Data Science Course > Measures of Central Tendency and Variability

Measures of Central Tendency for Grouped Data in Data Science

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When does the mean serve as the most accurate indicator of central tendency? When your data distribution is continuous and symmetrical, such as when your data is normally distributed, the mean is usually the best measure of central tendency to utilise.

The mean, median, mode, and midrange are the four measurements of central tendency. The arithmetic mean of the highest and minimum values in a data collection is the mid-range or mid-extreme of a set of statistical data values.

Central Tendency Measures are a summary measure that seeks to summarise an entire collection of data with a single value that indicates the middle or centre of its distribution. The mean, median, and mode are the three primary metrics of central tendency.

The leading measures of central tendency for grouped data are:

  • Mean The average value of a group.
  • Median: The middle value in a group.
  • Mode: The most frequently occurring value in a group.
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Central tendency is a measure of the center of a set of data. The most common measures are the mean, median, and mode.

  • Mean: The arithmetic average is calculated by adding together all the values in a data set and dividing by the number of values in that set. This can be expressed as
  • Median: The median is found by arranging all the values in a data set in order from smallest to largest and finding the value that divides them into two halves; if there are an even number of values, then the median will be one less than their mean. This can be expressed as
  • Mode: The mode is found by arranging all the values in a data set from smallest to largest and finding which value appears most frequently.

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  2. How to use LearnVern
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  1. Introduction to Data Measurement in Data Science
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  2. Types of Variables in Data Science
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  3. Levels of Data Measurement in Data Science
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  1. Charts and Graphs in Data Science
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  2. Frequency Distribution in Data Science
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  3. Bar Graphs and Pie Charts in Data Science
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  4. Summarizing Quantitative Data in Data Science
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  5. Cummulative Distribution in Data science
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  6. Histogram and Scatter Plot in Data Science
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  7. Cross Tabulation in Data Science
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  1. Measures of Central Tendency and Variability in Data Science
    6m 51s
  2. Mean and Percentiles in Data Science
    5m 32s
  3. Quartiles in Data Science
    2m 38s
  4. Measures of Variability in Data Science
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  5. Variance and Standard Deviation in Data Science
    7m 30s
  6. Application of Standard Deviation in Data Science
    5m 40s
  7. Z-score and Co-efficient of Variation in Data Science
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  8. Measures of Central Tendency for Grouped Data in Data Science
    3m 59s
  9. Standard Deviation and Variance for Grouped Data in Data Science
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  1. Skewness in Data Science
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  2. Kurtosis in Data Science
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  3. Box and Whisker Plots in Data Science
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  1. Hypothesis Testing in Data Science
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  2. Statistical Hypothesis in Data Science
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  3. Errors in Statistical Hypothesis in Data Science
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  4. Statistical Inference for Single Population in Data Science
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  5. Testing Example in Data Science
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  6. Statistical Inference with Two Population in Data Science
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  1. R Programming in Data Science
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  2. Data Types in R in Data Science
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  3. Understanding R Console and R Studio in Data Science
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  1. Analysis of Variance and Annova in Data Science
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  2. Assumption and Variation in One-Way ANOVA in Data Science
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  3. Two way ANOVA in Data Science
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  4. Summary of ANOVA in Data Science
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  6. One way ANOVA Example in Data Science
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  7. Two way ANOVA Example in Data Science
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  1. Understanding Datamining in Data Science
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  1. Introduction to Simple Linear Regression and Correlation in Data Science
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  2. Least Square and Residual Analysis in Data Science
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  4. Hypothesis and Co-Efficient of Determination in Data Science
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  5. Regression Example - Creating Model in Data Science
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  6. Residual Analaysis of Model example in Data Science
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  7. Residual Analysis of Model Part 2 in Data Science
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  8. Residual Analysis of Model Part 3 in Data Science
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  1. Understanding Clustering in Data Science
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  3. Example of Hierarchial Clustering in Data Science
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  4. K Means Clustering in Data Science
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  5. Example of K Means Clustering in Data Science
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  1. Theoretical Concepts of Dashboard
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  2. Creating Pivots and Preparing Charts
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  3. Slicers in Dashboard
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  4. Preparing Final Dashboard
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  5. Presenting Dashboard
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