The document discusses the importance of econometric models in the service industry, highlighting how they transform qualitative data into quantitative insights through economic theory and statistical methods. It covers linear regression as a foundational tool in econometrics, detailing its advantages and disadvantages, particularly in relation to its application within companies like Deloitte. A case study on Nike's stock prices illustrates practical applications of these econometric models in economic policymaking.

1. IMPORTANCE OF ECONOMETRIC
MODEL IN SERVICE INDUSTRY

2. SUBMITTED BY: SUBMITTED TO:
MADHUR CHHIBBER (BM-022135) DR. AAKASH
ASHUTOSH KUMAR (BM-022064)
MEGHA GANDHI (BM-022142)
KOMAL KUMARI (BM-022131)
MANYA GUPTA (BM-022105)
MANISH KUMAR SINGH (BM-022138)
JAGRITI GUPTA (BM-022121)
BUDHITYA DEV (BM-022071)

3. • INTRODUCTION
• ECONOMETRICMODELS
• LINEAR REGRESSION
• COMPANY PROFILE
• ADVANTAGESAND
DISADVANTAGES
• CASE STUDY
• CONCLUSION

4. Introduction:
 Econometrics turns qualitative data
into quantitative data.
 Econometrics uses economic theory,
mathematics, and statistical inference
to quantify economic phenomena. In
other words, it turns theoretical
economic models into useful tools for
economic policymaking.
 Econometrics deals with the
measurement of economic
relationships.
 The relationships of economic theories
are usually expressed in mathematical
forms and combined with empirical
economics.

5. ECONOMETRICS MODEL
 A set of equations, derived from the economics theory and
mathematical model, statistical tools i.e. regression.
 Information regarding observed variables and disturbances.
 Statement about the errors in the observed values of variables.
 Information on the probability distribution of disturbances.

6. LINEAR REGRESSION
 Linear regression is the starting point of econometric
analysis.
 The linear regression model has a dependent variable
that is a continuous variable, while the independent
variables can take any form (continuous, discrete, or
indicator variables).
 A simple linear regression model has only one
independent variable, while a multiple linear regression
model has two or more independent variables.

7. COMPANY PROFILE
 Is an international professional services network headquartered
in London, United Kingdom. Deloitte is the largest professional
services network by revenue and number of professionals in the
world and is considered one of the Big Four accounting
firms along with EY, KPMG and PricewaterhouseCoopers.
 Deloitte provides audit, consulting, financial advisory, risk
advisory, tax, and legal services with approximately 334,800
professionals globally. In FY 2021, the network earned revenues
of US$50.2 billion in aggregate. As of 2020, Deloitte is the third-
largest privately-owned company in the United States, according
to Forbes. The firm has sponsored a number of activities and
events including the 2012 Summer Olympics.

8. Advantages Disadvantages
Linear regression performs exceptionally well for linearly
separable data
The assumption of linearity between dependent and
independent variables
Easier to implement, interpret and efficient to train It is often quite prone to noise and overfitting
It handles overfitting pretty well using dimensionally
reduction techniques, regularization, and cross-validation
Linear regression is quite sensitive to outliers
One more advantage is the extrapolation beyond a
specific data set
It is prone to multicollinearity

9. CASE STUDY
 BELOW IS A TABLE OF NIKE’S CLOSING PRICES FOR 12
CONSECUTIVE DAYS OF TRADING (18/3/20 TO 2/4/20).
DAY (X) 0 1 2 3 4 5 6 7 8 9 10 11
CLOSING
PRICE (Y)
68.04 70.34 67.45 62.80 72.33 79.01 84.30 83.23 85.38 82.74 79.23 80.14

10. y = ax + b
a = 1.646958042 (Slope)
b = 67.1908974359 (y-int)
y = 1.65x + 67.19

12. Econometrics is all about
Considering Economic theory,
Collecting data for the variable of
economic theory and applying
statistical tool on the data while
testing some linear, some conclusion
that is helpful in the policy making.