This document provides an overview of regression models and their basic concepts. It defines regression as analyzing previous data trends to predict future outcomes. Regression differs from correlation in that it implies a causal relationship between an independent variable and dependent variable, rather than just a bidirectional relationship. An example is given of a regression equation modeling the relationship between education level and salary, with education as the independent variable and salary as the dependent variable. The document concludes with a summary of the key points covered.

1. Regression Model:
Basic concepts
Dr. Rajeev Kumar
M.S.W.,(TISS, Mumbai), M.Phil., (CIP, Ranchi)
UGC-JRF., Ph.D. (IIT Kharagpur)
©Dr. Rajeev Kumar 2020 1
12/2/202
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2. Have you come across such news??
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3. What does it call?
• This is called prediction
or forecasting.
• To predict some event in
future, we need to apply
regression analysis.
• We call it regression,
because to predict future
outcome, we have to
analyze the previous data
trend
• Regression means (going
back).©Dr. Rajeev Kumar 2020 312/2/2020

4. Lets revise the concept of
correlation
• What is correlation?
• This is bidirectional relation
between two variables
• X↔Y
• Its value range from -1 to +1
• If one variable (X) is increasing or
decreasing but other (Y) is
constant
• Or both (X) and (Y) are constant,
then there is Zero correlation.
• Correlation is not causation
©Dr. Rajeev Kumar 2020 412/2/2020

5. Lets understand bidirectional
more clearly
What is bidirectional?
Bidirectional means two directional.
X (age) Y(height)
It means, up to a certain age (18-20 years) when age increase,
height also increase. Both age and height are increasing. This is
the example of positive correlation.
X (more time in watching TV) Y(less time in study)
More you watch TV, less you study. X increase then Y decrease.
There is negative correlation.
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6. What is causation? And How it is
different from correlation
Correlation is not causation.
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7. We will have the significant positive correlation
between ice-cream eating and sun burn. But this is
spurious relation. It may have statistical significance
but no practical significance. Ice-cream eating is not
causing sun-burn.
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8. Difference between correlation and
regression
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9. More clearly differentiating
correlation and regression
Correlation
• Both (X) and (Y) move
together. One increase other
also increase or decrease.
• There is bidirectional relation
• (X) ↔ (Y)
• There is not causation between
X and Y
• There are no independent and
dependent variables in
correlation
Regression
• Here (X) affect (Y)
• There is causal relation
between (X) and (Y)
• There is uni-directional
relation between X
(Independent variable) and
Y (Dependent variable)
(X) → (Y)
©Dr. Rajeev Kumar 2020 912/2/2020

10. Lets understand independent and
dependent variables
The variable pose impact on other is called independent
variable. The variable who is affected in called dependent
variable
Here cigarette smoking = independent variable
Cancer = dependent variable
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11. We can write the same sentence in different ways
• Cancer is the outcome of cigarette smoking
• Cancer is the impact of cigarette smoking
• Cigarette smoking predicts the occurrence of cancer
• There is effect of cigarette smoking on chance of cancer
development.
• Habit of cigarette smoking explains the prevalence of
cancer in population
• Cigarette smoking is determinant of cancer occurrence.
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12. It means there are different names of independent and dependent
variables.
Independent variable
1. Predictor variable,
2. Determinant variable,
3. Impacting variable,
4. Explanatory variable
Dependent variable
1. Outcome variable,
2. Explained variable
3. Targeted variable
4. Predicted variable
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13. The relation between correlation
and regression
Calculate
correlation
Significant
correlation
Do
regression
analysis
Either significant
regression
Not significant regression
Not
significant
No need of regression
analysis
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14. Example of regression analysis
• Here we will calculate the effect of education on
salary (wage)
• We will try to understand the regression equation
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35. Express the variables in equation
• Dependent variable (Y)= cancer
• Independent variable (X)= Cigarette smoking
• C= Residuals (other factor)
Y(cancer) = β0 (No cigarette smoking) +β1 (Cigarette smoking)+ C
C= other factors such as genetic predisposition, toxic environment
etc.,
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36. What does C (residuals) means?
Y(cancer) = β0 (No cigarette smoking) +β1 (Cigarette smoking)+ C
C= other factors such as genetic predisposition, toxic
environment, passive smoking, exposed to fumes and pollution
etc.,
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37. Summary of the session
• We learnt the applicability of regression in daily life
• The differentiation between correlation and regression
• The relation between correlation and regression.
• The types of variables (independent and dependent)
• Practical example of regression and expression in the equation.
• In the next class, we will discuss about the types of regression
models
• Identifying the variables and expression in equation.
• We will learn about interpreting the results of regression through
published results in Journal articles.
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38. Thanks for your kind attention and
keep learning
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