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https://github.com/iankitnegi/mbastatistics

Sharing my learnings from the Pre-MBA Statistics course by IIM-A. Explore my notes, data projects, and visualizations demonstrating the power of statistics in business.
https://github.com/iankitnegi/mbastatistics

iima mba statistics

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Sharing my learnings from the Pre-MBA Statistics course by IIM-A. Explore my notes, data projects, and visualizations demonstrating the power of statistics in business.

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README 

        
# Pre-MBA Statistics  

Welcome to my GitHub repository where I document my journey and insights from the Pre-MBA Statistics course by IIM-A. This space is dedicated to sharing my learning, projects, and practical applications of statistical concepts explored during the course. Dive in to find detailed notes, data analysis projects, and visualizations that showcase the power of statistics in the business world.      



## Numerical Data:  

Numerical data, as the name suggests, is data that consists of numbers. This type of data can be quantified and subjected to mathematical operations. It's typically divided into two types:  

- Discrete Data: Consists of countable numbers, like the number of students in a class. Examples: Number of cars in a parking lot, number of books on a shelf.

- Continuous Data: Consists of measurements that can take any value within a range. Examples: Height of students, temperature readings, time taken to complete a task.



Numerical data is fundamental in statistics and data analysis because it allows for precise calculations and insights. It can be represented using various statistical measures like mean, median, standard deviation, etc.



### Mean:  

In statistics and data analysis, the mean (also known as the average) is a measure of central tendency that represents the typical value in a dataset. It's calculated by summing up all the values in the dataset and then dividing by the number of values.    

#### Importance of Mean:

- Representation of Central Value: The mean gives you an idea of what a typical value in the dataset looks like.

- Basis for Other Measures: It's used in various statistical calculations and other measures like variance and standard deviation.

#### Considerations:

- Affected by Outliers: The mean can be heavily influenced by outliers (extremely high or low values).

- Best for Symmetrical Distributions: It's most accurate for datasets with a symmetrical distribution.



### Median:  

The median is a measure of central tendency that represents the middle value in a dataset when it is ordered from least to greatest. It divides the dataset into two halves, with 50% of the values being less than or equal to the median and 50% being greater than or equal to the median.  

#### Importance of Median:

- Resistant to Outliers: Unlike the mean, the median is not affected by extremely high or low values, making it a better measure of central tendency for skewed distributions.

- Useful for Skewed Data: In datasets that are not symmetrically distributed, the median provides a better central location of the data.



### Percentiles:  

A percentile indicates the value below which a given percentage of observations in a dataset falls. For example, the 20th percentile is the value below which 20% of the observations may be found.  

### How to Calculate Percentiles:  

- Sort the Data: Arrange your data in ascending order.  

- Determine the Percentile Rank: Use the formula: 𝑃 = (𝑅−1/𝑁)×100  

  where: 𝑃 is the percentile rank.    

         𝑅 is the rank of the value in the sorted dataset.    

         𝑁 is the total number of values.    



### Types of Data:  

Source 1: [Link](https://www.turing.com/kb/statistical-data-types)  Source 2: [Link](https://www.mymarketresearchmethods.com/types-of-data-nominal-ordinal-interval-ratio)  



## Random Experiment:  

Random experiment is a process or activity that leads to one of several possible outcomes, where the outcome cannot be predicted with certainty beforehand. Each time the experiment is conducted, it may result in a different outcome, even if the conditions are the same.



Key Characteristics:

- Uncertainty: The outcome cannot be known in advance.

- Repeatability: The experiment can be repeated under the same conditions.

- Outcomes: There are multiple possible outcomes.

- Randomness: Each outcome is determined by chance.



Examples of Random Experiments:

- Flipping a Coin: Each flip can result in either heads or tails, and the result is unpredictable.

- Rolling a Dice: Each roll can result in one of six faces showing up, and the result is random.

- Drawing a Card: Drawing a card from a shuffled deck has multiple possible outcomes, and which card is drawn is uncertain.



### Sample Space:  

In statistics and probability theory, the sample space is the set of all possible outcomes of a random experiment. It provides a comprehensive list of everything that could happen when the experiment is performed.



Key Points:

- Complete Set: It includes every possible outcome.

- Notation: Often denoted by the symbol 𝑆 or Ω

- Mutually Exclusive Outcomes: Each outcome in the sample space is distinct from the others.



Examples of Sample Spaces:

- Flipping a Coin: The sample space is 𝑆 = {Heads, Tails}

- Rolling a Dice: The sample space is 𝑆 = {1,2,3,4,5,6}

- Drawing a Card from a Deck: The sample space consists of all 52 cards, 𝑆 = {Ace of Hearts, 2 of Hearts,…,King of Spades}



Importance in Probability:

The sample space is crucial for defining events, which are subsets of the sample space. For example, in rolling a dice, an event could be rolling an even number, which is a subset of the sample space: {2, 4, 6}



### Events:  

An event is any outcome or a specific set of outcomes from a random experiment. Events are fundamental in the study of probability because they form the basis for determining the likelihood of different outcomes.  



Key Points:

- Definition: An event is a subset of the sample space. The sample space includes all possible outcomes, while an event consists of one or more of these outcomes.

- Simple Event: An event with a single outcome.

- Compound Event: An event with two or more outcomes.



Examples of Events:

- Flipping a Coin:    

  Simple Event: Getting Heads.  

  Compound Event: Getting either Heads or Tails (which is also the entire sample space).  

- Rolling a Die:    

  Simple Event: Rolling a 3.      

  Compound Event: Rolling an even number (2, 4, 6).