Quantitative Methods for Business

This document discusses the need and significance of statistical techniques in analyzing information for ethical considerations and making decisions. It covers topics such as regression coefficients, selection of regions, and expected running costs. The subject is Quantitative Methods for Business.

Quantitative Methods for Business

Contents
INTRODUCTION...........................................................................................................................3
QUESTION 2..................................................................................................................................3
a) Explanation of meaning of 4 regression coefficient of data....................................................3
b) Brief explanation regarding decision taken of selection of region..........................................5
c) Calculation of expected running cost......................................................................................5
QUESTION 3..................................................................................................................................6
QUESTION 5..................................................................................................................................9
b) Evaluate whatever income of self-employment in clothing industry is different.................10
c)Decision taken regarding manufacturing process...................................................................10
CONCLUSION..............................................................................................................................11
REFERENCES..............................................................................................................................12

INTRODUCTION
In order to analyse information for the ethical consideration, individual people use statics tools.it
is a study subdivision that is helpful for individual people to helping global, manufacturing
issues by accumulating important information (Gatsi, 2016). In order to analyse the information,
the relationship between factors and discover out the incidence of successes and failures of
events or projects by calculating the likelihood frequency, this document has been developed to
describe the need and significance of statistical techniques. This study further illustrates the use
of mean difference and correlation effects in order to take potential decisions on the execution of
industrial activities.
QUESTION 2
a) Explanation of meaning of 4 regression coefficient of data.
Calculation of regression coefficient of
data
Y= a+bx
X Y
( X-
X)
9Y-
Y)
(x-
x)2
(y-
y)2
(x-x) (y-
y)
4 5.3 -4 3.7 16 13.69 14.8
4.6 6.7 -3.4 2.3 11.56 5.29 7.82
5.9 7.5 (2.10 2.5 4.41 6.25 5.25
6.7 8.8 1.3) 0.2 1.69 0.04 0.26
8 8 0 1 0 1 0
8.9 9.1 0.9 0.1 0.81 0.01 0.09
8.9
10.
5 0.9 1.5 0.81 2.25 1.35
10.1 10 2.1 1 4.41 1 2.1
10.8
11.
7 2.8 2.7 7.84 7.29 7.56
12.1
12.
4 4.1 3.4 16.81 11.56 13.94

Sum = 80 90 64.34 48.38 53.17
Mean = 8 9
b1 = Σ [ (xi - x) (yi - y)] / Σ [ (xi - x)2]
B1 = 53.17/64.34 = 0.82
b0 = y - b1 * x = 9-0.82*8 = 2.44
Distance
Travelled
Running
Costs (x-x) (y-y) (x-x)2 9y-y)2
(x-x)
(y-y)
3.5 6.9 -4.5 -2.1 20.25 4.41 9.45
4.6 7.6 -3.4 -1.4 11.56 1.96 4.76
5.3 7.9 -2.7 -1.1 7.29 1.21 2.97
6 8.3 -2 -0.7 4 0.49 1.4
7.2 8.8 -0.8 -0.2 0.64 0.04 0.16
8.4 9.2 0.4 0.2 0.16 0.04 0.08
10.1 9.6 2.1 0.6) 4.41 0.36 1.26
11.1 10.3 3.1 1.3 9.61 1.69 4.03
11.5 10.1 3.5 1.1 12.25 1.21 3.85
12.3 11.3 4.3 2.3 18.49 5.29 9.86
80 90 0 0 88.66 16.7 37.82
Mean of X series = 8
Mean of Y series = 9
Coefficient regression = 37.82/88.66 = 0.42
9-0.42*8 = 5.64
Regression coefficients:This is a method that forms part of the central trend calculation which is
measured for the purpose of measuring the average shift of the factor with the same dependent
variable in the other factor (Apuke, 2017). The slope line of the coefficients of correlation has
also been considered to represent the value. The regression model helps to assess the relations
between various or more than 2 variables depending with one and one exponential function on
the other. The coefficient is determined by combining the values of predictor variables.

There are different approaches by which individuals can measure coefficient regression, but liner
correlation is among the most relevant and beneficial approaches as the estimation is fairly
straightforward than other forms of estimated regression solving or measurement. Using the
measurement of personal correlations to assess error and the cause for deviations, all of which
contribute to more company policies being determined and helpful in making important choices.
Coefficient regression assist in determining out accurate amount and identify optimistic and also
pessimistic relationship between two variables (Molina-Azorin, 2016). Relationship between two
variables of Car F and Car L is optimistic that also implies that whenever the duration of travel
time is already rises then the expense of trying to travel or trying to run affect the total instantly.
On the grounds of that individual, they will be capable of taking a professional judgement, that
will be useful for future development or corporate strategic planning.
b) Brief explanation regarding decision taken of selection of region.
In order to extend the new area for the purposes of individual transport, it is appropriate to opt to
go with Car DF in necessary to protect the range as the operating expense is comparatively
smaller than Car L in plan to enlarge the distance cap. They then agreed to choose Car F. Which
would assist or gain in handling the organizational costs for the purposes of operating business
activities.
The length of transport also increases because of the expense increase, which could be the
explanation that the projected pace also increases.
c) Calculation of expected running cost.
The expected cost of Car increase from 10 % thus in order to
X
4 5.3 21.2
4.6 6.7 30.82
5.9 7.5 44.25
6.7 8.8 58.96
8 8 64
8.9 9.1 80.99

