Boost Accuracy by Weighting Variables in Google Sheets

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在此免费的指导 项目中,您将:

Understand the fundamentals of statistically weighting variables.

Conduct a factor analysis and build the framework for weighting the variables in Google Sheets.

Weight variables, visualize them, and report findings in Google Sheets.


Clock2 hours
Comment Dots英语(English)

By the end of this project, you will be able to confidently identify when weighting of variables is warranted. You will also be able to apply statistical weighting to variables to produce a valid representation of the data so that it can be mined for insights using any spreadsheet software. To do this, you will work in the free-to-use spreadsheet software Google Sheets. The terms “weighting” and “weighted” are most-commonly used in reference to grading. You have probably heard them used in the context of a course you may have taken. When we hear that something like a grade will be weighted, we often feel a wave of relief because it implies that there will be a balancing of the scores within the class and the hope rises within us that the weighting might boost our personal letter grade. Statistical weighting in data sets—especially survey data sets—works in much the same way. It seeks to balance data variables or factors so that the values in the data set accurately represent the population. When data is weighted appropriately it enhances our confidence that the insights we pull from a data set are actionable. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.


Some familiarity with spreadsheet software is helpful.


Survey MethodologyStatistical AnalysisWeighting VariablesData AnalysisInsight Mining



  1. Identify when variables should be weighted and how to apply statistical weighting.

  2. Identify use cases for statistical weighting of variables selected during a factor analysis.

  3. Access Google Sheets, import data, and select factors that will aid in finding insights.

  4. Conduct a factor analysis and create the framework for weighting the variables.

  5. Apply statistical weighting to variables, create a visualization, and report your findings.






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