AUTHOR: – SARITA YADAV/022, ITM, EMBA/15/KHARGHAR

INTRODUCTION
Siemens is a global powerhouse focusing on the areas of electrification, automation and digitalization. One of the world’s largest producers of energy-efficient, resource-saving technologies, Siemens is a leading supplier of systems for power generation and transmission as well as medical diagnosis. In infrastructure and industry solutions the company plays a pioneering role. Company’s stock traded in NSE is symbolized as ‘NSE: SIEMENS’.

OBJECTIVES:
To Calculate beta of SIEMENS and find its significance using regression analysis with NIFTY50.

DATA COLLECTION:
The closing price data of Nifty50 and SIEMENS was taken from www.nseindia.com for the time period 1st March 2019 to 28th Feb 2020.
From the available data, the closing rates of all the Fridays in the year was sorted to find out weekly returns for both Nifty as well as SIEMENS. Then the weekly returns was calculated for both by using formula –
Weekly Return =(C3-C2)/(C2*100)
where, C3 is present week closing price and C2 is the previous week closing price.
Once the data is calculated, weekly return column for NIFTY50 is considered as “X” variable and the weekly returns column for SIEMENS is considered as “Y” variable.
The Model and formulas used are:-
Y = a +bX
X ̅ =∑X/N
Y ̅=∑Y/N
x = X – X ̅
y = Y – Y ̅
b=∑xy/∑(x)^2
a= Y ̅- bX ̅
e = Y – Y ̅
Variance of error=(σe)^2 =∑e^2/N-K
S.E of b = √ ((σe)^2 /∑x^2)
t stat of b = b/ S.E of b
TSS=ESS+RSS
ESS = (b^2)*(∑x^2)
RSS = ∑e^2
R^2 = ESS/TSS
F = Mean ESS/Mean RSS


DATA ANALYSIS:
Using the Regression Add-on in Microsoft Excel Data Analytics tool we get following values:
R Square:- R2= 0.1925
a = -0.2395
b = 0.7449
N (Observations) = 50
F = 10.964698
Therefore, formulating below question:
Y = -0.2395 +0.7449 X

RESULT:-
The above equation tells us the relationship between “Y” and “X”, that is Demand and Price. If price raises by unit, demand raises. Positive sign says that, there is reverse relationship. Means if price rises demand rises, similarly if price falls then demand also falls.

CONCLUSION:-
Price is significant and overall model is significant but error will be 19%.