lesson
Cleaning Data: Dealing with Missing Data and Anomalies
Clean raw data sets by identifying, correcting, or removing transcription errors, trace values, and extreme outliers.
Updated 2 months ago · 3 views
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This lesson is part of a track
Public learning track
Data Presentation, Cleaning, and Outlier Analysis
8 lessons
Goal
Construct and interpret statistical diagrams, calculate frequency densities in histograms, identify outliers, and clean raw data.
8 lessons
0 of 8 done
- Categorising Data: Quantitative, Qualitative, Discrete, and ContinuousUp next
- Histograms: Frequency Density and Unequal Class WidthsNot started yet
- Estimating Frequencies and Probabilities from HistogramsNot started yet
- Cumulative Frequency Curves, Medians, and PercentilesNot started yet
- Outlier Detection and Box-and-Whisker PlotsNot started yet
- Comparing Data Distributions Using Box PlotsNot started yet
- Cleaning Data: Dealing with Missing Data and AnomaliesYou are here
- Critiquing Data Presentation TechniquesNot started yet
Learning standards covered
L4General Certificate of Education Advanced Level (UK A-Level) · Maths · 11th Grade[Recognise and interpret possible outliers in data sets and statistical diagrams] [Select or critique data presentation techniques in the context of a statistical problem] [Be able to clean data, including dealing with missing data, errors and outliers]
Open Global Standard:OGM.13.STA.2
OGM.13.STA.2