R Square: It is the coefficient of determination Coefficient Of Determination Coefficient of determination, also known as R Squared determines the extent of the variance of the dependent variable which can be explained by the independent variable. -1 indicates a strong negative relationship.1 Indicates a strong positive relationship.The Correlation Coefficient is the value between -1 and 1. Submit a new Pull Request to the Android Beacon Library project, providing a link to your spreadsheet calculations.Multiple R: This calculation refers to the correlation coefficient, which measures the strength of a linear relationship between two variables. You may wish to compare if this customized formula predicts distance better than the default Nexus formula with the library before submitting the constants for inclusion in the library. Validate ResultsĬheck the predicted distances against the actual distances to see that the formula provides a reasonable fit for the device.

For the fourth column, calculate the predicted distance using the formula y=A*x^B+C. Make four new columns to form table 7: RSSI, Ratio, Actual Distance, Predicted Distance, and paste for existing values into the first three. To do this, subtract the actual distance from the predicted distance in table 5 above, and put this in table 6. In order to optimize distance estimates at 1 meter, we add an intercept variable C. The power regression assumes a zero intercept. For the fourth column, calculate the predicted distance using the formula y=A*x^B. Make four new columns to form table 5: RSSI, Ratio, Actual Distance, Predicted Distance, and paste for existing values into the first three. Just paste the two columns from the previous step into the form field, and it will output the A and B constants. While there are many tools that can be used to run a power regression, the online website here may be the easiest. This will align your dependent and independent varaibles for the power regression. Make two new columns and copy your ratio values into the first column and the distance values into the second column. Divide your measured RSSI at each distance by the iPhone 1m measured RSSI. Make a new column beside your measured RSSI values at various distances. Paste your measured RSSI values at various distances for the Android device into table 2.Paste your measured iPhone 1m RSSI average into table 1.You can see an example spreadsheet here that you can follow as a guide. Make a spreadsheetĬreate a new spreadsheet using Excel, Google Docs or similar. After taking distance measurements for a specific Android device, the next step is to run a power regression to get the A, B and C constants used in the y=A*x^B+C formula.

Power regression excel