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Statistics for AIML - Inferential Statistics - Overview Tutorial

In Inferential statistics, we make an inference from a sample about the population. The main aim of inferential statistics is to draw some conclusions from the sample and generalize them for the population data. E.g. we have to find the average salary of a data analyst across India. There are two options.

  1. The first option is to consider the data of data analysts across India and ask them their salaries and take an average.
  2. The second option is to take a sample of data analysts from the major IT cities in India and take their average and consider that for across India.

The first option is not possible as it is very difficult to collect all the data of data analysts across India. It is time-consuming as well as costly. So, to overcome this issue, we will look into the second option to collect a small sample of salaries of data analysts and take their average as India average. This is the inferential statistics where we make an inference from a sample about the population. 

In inferential statistics, we will discuss probability, distributions, and hypothesis testing.

Statistics for AIML

Statistics for AIML

  • Introduction
  • Data Visualization
    • Overview
  • Descriptive statistics
    • Overview
    • Calculate Z Score
    • Covariance and Covariance matrix
    • Covariance vs. Correlation
    • QQ-Plot
    • Central Limit Theorem
  • Inferential Statistics
    • Overview
    • Hypothesis Testing
    • Statistical Test and there types
    • Bias Variance Trade Off
  • Regression Metrics
    • Overview
    • Accuracy
    • PR Curve (Precision-Recall Curve)
    • AUC-ROC Curve
    • Different types of Sampling
    • Skewness
    • Kurtosis
    • Degree Of freedom
    • Different Types Of Probability Distribution
    • Outlier
    • Bayes Theorem
    • Probability

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