Linear Regression Error on Excel

In summary, Linear Regression Error on Excel refers to the difference between the predicted values and the actual values in a linear regression model. This error is commonly calculated using the root mean square error (RMSE) or the mean absolute error (MAE) methods. Excel provides various functions and tools to help users identify and minimize these errors, such as the "Solver" add-in and the "Data Analysis" tool. By understanding the concept of linear regression error and utilizing Excel's features, users can improve the accuracy and reliability of their regression models.
  • #1
Calabi_Yau
35
1
This is for an experimental physics homework,I am using the latest version of MS Excel. I have a set of data, I perform linear regression on them and it gives me a line y=ax + b.

Given that both a and b have physical significance I would like to know how could I know the uncertainty associated with both a and b, so I could get meaningful results.

How do I do this? The statistics pack seems to give everything but these values.

Any help would be appreciated.
 
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  • #2
  • #3
Uncertainty with regards to coefficients of a model fit to data can be inferred from so called "Goodness of Fit" measures. There are many different ways to approach this however, since you are using Excel, the easiest for way for you is to have Excel display the R2 value. You can do this by either right clicking on your line, selecting Format Trendline and checking off the box next to "Display R-squared value on chart." If you are fitting the line then the same dialog box will have that option on the bottom. Interpretation of the value is fairly straight-forward, a quick Google search will gives lots of discussions.
 
  • #4
useful textbook

There is a useful textbook explaining things in an introductory manner. Have a look at: "Data Fitting and Uncertainty (A practical introduction to weighted least squares and beyond)", ISBN 978-3-8348-1022-9.
 
  • #5


As a scientist, it is important to accurately determine the uncertainty associated with your data in order to obtain meaningful results. In Excel, the uncertainty for a and b in linear regression can be calculated using the LINEST function. This function returns an array of values, with the first value being the slope (a) and the second value being the y-intercept (b). The third and fourth values in the array are the uncertainties for a and b, respectively.

To use the LINEST function, you will need to select the cells containing your data and the corresponding y-values. Then, in an empty cell, type "=LINEST(y-values, x-values, TRUE, TRUE)". This will return an array of values, with the third and fourth values being the uncertainties for a and b.

Additionally, you can also use the Analysis ToolPak in Excel to perform a regression analysis and obtain the uncertainties for a and b. To do this, go to the "Data" tab and click on "Data Analysis". Then, select "Regression" from the list of analysis tools and input your data. The resulting output will include the uncertainties for a and b.

It is important to note that the uncertainties calculated using these methods assume that the data follows a linear relationship and that there are no systematic errors present. If you suspect there may be systematic errors in your data, it is recommended to consult with your instructor or a statistician for further guidance on how to accurately determine the uncertainties for a and b.
 

Related to Linear Regression Error on Excel

What is Linear Regression Error on Excel?

Linear Regression Error on Excel refers to the amount of variation in a dataset that cannot be explained by the linear relationship between the independent and dependent variables. It is also known as the residual error or the difference between the observed and predicted values.

How is Linear Regression Error calculated on Excel?

In Excel, the Linear Regression Error can be calculated using the "LINEST" function, which returns the slope, intercept, and other statistical values. The error can be found by subtracting the predicted values from the actual values in the dataset.

What is the purpose of calculating Linear Regression Error on Excel?

The main purpose of calculating Linear Regression Error on Excel is to evaluate the accuracy of the linear regression model. It helps to identify the amount of variation that is not explained by the model and can be used to improve the model's performance.

How can I interpret the Linear Regression Error on Excel?

The Linear Regression Error on Excel can be interpreted as the amount of variation in the dependent variable that is not explained by the independent variable. A higher error value indicates a weaker linear relationship between the variables, while a lower error value indicates a stronger relationship.

Can Linear Regression Error be negative on Excel?

Yes, Linear Regression Error can be negative on Excel. This occurs when the predicted values are higher than the actual values, resulting in a negative difference. However, it is important to note that the overall error value should be interpreted in the context of the dataset and the specific research question.

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