MINERAL PROCESSING WEB APPLICATIONS: STATISTICAL ANALYSES
By using these tools and documents on this website, you agree to the disclaimer and End User License Agreement section. |
Send large or blocked file: Some IT policies or email clients such as Microsoft Outlook may block large or executable file attachments. In other cases, some companies do not allow employees to use external USB storages but they can download files from online to local drives. Use this tool to upload your file and you will receive an email with the instructions for the recipients to download it. Plant trial data cleaner: Due to various reasons such as shutdowns, changes in feed, operating conditions or processing equipments, other concurrent trials and data recording; plant trial data require extensive cleaning prior to conducting statistical analyses. Watch a Youtube video example. Cumulative sums: Due to daily plant variations, time series plots do not often show the time at which a change has occurred. Instead, cumulative sums are used to identify changes in operating parameters with time. Watch a Youtube video example. Minimum number of tests: In order to statistically detect a difference D between two means using Student's t-tests, the number of tests or sample size for each condition needs to be above a minimum value. Watch a Youtube video example. t-tests: Student's two samples t-tests assuming equal variances quickly comparing parameters'values and/or KPI for two different trial conditions (e.g., Old Reagent vs. New Reagent). This tool can complete t-tests for up to ten parameters at once. It also calculates the statistical significance of the differences (for 1 and 2 tails), the confidence levels and confidence intervals for the provided alpha. Watch a Youtube video example here. Comparison of regression lines: Comparison of two trendlines such as recovery versus feed grade or concentrate grade versus feed grade for two different conditions (e.g., Old Reagent vs. New Reagent). This statistical method isolates the effect of the new condition (e.g., New Reagent) from that of another important parameter (e.g., feed grade). Up to ten (10) statistical comparisons of trendlines can be completed at once. Watch a Youtube video example here. 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,...,xn) It automatically calculates the confidence intervals and removes outliers based on the standard residuals limits specifed by the user (e.g., ±3) so they don't have to be manually removed 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) Watch a Youtube video example here. |
© Copyright metallurgical.app