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**Introduction **

**Regression Analysis** is the study of relationships between variables
- Going beyond categorical variables
- Model based relationship (linear and non-linear)
- Useful towards interpretation and prediction

**Examples :-**
- How do wages of employees get affected with experience, education, Promotions, etc.
- How does the current price of stock depends on its past values
- How does sales revenue get affected as a function advertising expenses, competitors advertisements etc.
- Relationships between speed and fuel efficiency of a car.
- How does the price of a house get affected by number of bedrooms, square footage, etc.

**Categorizations nomenclature and concepts**
- Linear versus non-linear
- Simple versus multiple
- Cross sectional versus time series
- Response (or Dependent) variables
- Explanatory (or Independent) variables
- Scatter plots and outliers
- Unequal variance
- Corelations (a quantitative indicator of linear relationship) and R square

**Fitting Lines**
- Role of descriptive and inferential statistics
- y = mx + c or y = bo + b1x
- Optimization to identify the best fit
- Inference on the individual parameters

- The test for the H0 :Î²0 = 0
- Inference for the overall model

- ANOVA of a different kind
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ReplyDeleteplz make a video on it

ReplyDelete...if it possible