Microsoft Excel – Data Analysis and Statistic Calculations

Course code: MSEXDS

At the course participants meet advanced data analysis methods. All standard statistical – analytic functions are discussed especially the Analysis ToolPack Add-Inn which is used for demanding statistical analyses. Listeners learn how to work with tools such as a descriptive statistics, a properties correlation, a values prediction and a work with time series. The course also focuses on clear interpretation of found results.

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Course dates

Starting date: Individual

Type: In-person/Virtual

Course duration: 2 days

Language: en/cz

Price without VAT: 250 EUR

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Individual In-person/Virtual 2 days en/cz 250 EUR Register
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Course description

At the course participants meet advanced data analysis methods. All standard statistical – analytic functions are discussed especially the Analysis ToolPack Add-Inn which is used for demanding statistical analyses. Listeners learn how to work with tools such as a descriptive statistics, a properties correlation, a values prediction and a work with time series. The course also focuses on clear interpretation of found results.

Course structure

Analysis of extensive data volumes by helping PivotTable (PT) and PivotTable Chart Report.

  • PT creation principles
  • Total functions in PT
  • Build-in functions for different data views in PT
  • Calculated fields and items
  • Data analysis and data understanding in PT

Basic data analysis

  • Frequency analysis
  • Histogram – frequency chart
  • Level characteristics (Mean, Median, Mode, Quantity)
  • Variable characteristics (Variance, Deviation)
  • Shape allocation characteristics (Kurtosis, Skewness)

Dependence analysis

  • Correlation
  • Regression analysis (appropriate regression model, comparison of two different alternatives, judgment of regression model quality, estimates based on chosen regression model
  • Graphic dependence analysis
  • Multi regression (more independent variables)

Time series analysis

  • Time series characteristics
  • Basic time series description (differences, growth speed – chain, indexes)
  • Time series modeling
  • Time series decomposition (trend, seasonal, cyclic, random item of time series)
  • Time series purge of seasonal component (moving average)
  • Estimates based on time series model
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