Log In

Explore the World's Best Ideas

Join today and uncover 100+ curated journeys from 50+ topics. Unlock access to our mobile app with extensive features.

1. Linearity

A Linear Regression is Linear because the Equation is Linear.
To verify the Linearity between Independent and Dependent Variable apply Scatter plot.
If the Data points form a pattern that looks like a Straight Line then Linear Regression is Suitable.

2

40 reads

MORE IDEAS ON THIS

Autocorrelation Detection

To detect autocorrelation

Plot all points and check for patterns or
Use Durbin - Watson test.

There is no remedy for Autocorrelation. Instead of linear regression, we can use

Autoregressive Models.
Moving Average Models.

2

17 reads

What is Ordinary Least Square Method?

Ordinary least squares, or linear least squares, estimates the parameters in a regression model by minimizing the sum of the squared residuals.

2

93 reads

Heteroscedasticity

In order to prevent Heteroscedasticity, we need to

Look for Omitted Variable Bias.
Look for Outliers.
Apply log Transformation.

2

21 reads

Non-Linear Pattern

If the pattern doesn't looks like a Straight Line, then we need to apply

A Non Linear Regression or
Exponential Transformation or
Log Transformation.

2

37 reads

2. No Endogeneity

Technically, Endogeneity occurs when a predictor variable (x) in a regression model is correlated with the error term (e) in the model.
This usually occurs due to the Omitted Variable Bias (when we forget to include a releva...

2

26 reads

3. Normality of Error Terms

We need to consider that our Error Term is normally distributed.
We only need Normal Distribution while making Statistical Inferences.
T-tests and F-tests work because we have assumed normality.
If Error Term isn't normally distribute...

2

25 reads

4. Zero Mean of Error Terms

If the Mean of the Error Terms is not expected to be zero then the line is not the Best fitting one.
Having an Intercept Solves the problem.

2

24 reads

5. Homoscedasticity

Homoscedasticity means to have “Equal Variance”. The error terms should have equal variance with one another.
When the error terms don’t have “Equal Variance”, then Heteroscedasticity happens.

2

21 reads

Assumptions for OLS

OLS Assumptions are the Conditions that we need to consider them before performing Regression Analysis.

Some OLS Assumptions are:

Linearity
No Endogeneity
Normality
Zero Mean of Error Terms
Homoscedasticity

2

63 reads

6. No Autocorrelation

Autocorrelation is a mathematical representation of the degree of similarity between a given time series and a lagged version of itself over successive time intervals.
It is similar to the Correlation between two different Time Series but Autocorrela...

2

19 reads

Fixing Multicollinearity

To fix Multicollinearity:

If the Dataset is small, we can drop one independent variable. Or we can Transform them into One Variable (Eg. Average of two Variables)
If the Dataset is large, we will use Ridge and Lasso Regression.

2

22 reads

7. No Multicollinearity

We observe Multicollinearity when two or more Independent Variables in a model are “Highly Correlated”.
Correlation could be Positive (or) Negative.
This will lead result in less reliable predictions.
Multicollineari...

2

19 reads

CURATED FROM

OLS (Ordinary Least Square) Assumptions

13 ideas

·

427 reads

CURATED BY

Le Saì De vénu

@v_for_venusai

Enthusiastic with a Splash of craziness

These are some of the Assumptions to be pondered while Applying Ordinary Least Square Method and Performing Regression Analysis.

“

More like this

Polynomial regression

Polynomials are algebraic expressions consisting of variables and coefficients.
Polynomial regressions are the most used in machine learning.

Polynomial regression is a unique case of linear regression:

You fit a polynomial equ...

Data Reading On Statistical Features

A typical data set diagram (box plot) carries a lot of information.

If it is short, it means the data points are similar, but if it is tall, it implies there is a lot of range and variance.
A median (the line in the middle of a dataset graph) provides a more accurate reading...

Unlocking your potential

Learning doesn’t happen in a straight line. Learning looks more like a broken staircase than a straight line.

Learning tough skills doesn’t happen over the course of days or weeks or months. Years is the right timeframe for most things.

Supercharge your mind with one idea per day

Enter your email and spend 1 minute every day to learn something new.

Email

I agree to receive email updates