Data Science Course > Charts and Graphs

Cummulative Distribution in Data science

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The CDF stands for Cumulative Distribution Function. The likelihood that a random variable X would take a value less than or equal to x is defined as the Cumulative Distribution Function (CDF) of that random variable.

The likelihood that the variable will assume a value less than or equal to x is represented by the cumulative distribution function (cdf). F(x) = Pr[X \le x] = \alpha. For a continuous distribution, this can be expressed mathematically as.

There are three types of cumulative distributions:

  • Continuous
  • Discrete and
  • Geometric

A cumulative distribution function is an important tool in the Data Science field. It allows us to find the probability of a certain outcome.

  • A cumulative distribution function is a tool that helps us find the probability of a certain outcome
  • It allows for easy interpretation and visualization of data
  • It can be used to find what percentage of data falls within a given range
  • It can be used to compare distributions and see which one has the most data points

Statistics typically analyze data by using the mean and variance, but it is often used in conjunction with other functions such as the median, to provide a more holistic view of the data.

This cumulative distribution can be used to model the success rates for different types of experiments. This can be done by calculating it from the sample data and then using it to predict whether there will be more successes or failures in future experiments.

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Course Content

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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
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  2. Mean and Percentiles in Data Science
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  3. Quartiles in Data Science
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  4. Measures of Variability in Data Science
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  5. Variance and Standard Deviation in Data Science
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  6. Application of Standard Deviation in Data Science
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  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
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  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. 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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  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
    5m 41s
  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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