The document analyzes the future performance of PRAN AMCL LTD using a linear regression model. It finds that: 1) The regression equation indicates profit is influenced by various variables like sales, salary, advertisement etc. 2) There is a very high positive relationship (R=0.813) among the variables but the relationship is not statistically significant. 3) Sales has the most influence on profit but the relationship is also not statistically significant. 4) Analysis of historical profit data from 1999-2013 finds the company has a average annual growth rate of 3.49% and acceleration rate of 3.76%, suggesting future performance will be promising if this trend continues.

2. Future Performance Analysis of
PRAN AMCL LTD

3. GROUP MEMBERS OF UNBOUND……..
Name Class ID
Golam Rabbani Sunny(GC) 51
Mohammad zahedul lslam 87
Tanjia Sobren 72
Umme Salma Soma 91
Jayanti Das Jaya 59
Jaed Abdul Alim 103
Ashif Mohammed Rabbi 22

4. Given Information of PRAN AMCL LTD.
PRAN AMCL (LTD) started in 1981 as processors of fruits
and vegetables in Bangladesh. It is the largest food and
nutrition’s company in Bangladesh. It is the largest exporter
of processed agro products with compliance of HALAL and
HACCP to more than eighty two countries. The Company’s
principal activity is the manufacturer and sale of juice,
snacks, soft-drinks, cakes dairy products etc. All the PRAN
products are produced as per international standards
maintaining highest level of quality at every stage of it’s
production process.

5. The required equation is, y=a+b1x1+b2x2+b3x3+b4x4+b5x5+b6x6. Where y= Profit
a= Constant
X1 = Sales
X2 = Salary
From the Table we get X3 = Advertisement
Y=37234695.337+.065x1-.340x2+.464x3-.142x4-.128x5+.103x X4 =Operating Expense
X5 = Current Asset
X6 =Current Liability
Coefficientsa
Model Unstandardized Coefficients Standardize
d
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 37234695.337 21500505.14
1 1.732 .122
SALES .065 .067 3.005 .960 .365
SALARY -.340 .967 -.580 -.352 .734
ADVERTISEMENT .464 1.993 .415 .233 .822
OPERATINGEXPENSE -.142 .138 -1.074 -1.031 .333
CURRENTASSET -.128 .068 -2.820 -1.870 .098
CURRENTLIABILITIES .103 .085 1.615 1.215 .259
a. Dependent Variable: PROFIT

6.  Y=37234695.337+.065x1-.340x2+.464x3-.142x4-.128x5+.103x6
 2.3 This equation indicates that
 i) If all the variables remain zero then there will be a profit of
37234695.337 TK.
 ii) If a volume of sale increases for 1crore, profit of the company
will increase by .05 croer, provided salary, advertisement,
operating expense and product cost remains constant
 iii) If the amount of salary increase for 1crore, profit of the
company will decrease by .340 croer, provided other variables
remains constant
 iv) If the amount of advertisement is increased by 1crore, the
profit of the company will be increased by .464 crore, provided
other variables remain constant

7. v) If the amount of operating expense is increased by 1crore,
the profit of the company will be decreased by .142 crore,
provided other variables remain constant
vi) If the amount of current asset is increased by 1crore , the
profit of the company will be decreased by .128 crore,
provided other variables remain constant
vii) If the amount of current liabilities is increased by 1crore,
the profit of the company will be increased by .103 crore,
provided other variables remain constant

8. It determines the extent or the degree of relationship among the variable
Range Degrees of relationship
0 Absence of relationship
.01-.29 Very low degree of
relationship
.30-.49 Low degree of relationship
.50-.69 Moderate degree of
relationship
.70-.89 High degree of relationship
.90-.99 Very high degree of
relationship
1 Perfect relationship
R= coefficient of multiple correlation

9. Model Summary
Model R R Square Adjusted R Square Std. Error of the
Estimate
1 .813a .661 .407 5750924.32294
a. Predictors: (Constant), CURRENTLIABILITIES, SALARY, OPERATINGEXPENSE,
CURRENTASSET ,
ADERTISEMENT, SALES
R=.813, there exists a very high degree of positive relationship among the variables under the study.
R=.813a there exists a high degree of positive relationship among
the variables under the study.

10. MULTICOLLINEAERITY
Multicollinearity is the situation in which independent variable in a multiple regression equation are
highly interrelated.
Problems Created by Multicollinearity …..
1. The Regression coefficient would be unreliable and hence may draw illogical economic
interpretation.
2. The confidence intervals of tests employing the t and F distributions are no longer strictly
applicable.
3.The standard error of the regression coefficient underestimates the variability of the estimated
regression coefficient.
When extreme Multicollinearity exists, no acceptable way is available to perform a
multiple
regression analysis by using the given set of independent variables. There are three suggested
solutions. They
are:
1. Log transmission
2. Factoring
3. Dropping

12. R2= Coefficient of multiple determination
It determines the explanatory power of the independent variables on the
dependent variables. If it is more than 50% then the independent variables are
very influential
Model Summery
Model R R Square Adjusted R Square Std. Error of the
Estimate
1 .813a .661 .407 5750924.32294
R2=.661, indicates that sales, expenses, salary and product other expense explain 66.1%
variation in the profit. So the variables are very influential.
a. Predictors: (Constant), CURRENTLIABILITIES, SALARY,
OPERATINGEXPENSE, CURRENTASSET, ADVERTISEMENT, SALES

