$ - and some might say that you cannot know what it's actually measure of.

I have no idea why you would like that, but this should contain what you are looking for.

Are that alot of fitted values < 0? If not then I do not think that you model is wrong (per say), it's just that the estimator (OLS?) can't fit the data (around 0) well. Most likely because there is not a lot test scores at 0?

You could do exponential regression if indeed Y is count variable, the tobit model is another way to go if you are looking for cornor solution. But both of these models are harder (than OLS) to interpret - because they are non-linear - OLS is often the easy choice.

EDIT: Please note that when you force the intercept, there is no agreed upon way of calculating R^2 - and some might say that you cannot know what it's actually measure of.

I have no idea why you would like that, but this should contain what you are looking for.

Are there a lot of fitted values $< 0$? If not then I do not think that you model is wrong (per se), it's just that the estimator (OLS?) can't fit the data (around 0) well. Most likely because there is not a lot of test scores at 0?

You could do exponential regression if indeed $Y$ is count variable; the tobit model is another way to go if you are looking for corner solution. But both of these models are harder (than OLS) to interpret - because they are non-linear - OLS is often the easy choice.

EDIT: Please note that when you force the intercept, there is no agreed upon way of calculating $R^2$ - and some might say that you cannot know what it's actually measure of.

I have no idea why you would like that, but this should contain what you are looking for.

Are that alot of fitted values < 0? If not then I do not think that you model is wrong (per say), it's just that the estimator (OLS?) can't fit the data (around 0) well. Most likely because there is not a lot test scores at 0?

You could do exponential regression if indeed Y is count variable, the tobit model is another way to go if you are looking for cornor solution. But both of these models are harder (than OLS) to interpret - because they are non-linear - OLS is often the easy choice.

EDIT: Please note that when you force the intercept, there is no agreed upon way of calculating R^2 - and so might say that you cannot know what it's actually measure of.

I have no idea why you would like that, but this should contain what you are looking for.

Are that alot of fitted values < 0? If not then I do not think that you model is wrong (per say), it's just that the estimator (OLS?) can't fit the data (around 0) well. Most likely because there is not a lot test scores at 0?

You could do exponential regression if indeed Y is count variable, the tobit model is another way to go if you are looking for cornor solution. But both of these models are harder (than OLS) to interpret - because they are non-linear - OLS is often the easy choice.

EDIT: Please note that when you force the intercept, there is no agreed upon way of calculating R^2 - and some might say that you cannot know what it's actually measure of.