Understanding the Difference Between Sample and Population
- Overview
- Populations are hard to observe
- 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
- Sample has to have two important characteristics
- Randomness
- Representativeness
- 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
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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
- Discrete:
- Categorical:
- 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)
- Nominal:
- Quantitative: Both represent numbers
- intervals:
- dont have true zeros
- Example: Degrees celsius and Fahrenheit
- ratios:
- Have true zero’s
- Example: lenght, degrees kelvin
- intervals:
- Qualitative
- Types of Data