Error in declination of linear regression

In summary, the conversation discusses a lab exercise where different masses of a magnetic material were measured on a scale while changing the strength of the magnetic field. Linear regression was then used to find the slope, but the error in the measurements was not taken into account. The question is how to find the total error of both the regression and the measurements. It is noted that regression algorithms will not distinguish between different sources of variation and will only give numbers for the total variation. Therefore, it is unlikely that the error in the measurements can be completely removed from the results.
  • #1
UiOStud
9
2
During a lab exercise we measured different masses of a magnetic material on a scale while changing the strength of the magnetic field it was in. Afterwards we plotted the masses and the fieldstrength hoping to find a linear slope. Then we drew a linear slope by using linear regression and found the declination of the slope and it's error. However this error does not take into account that the values of the masses also have an error. How can I fin the total error of the declination with the errorin the measurements in mind?
 
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  • #2
Are you asking how you can remove the measurement error from your results to get the true slope of the line? I doubt that you can.
 
  • #3
Okay, let me try to explain in other words. I need to find the exact error in the linear slope I'm drawing. Because the points are not actually perfectly aligned there is an error in my regression. But finding the error from the residuals is not enough because there is an extra error comming from the fact that there is an error in the measurements themselves. The total error will be greater than the one I find only looking at the residuals. How can I find the total error of both the error in measurements and the error of regression?
 
  • #4
Regression algorithms in statistics packages will give you numbers for the statistical standard deviation of the estimated parameters, like the slope. They will not distinguish between different sources of variation. So the numbers they give will be for the total variation of the measurements and the mass.
 
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What is "Error in declination of linear regression"?

"Error in declination of linear regression" refers to the degree to which the actual data points deviate from the predicted values in a linear regression model. It measures the accuracy of the model in fitting the data and can help determine the reliability of the results.

How is "Error in declination of linear regression" calculated?

The error in declination of linear regression is typically calculated using a metric called the root mean square error (RMSE). This involves taking the square root of the average of the squared differences between the actual data points and the predicted values. A lower RMSE indicates a better fit for the model.

What causes "Error in declination of linear regression"?

"Error in declination of linear regression" can be caused by a variety of factors. These may include outliers in the data, incorrect assumptions about the relationship between variables, or insufficient data points to accurately represent the population. It is important to identify and address these causes in order to improve the accuracy of the model.

How can "Error in declination of linear regression" be reduced?

There are several ways to reduce the error in declination of linear regression. One method is to increase the amount of data used to build the model, which can help to account for variation and minimize outliers. Additionally, making sure that the assumptions of linear regression are met and selecting the appropriate variables for the model can also help to reduce error.

Why is "Error in declination of linear regression" important?

"Error in declination of linear regression" is important because it provides insight into the accuracy and reliability of a linear regression model. A high error can indicate that the model may not be a good fit for the data and should be further evaluated or adjusted. It can also help to identify areas for improvement in the model and guide future research and analysis.

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