MULTIPLE REGRESSION:

Multiple linear regression analysis to model one predicted or dependent variable as a function of many predictors or independent variables: Y = f(X₁,X₂,X₃,...,x_n) It automatically calculates the confidence intervals and removes outliers based on the standard residuals limits specified by the user (e.g., ±3) so they don't have to be manually removes one by one. It also accounts for categorical predictors such as "day shift / night shift" or "trial ON / trial OFF", for examples, without the need to transform them to zeroes and ones.
Example:
Recovery = f(feed grade, throughput, particles size, reagent dosages, pH, trial ON)

The cleaned input data Excel file should have the parameters or variables'names in the top cell of each column at the top row (e.g., date/timestamp, throughput, feed grade, recovery, etc..).
The parameters' values are in the rows below the parameters'names. See example in table below.

Date	Mill availability	Throughput	Feed grade	Concentrate grade	Tailing grade
1/01/2022	92%	1200	1.21	25.4	0.150
2/01/2022	95%	1220	1.18	26.1	0.145
...	...	...	...	...	...
31/07/2022	97%	1180	1.19	14.8	0.147

To use:
✅Choose the cleaned Excel (.xlsx) data file and click on "Upload to a table".
✅Select the predicted variable or parameter to model (e.g., recovery).
✅Select the predictors or independent variables (e.g., feed grade, throughput, mill power, etc...).
✅Select if there is a categorical predictor or dependent variable (e.g., "Trial" or "Shift") and provide the category whose effects are to be assessed (e.g., "ON" for "trial ON" or "Day" for "day shift").
✅Provide the confidence level (e.g. 90 to calculate the 90 percent confidence interval).
✅Enter the absolute value of standard residuals limits (e.g., use 3 to automatically remove outliers with standards residuals below -3 or above +3).
✅Click on "Calculate" to run the regression model.

Watch a Youtube video example here.

large files may take time to upload depending on your coputer resources, please wait.

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