13. Model Unstandardized Coefficients Standardize
d
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 37234695.337
21500505.1
41
1.732 .122
SALES .065 .067 3.005 .960 .365
SALARY -.340 .967 -.580 -.352 .734
ADVERTISEMENT .464 1.993 .415 .233 .822
OPERATINGEXPEN
SE
-.142 .138 -1.074 -1.031 .333
CURRENTASSET -.128 .068 -2.820 -1.870 .098
CURRENTLIABILITIE
S
.103 .085 1.615 1.215 .259
a. Dependent Variable: PROFIT
Coefficients a
As b1 is significant at 0.365 level, there is no significant relationship between sales and profit. The
significance value also indicate that the relationship between profit –production, profit –dividend,
profit-advertisement, profit-asset, profit-liability are not statistically significant. Because in both the
cases the significant level than the calculated value.

14. ANOVA
Model Sum of Squares Df Mean Square F Sig.
1
Regressio
n
515641393686770.900 6 85940232281128.480 2.598 .106b
Residual 264585044545508.620 8 33073130568188.580
Total 780226438232279.500 14
a. Dependent Variable: PROFIT
b. Predictors: (Constant), CURRENTLIABILITIES, SALARY,
OPERATINGEXPENSE, CURRENTASSET, ADVERTISEMENT, SALES
The result of ANOVA table indicates that the relationship between profits
and all six independent variable is not statistically significant. The test is
not significant at .106 as the level which is greater than .05.

15. Descriptive Statistics
Mean Std. Deviation N
PROFIT 40524655.6000 7465283.64322 15
SALES 930757008.2000 346748899.07616 15
SALARY 41192336.4000 12726816.52109 15
ADVERTISEMENT 25670953.8667 6681224.46237 15
OPERATINGEXPENSE 170472841.0667 56525824.69595 15
CURRENTASSET 605780380.8000 164628387.40208 15
CURRENTLIABILITIES 455325875.8000 117142041.26431 15

16. Model Unstandardized Coefficients Standardize
d
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 37234695.337
21500505.1
41
1.732 .122
SALES .065 .067 3.005 .960 .365
SALARY -.340 .967 -.580 -.352 .734
ADVERTISEMENT .464 1.993 .415 .233 .822
OPERATINGEXPEN
SE
-.142 .138 -1.074 -1.031 .333
CURRENTASSET -.128 .068 -2.820 -1.870 .098
CURRENTLIABILITIE
S
.103 .085 1.615 1.215 .259
a. Dependent Variable: PROFIT
Influential variable
Sales is the most important variable

17. Time series Analysis
Year Profit(million) t yt t2
1999 34 1 34 1
2000 34 2 68 4
2001 42 3 126 9
2002 43 4 172 16
2003 44 5 220 25
2004 40 6 240 36
2005 41 7 287 49
2006 29 8 232 64
2007 29 9 287 81
2008 36 10 232 100
2009 40 11 261 121
2010 44 12 360 144
2011 45 13 440 169
2012 52 14 528 196
2013 55 15 585 225
Total 608 120 728 1240

18. Here n=15
∑y=22501
∑yt= 139177
∑y= an+ b .∑t……………………. (1)
∑yt=a ∑t+ b ∑t2…………………. (2)
After the calculation of these two equations, we
get
the value of a= 33.67
b= 0.86
The equation is Y = a+ bt
=33.67+0.86 t
∑y 608
∑t 120
∑yt 5106
∑t2 1240

19. Linear Equation
y = 0.8643x - 1693.2
0
10
20
30
40
50
60
1998 2000 2002 2004 2006 2008 2010 2012 2014
profit
profit
Linear
(profit)

20. Growth Rate
GR (Growth rate) =
=
=3.49%
Where n= year
Yn = Profit of 2013
Y1 = Profit of 1999
It means that during the period
1999-2013 Profit increase
of an average rate is 3.49%.
year Profit (million)
1999 34
2000 34
2001 42
2002 43
2003 44
2004 40
2005 41
2006 29
2007 29
2008 36
2009 40
2010 44
2011 45
2012 52
2013 55
Toatal y=608

21. Acceleration Rate year Profit (million)
1999 34
2000 34
2001 42
2002 43
2003 44
2004 40
2005 41
2006 29
2007 29
2008 36
2009 40
2010 44
2011 45
2012 52
2013 55
Total Y=608
AR (Acceleration Rate) =
=
= 3.76%
Where n = year
Yn = Profit of 2013
Y1= Profit of 1999
Y2= Profit of 2000
It indicates that in the year to come
growth rate will be increased at an average
rate of 3.76 of the previous year and in future
in each sequential year. The growth rate will
be (100 + 3.76%) or 103.76 of the previous
year of growth rate.

22. Future Performance analysis….
• PRAN Agricultural Marketing Company (ltd) is
conducting a good position in the market where they have the
growth rate of 3.49% and the acceleration rate is 3.76%. So,
it can be easily said that the performance of PRAN
Agricultural Marketing Company (ltd) in future will be
promising if they maintain this trend in future.