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Using Principal Component Analysis (PCA), we can reduce the dimensionality of our data, Choosing the Right Number of Principal Components

Now available: unit process level data Unit process level is the disaggregated inventory of inputs and outputs to the level used for modelling: all details of how each dataset has been established and its linked processes are included. It allows for flows to be manipulated to perform contribution or sensitivity analysis. Read more in the article below; or start the process right away by providing the necessary information to info@globalfeedlca.org (see website) https://lnkd.in/eEXgrkh9

indepth knowledge on methodologies for data collection

High quality analysis != Low quality data + complex&fancy analysis = High quality data + simple analysis

Do you monitor the yield of your process as a Quality Metric?   Did you know you can create a control chart for yield data? You absolutely can!   The trick is selecting the right control chart based on the data you’re collecting and properly calculating control limits!   To learn more about attribute data control, check out this youtube video!   https://lnkd.in/eF_fcFj   -Andy Your Guide to Certification

Reflection on the course SECI1143 Probability Statistical Data Analysis (Semester 2 23/24).

When selecting intervals for a frequency distribution, having too few intervals can result in excessive aggregation and loss of important details, while too many intervals can overwhelm the analysis with unnecessary detail. The optimal choice of intervals depends on the type of data, the number of observations, and the analyst’s experience.

A DFD ( Data Flow Diagram ) to ensure valid applicants are registered

Cleaning Data is as easy as CIRCI (pronounced "Sir-See"): Check data types Identify missing values Re-express categorical variables Check for duplicates Identify outliers "Clean data is power."

Upgrade Your Appraisal Game with DataMaster Cut your data verification time in half and improve the accuracy of your reports. Watch this video to see how DataMaster's features optimize your workflow for maximum efficiency. https://hubs.ly/Q02wL4Lp0