# Should linear regression always be forced through the origin?

• garyman
In summary, it is generally accepted that forcing your curve through the origin can give you a better estimate of your gradient.
garyman
Wasn't sure which section this should be in, so please move if inappropriate. If a plot between two variables theoretically should pass through the origin, should you force the linear regression through the origin. I seem to get a much better value for my gradient when forced through the origin, and was just wondering if this is acceptable.

I would not force it through the origin, because in reality it might not go through it due to some error.

Curve fitting experimental data is not a simple thing. FWIW, we teach all students and post-docs a 1-credit ethics course where this (in addition to other scenarios) is discussed.

If the theory is sound, the curve should be generated by the theory and experimental data compared against it. Deviations from experimental data points and theoretical predictions are then first decided to be statistically significant or not, and then described and (possibly) explained.

If there is no theory (as is sometimes the case in biology, for example), then there's no curve to fit- there is no known functional (quantitative) relationship between the two quantities, although good data may imply a particular relationship.

garyman said:
Wasn't sure which section this should be in, so please move if inappropriate. If a plot between two variables theoretically should pass through the origin, should you force the linear regression through the origin. I seem to get a much better value for my gradient when forced through the origin, and was just wondering if this is acceptable.

You should never force your curve fit to pass through a point that is not part of your data. In many cases, if the theoretical description includes a (0,0), then you can simply point out the discrepancy in your data and your theoretical description, and maybe discuss possible reasons on why that occurred (there are many systematic errors that could have caused that). But at no point should you force your data to "obey" the theory.

Zz.

## 1. How do I choose the right type of graph for my data?

The type of graph you choose depends on the type of data you are working with. Bar graphs are used for comparing discrete categories, line graphs are used for showing trends over time, and scatter plots are used for showing relationships between two continuous variables. Consider the purpose of your data and choose the graph that best represents it.

## 2. What is the purpose of labeling my axes on a graph?

Labeling the axes is important because it provides context and meaning to the data being presented. The horizontal axis (x-axis) represents the independent variable and the vertical axis (y-axis) represents the dependent variable. Labels should be clear and concise, and include units of measurement if applicable.

## 3. How do I determine the appropriate scale for my graph?

The scale of a graph refers to the range of values shown on the axes. It is important to choose a scale that allows the data to be easily interpreted and clearly demonstrates any patterns or trends. Generally, the scale should start at zero and increase in equal intervals.

## 4. What is the purpose of including a title and legend on a graph?

The title of a graph should provide a brief overview of the data being presented. This helps the reader to understand the purpose of the graph and what information is being displayed. A legend is used to explain any symbols or colors used in the graph and their corresponding meaning. This makes it easier for the reader to interpret the data.

## 5. How do I interpret the data on my graph?

Interpreting the data on a graph involves looking at the patterns, trends, and relationships shown. This can help to identify any significant findings or conclusions. It is important to consider the context of the data and any potential limitations or factors that may have influenced the results.

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