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MANAGERIAL ECONOMICS
PRESENTATION REPORT SUBMITTED BY:-
Avinash Nair P-59
Prakash Panji P-23
Swati Khandelwal P-36
Rita Malhan P-49
Prahlad Walve P-56
Under the guidance of:-
Prof. Neelema Shastri
Dr. V.N. BEDEKAR COLLEGE OF MANAGEMENT STUDIES
Chendani Bunder Road, Thane 400601
SIGN:-
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ACKNOWLEDGEMENT
We are elated to present this topic and would like to take this
opportunity to express our sincere thanks to all, who by their direct or
indirect contribution have helped us make it possible.
We thank Prof. Neelima Shastri our project guide for her assistance.
Without her constant support and motivation this project would not have
been possible. We also thank her for giving us such a presentation topic
that is close to our heart as well as that would lead to our personal
development.
OBJECTIVES
The Objective of our presentation was to understand the
fundamentals of multiple regression analysis and its applications. The
report has not only helped us to understand comprehend regression
analysis but also theoretically but also understand its practical
applications.We also learnt the techniques wherein multipleregression analysis is used as a strategic business tool for decision
making.
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Why Learn Multiple Regression Analysis
Explain model building using multiple regression analysis:
To understand the model building process which is the
elementary
Apply multiple regression analysis to business decision-making
situations
Analyze and interpret the computer output for a multiple
regression model
Test the significance of the independent variables in a multiple
regression model
Recognize potential problems in multiple regression analysis
and take steps to correct the problems
Incorporate qualitative variables into the regression model by
using dummy variables
Use variable transformations to model nonlinear relationships
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An Introduction to Multiple Regression
Definition
Regression analysis applied to equation with two or more
independent variables
Multiple regression is a technique that allows additional factors
to enter the analysis separately so that the effect of each can be
estimated. It is valuable for quantifying the impact of various
simultaneous influences upon a single dependent variable. Further,
because of omitted variables bias with simple regression, multiple
regression is often essential even when the investigator is only
interested in the effects of one of the independent variables.
y Example
Consider the GDP Of any economy it is dependent on various
factors like growth rate, inflation rate, unemployment rate
and so on. A Simple regression analysis would fail to predict
the impact of other variables, this however is considered in a
multiple regression analysis. The future values can be
forecasted considering all the parameters that would tend toaffect the dependent variable.
Steps for Multiple Equation Regression Model
Select the dependent variableSelection of the dependent variable relies on the need or the
main objective of the research.
Identify the potential independent variablesThis step involves recognizing all possible parameters that
could tend to affect the the dependent variables. This stepIs the foundation of the model all further decisions are based
on this step.
Gather Sample DataThe data relevant to our research must be collected and the
observations must be tabulated accordingly.
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Steps of Multiple Regression Analysis
y
Construct a regression modely Estimate the regression and interpret
y the result
y Conduct diagnostic analysis of the results
y Change the original regression model if necessary
y Make predictions
Business Statistics: A Decision -
Making Approach, 7e 2008
Prentice-Hall, Inc.
Chap 15-8
Multiple Regression Model
Two variable model
y
x1
x2
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St forM lti l Equation R ression Model
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