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BIRLA INSTITUTE OF TECHNOLOGY AND

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End Semester Report

Submitted in partial fulfilment of the requirements of

Course Name : PHA G616 Pharmaceutical Administration &
Management (PAM)

Title: Forecasting by Linear Regression Analysis

Report Submitted by: Suvarna Kale.
ID: 2017H1460152H.

Instructor -in -charge: Dr. Akash Chaurasiya.

Evaluator’s remarks:

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Contents

Course Name : PHA G616 Pharmaceutical Administration ; ………………………….. ………. 1
Management (PAM) ………………………….. ………………………….. ………………………….. ……….. 1
Evaluator’s remarks: ………………………….. ………………………….. ………………………….. ……………… 1
Forecasting ………………………….. ………………………….. ………………………….. ………………………….. . 3
Types of Forecasting ………………………….. ………………………….. ………………………….. …………… 3
• Short range ………………………….. ………………………….. ………………………….. ……………… 3
• Long range ………………………….. ………………………….. ………………………….. ………………. 3
Forecasting Process: ………………………….. ………………………….. ………………………….. …………… 3
Methods of Forecasting: ………………………….. ………………………….. ………………………….. ………… 4
Linear Regression Analysis ………………………….. ………………………….. ………………………….. ……. 5
Variables role: ………………………….. ………………………….. ………………………….. …………………… 5
Dependent variable: ………………………….. ………………………….. ………………………….. ………… 5
Independent Variable: ………………………….. ………………………….. ………………………….. ……… 5
? Linear Regression Function ………………………….. ………………………….. ………………………….. 5
REGRESSION MODELS ………………………….. ………………………….. ………………………….. ……… 6
Case Study: A Linear Regression Approach to Prediction of Stock Market Trading Volume. 6
ABSTRACT ………………………….. ………………………….. ………………………….. …………………………. 6
INTRODUCTION ………………………….. ………………………….. ………………………….. …………………. 6
• Scatter plot ………………………….. ………………………….. ………………………….. …………………. 7
Results after applying Regression Formula: ………………………….. ………………………….. ………….. 8
Conclusion ………………………….. ………………………….. ………………………….. ………………………….. . 9
References: ………………………….. ………………………….. ………………………….. ………………………….. . 9

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? Forecasting is define as the process of making predictions of the future based o n
past and present data and mo stly by analysis of trends ( recently observed trend data).
? Most of the Business Enterprises uses Forecasting as an integral part of business.

Fig 1. Forecasting.
? Types of Forecasting

? Forecasting may be:
• Short range : Forecasting for an hour , day, week or month.
• Long range : Forecasting for next six months, next year, the next five years, or the life
of product or service.
? Forecasting Process :

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There are various methods and Techniques b y which the forecasting is done depending on the
condition:

? Methods of Forecasting:

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Linear Regression Analysis

• Linear regression : It is a mathematical regression techniqu e which r elates one
variable ie.independ ent variable to that of another, the dependent variable which gives
a straight line.
• General form of a linear equation is as following:

y = a+bx

where,
y = the dependent variable
a = the intercept
b = slope of the line
x = the independent variable.

Fig.Linear Regression Graph.

? Variables role:

? Dependent variable:
o This is the variable whose values we want to explain, estimate or forecast .
o Its value is dependent on something else .
o It is denoted by y.

? Independent Variable :
o This is the variable that explains the other one/ to predict the dependent variable
o Its value are independent
o It is denoted by x.

Equations:

? Linear Regression Function :
y=a + bx

? Slope(b) of the Regression line:
b=r* STD deviation of y/std deviation of x

? Y-intercept(a) of Regression line:
a=mean of y – b * mean of x

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W hen there is use of more than one independent variable in regression analysis then it
is called as M ultiple Regression analysis.

? REGRESSION MODELS

? Stock M arketing C ase Study: Prediction of Stock Market Trading
Volume by Linear Regression Approach .

? ABSTRACT :

• Stock market is the business were people invest their money for gaining profit, but
there is risk of fluctuations as per the market. Predicting the daily stock is a very
much great challenge for the investors and stockholder. So Forecasting by Linear
regression helps the investor and the stockholders to invest the s hares very safely and
confident ly.

• In this Paper by applying the above method we get similar and good performance in
compare to the real volume so that the investor can invest confidently based on it.

? INTRODUCTION

• Due to financial benefits and its low risk is a growing topic in research, predicting the
stock market due to its importance and popularity among the masses and also small
and large compa nies is very much important.

• There is flucation effect on the behaviour of the people in terms of investment, capital
Simple
Linear
Nonlinear
Multiple
Linear
Nonlinear

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savings, stock price or the decrease or increase risk.
• So in general there are various methods to predict the stock market behaviour for
buying the share at cheapest price and selling it at highest price. So , it is very useful to
choose an appropriate method for forecasting.

? LINEAR REGRESSION

• Regression is used to predict a numerical value.
• The result can be ext ended by adding new information where the Target values have
• Establishes values between Predictor and T arget values.

? Scatter Plot of :

(Average) parameter is the mean of the prices of Open, Low, High and close to predict
The volume.

? There is Relationship between :

? Trading volume (Volume) as the dependent variable.

? Average price per share (Average) as the independent variable.

? The R-squared 0.358 simply determines that the 2 variables used for
determining the orientation of trend line was 35.5%.

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? The relationship between the independent parameters:

Open Close Low High
Low 0.975 0.986 – –
High 0.989 0.976 0.985 –
Volume -0.383 -.0429 -0.425 -0.391

? Linear regression was applied to the data by using data a nalysis and the summary of
data is as follows:

? Regression value by applying the re gression analysis by using Regression
Statistics.

M ultiple R 0.599
R Square 0.358
Standard Error 285577
Observations 59

? R egression analysis: The Std erro r:

? It measures the dispersed or s catter of the observed value along the line of regression
for the value of X.
? Confidence and prediction interval can be calculated by using the STD error .
? The STD error is eq ual to 285577 which is the error between the predicted and real
value and is calculated by using the degree of freedom, sum and mean of square.
ANOVA df SS MS
Regression 1 3E+12 3E+12
Residual 57 5E+12 8E+10
Total 58 7E+12

? Values of coefficient obtained after linear regression:

Coefficient Std error
Intercept 4675513 697440
Average -106938 18953

? Results after applying Regression Formula:

• By obtaining t he values of slope , coefficients, error and intercept and then
applying the linear regression on data for predicting the trading volume which is
unknown parameter to t he real volume by using formula.

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? Following table represents the predicted volume and the real volume of the average price:

Date Avg Price Predicted Volume Real Volume
28/ 6/13 33.13 965675 1081200
27 /6/13 32.96 963498 801800
26/6/13 32.49 1019301 835100
25/6/13 32.34 1042679 196700
24 /6/13 32.87 985747 1017800
21 /6/13 32.94 964849 1196100
20 /6/13 33.11 955342 1156600
18 /6/13 33.53 921192 1794100
17 /6/13 33.52 919738 2512100
ss14 /6/13 33.52 918908 645000
7/6/13 35.88 788848 656100

? Conclusion :

• The predicted Trading volume is almost very similar to the real values.

• The similarity of about 61% was observed between the real and the predicted value
observed.

• So, The Linear Regression is one of the most valuable and useful technique for
prediction of stock market trading volume.

? References:

Khaze,Linear Regression: Approach To Prediction Of Stock Market Trading Volume:
A Case Study, International Journal Of Managing Value And Supp ly Chains
(IJMVSC) Vol.4, No. 3, September 2013.