Data Science Basic | Introduction to Statistics by Diljeet Singh | Learn Smarter
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Knowledge
Introduction to Statistics

Statistics plays a crucial role in understanding and interpreting data. It covers descriptive and inferential statistics, measures of central tendency and dispersion, probability, distributions, and hypothesis testing, all essential for data science applications.

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Sections

  • 1

    Descriptive Vs. Inferential Statistics

    This section outlines the distinctions between descriptive statistics and inferential statistics, highlighting their purposes in data analysis.

    Learning Practice

  • 2

    Measures Of Central Tendency

    This section covers the three primary measures of central tendency: mean, median, and mode, essential for summarizing data.

    Learning Practice

  • 2.1

    Mean (Average)

    The mean, or average, is a fundamental measure of central tendency, calculated by summing all values and dividing by the count of those values.

    Learning Practice

  • 2.2

    Median (Middle Value)

    This section introduces the concept of the median, defining it as the middle value in a dataset and outlining its significance in statistical analysis.

    Learning Practice

  • 2.3

    Mode (Most Frequent Value)

    The mode is the value that appears most frequently in a dataset.

    Learning Practice

  • 3

    Measures Of Dispersion

    Measures of dispersion provide insights into the variability of data points within a dataset.

    Learning Practice

  • 3.1

    Variance

    Variance measures the degree to which data points differ from the mean of their dataset.

    Learning Practice

  • 3.2

    Standard Deviation

    Standard deviation quantifies the amount of variation or dispersion in a set of values, serving as a crucial measure in statistics.

    Learning Practice

  • 3.3

    Range

    This section explains the concept of range in statistics as a measure of dispersion within a dataset.

    Learning Practice

  • 4

    Introduction To Probability

    This section introduces probability as a measure of the chance of an event occurring, covering its range, formula, and an example with a fair die.

    Learning Practice

  • 5

    Common Distributions

    This section covers the various common distributions used in statistics, including normal, binomial, and Poisson distributions.

    Learning Practice

  • 5.1

    Normal Distribution

    The normal distribution is a fundamental concept in statistics characterized by its symmetric, bell-shaped curve, and is pivotal for data interpretation.

    Learning Practice

  • 5.2

    Binomial Distribution

    The binomial distribution models the number of successes in a fixed number of independent trials, each with the same probability of success.

    Learning Practice

  • 5.3

    Poisson Distribution

    The Poisson distribution measures the probability of a number of events occurring within a fixed interval of time or space under certain conditions.

    Learning Practice

  • 6

    Introduction To Hypothesis Testing

    Hypothesis testing is a statistical method used to determine the validity of an assumption about a population based on sample data.

    Learning Practice

  • 6.1

    Null Hypothesis (H₀)

    The Null Hypothesis (H₀) is a foundational concept in hypothesis testing, stating that there is no effect or difference in a population.

    Learning Practice

  • 6.2

    Alternative Hypothesis (H₁)

    The Alternative Hypothesis (H₁) proposes that there is a significant effect or difference in a population that a statistical test is examining.

    Learning Practice

  • 6.3

    P-Value

    The p-value is a crucial statistic that helps determine the significance of results in hypothesis testing.

    Learning Practice

References

Chapter 8_ Introduction to Statistics.pdf

Class Notes

Memorization

What we have learnt

  • Statistics helps summarize ...
  • Central tendency and disper...
  • Probability and distributio...

Final Test

Revision Tests