Data Science Classroom Series – 22/Oct/2021

Understanding the Difference Between Sample and Population

  • Overview Preview
  • Populations are hard to observe Preview
  • Samples are less time consuming and less costly
  • Lets assume we walk to university canteen to collect the details from students who will be the sample Preview
  • Sample has to have two important characteristics
    • Randomness
    • Representativeness Preview
  • Students from canteen violate both randomness and representativeness.
  • In this case how to draw a sample that is both random and representative, for this we need to have an access to university student database and then we choose sample out of it which are random & reprsentative.

10000 feet overview of descriptive and inferential statistics

  • Descriptive Statistics

    • 75% of the website traffic on flipkart.com is interested in mobiles
    • (We have some data & we conducted statistics on that => concluded)
  • Inferential Statistics

    • According to survey which we conducted the average rent in hyderabad for 2 BHK will be 20000 by 2025
    • (This study has taken a sample and predicted the observation for the whole population)

Descriptive Statistics Fundamentals

  • When we deal with statistics we deal with data
  • Data is classified by
    • Types of Data
      • Categorical:
        • This represents groups or categories
        • Examples: Car Brands (BMW, Audi, Mercedes), Answer to yes/no questions
      • Numerical: This represents numbers and is divided into two categories
        • Discrete:
          • This generally represent the data that can be usually counted in a finite matter
          • Example: Number of cars, SAT score
        • Continuous:
          • This generally represents data which is not possible to count which we rather measure
          • Example: Weight, height
    • Measurement Levels
      • Qualitative
        • Nominal:
          • Represents categories that cannot be put in any order
          • Examples: Four seasons, Car brands
        • Ordinal:
          • Represents categories that cna be ordered
          • Example: rating your meal (disgusting, unappetizing, neutral, tasty and delicious)
      • Quantitative: Both represent numbers
        • intervals:
          • dont have true zeros
          • Example: Degrees celsius and Fahrenheit
        • ratios:
          • Have true zero’s
          • Example: lenght, degrees kelvin Preview

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