X
8.9 10.5 93.45
10.1 10 101
10.8 11.7 126.36
12.1 12.4 133.92
Sum = 80 90 755
Distance Travelled
3.5 6.9 24.15
4.6 7.6 34.96
5.3 7.9 49.8
6 8.3 63.36
7.2 8.8 59.04
8.4 9.2 77.28
10.1 9.6 96.96
11.1 10.3 114.33
11.5 10.1 116.15
12.3 11.3 139
80 90 773
If the expense of these kilometres is more than 10%, the worth of these vehicles would rise from
755 to 830.5, which implies they have to increase supply to offset the cost. Even so, it also
involves the rise in the expense of these vehicles and the range to drive.

QUESTION 3
(a)
(b)
Box A Box B
White 3 Green 4
Blue 2 Blue 5
Total 5 Total 9
Probability of one is green and other is
white
Probability of green= 5/9
Probability of white= 3/5
Probability of one is green and other is
white= 5/9*3/5: 1/3
2. Probability of same color balls
So probability of blue from box A: 2/5
So probability of blue from box B: 5/9
Probability of same color
= 2/5*5/9
= 2/9
QUESTION 5
a)
Given data:
Mean (μ) = 10.5
Standard Deviation (σ ) = 3
X ∼ Random Sample (Clerical Workers)
In order to find Probability, First we have to find the value of z
Formula of z= σX−μ
Finding probability that their average time is more than 9.5 minutes (X >9.5)

Put the values of X, μ and σ in above formula
z= 39.5−10.5 = - 0.33
So, now look for the value of P at z= -0.33 from the z table is 0.3707
b) Evaluate whatever income of self-employment in clothing industry is different
Usage of mean and median in describing the relationship and discrepancies between two
variables. These methods are helpful in assessing the uncertainty and major variations between
different results (Scott and Siltanen, 2017).
In this case, the average scores were 15000 and the sample variance was 975, the other hand
meaning of the average in the manufacturing industry was 14500 and the criterion was 169, the
sample variance in this scenario was 9.82. On the grounds of this, it stated that the profit of
workers working in the garment sector as self-employment is unique compared to the fashion
industry because as level of difference is relatively high.
c)Decision taken regarding manufacturing process.
To evaluate the likelihood that 1% of the importance of the current procedure is greater than the
old one, the testing mean variance has been determined. By their use of the distribution principle,
individuals are able to discover their data series outcome and likelihood. For this function, for
measurement purposes, the mean and standard deviation are used.
As compared to the old one, the importance of the modern method of measuring standard
deviation is even greater (Fremeth,Holburn and Richter, 2016). Therefore, the degree and rate of
importance is much greater than the old protocol, which shows that as opposed to the other, the
standard process is more advantageous for industrial organizations. This judgment is based on
the estimation of the interaction worth of events and the estimate of the sampling error discovery
equation.

CONCLUSION
From the aforementioned analysis, it has been established that individuals are able to evaluate
the correlation between two or more variables and use these key pattern instruments to take
potential business decisions on behalf of the estimation of the likelihood of success or loss of
events through the use of various statistical methods. By using the estimation of confidence
interval and correlation values, individuals are able to devise possible strategies and make
decisions about operating their businesses, companies or any business field.

REFERENCES
Gatsi, J.G., 2016. Introduction to quantitative methods in business. Xlibris Corporation.
Apuke, O.D., 2017. Quantitative research methods: A synopsis approach. Kuwait Chapter of
Arabian Journal of Business and Management Review, 33(5471), pp.1-8.
Molina-Azorin, J.F., 2016. Mixed methods research: An opportunity to improve our studies and
our research skills.
Martí, J., 2016. Measuring in action research: Four ways of integrating quantitative methods in
participatory dynamics. Action Research, 14(2), pp.168-183.
Hodis, F.A. and Hancock, G.R., 2016. Introduction to the special issue: Advances in quantitative
methods to further research in education and educational psychology.
Counsell, A., Cribbie, R.A. and Harlow, L., 2016. Increasing literacy in quantitative methods:
The key to the future of Canadian psychology. Canadian
Psychology/psychologiecanadienne, 57(3), p.193.
Scott, N.A. and Siltanen, J., 2017. Intersectionality and quantitative methods: assessing
regression from a feminist perspective. International Journal of Social Research
Methodology, 20(4), pp.373-385.
Fremeth, A.R., Holburn, G.L. and Richter, B.K., 2016. Bridging qualitative and quantitative
methods in organizational research: Applications of synthetic control methodology in the
US automobile industry. Organization Science, 27(2), pp.462-482.