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BIRLA INSTITUTE OF TECHNOLOGY AND

SCIENCE, PILANI. Hyderabad campus.

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

been defined already.

• 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

Adjusted R Square 0.347

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:

? Farhad Soleimanian Gharehchopogh1, Tahmineh Haddadi Bonab2 And Seyyed Reza

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.

? http://www.businessdictionary.com/definition/forecasting.html.