CO2 EMISSION 1 1. CO2 EMISSION 1: Graphical Techniques and Research Methods for Business Decision Making

CO2 EMISSION 1 (a) Graphical technique for comparing values:1 (b) Graphical technique for comparing the percentage value 3 (c) Comparing a & b:4 2.

Statistics and
Research Methods
for Business
Decision Making

Table of Contents
INTRODUCTION...........................................................................................................................1
1. CO2 EMISSION..........................................................................................................................1
(a) Graphical technique for comparing values:......................................................................1
(b) Graphical technique for comparing the percentage value................................................3
(c) Comparing a & b:..............................................................................................................4
2. ANALYSIS OF TIME OF ASSEMBLY....................................................................................4
(a) Frequency Distribution and relative frequency distribution:............................................4
(b) Cumulative frequency distribution:..................................................................................5
(d) Ogive:................................................................................................................................7
(e) When data is less than 65:.................................................................................................9
(f) When data is more than 75:...............................................................................................9
3. ESTIMATION AND TESTING SIGNIFICANCE LEVEL .....................................................9
(a) Graphical Descriptive Measure of two variables:.............................................................9
(b) Scatter plot:.....................................................................................................................10
(c) Numerical brief report of data:........................................................................................12
(d) Coefficient of correlation between the rate of inflation and all ordinaries index:..........13
(e) Simple linear regression model and the linear equation:................................................14
(f) Coefficient of Determination R2 :...................................................................................14
(g) Evaluation of significant relationship at 5% significance level:.....................................15
(h) Value of the standard error of the estimate (se):.............................................................15
CONCLUSION..............................................................................................................................15
REFERENCES..............................................................................................................................17

INTRODUCTION
Using their knowledge and experience, corporate leaders and executives used to
take difficult choices. However, they would like to understand more and more what the
figures indicate. Statistics and Research Methods used by operational experts and
analysts in age of big data offer additional definite proof to support management actions
on manufacturing, distribution, advertising, and people management. These techniques
also assist executives in projecting future company circumstances so that they can
improve their practices when required (Berman and Wang, 2016). These tools can be
used for resources, machinery, equipment, cash and time apportionment and
standardization. Projects are planned using quantitative methods and configured with
labour force and material delivery. This study covers practical implications of different
statistical techniques and application of statistical knowledge to summarize data
statistically and graphically.
1. CO2 EMISSION
(a) Graphical technique for comparing values:
Nation
2009 (in millions of metric
tons)
2013 (in millions of metric
tons)
United States 5957 5300
China 5323 10330
Russia 1696 1800
Japan 1230 1360
India 1166 2070
Germany 844 840
Canada 631 550
United Kingdom 577 480
South Korea 500 630
Italy 467 390
Iran 451 410
South Africa 424 330
France 415 370
Saudi Arabia 412 490
Australia 407 390
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Graphical representation of table of CO2 emission is showing that China has
reported maximum increase in level of emissions. In 2009, China's CO2 was in 5323
millions of metric tons which has further increased to 10330 millions of metric tons in
year 2013. South Africa has reported maximum decrease in emissions as this country's
figures of CO2 emissions are 424 in year 2009 and 330 in year 2013. Among all
countries Australia is only one country which has reduced the CO2 emission during year
2009 to 2013. Australia's CO2 emission figure was 407 millions of metric tons in year
2009 which has been reached to 390 millions of metric-tons.
2
South Africa
United Kingdom
Italy
Canada
United States
France
Iran
Australia
Germany
Russia
Japan
Saudi Arabia
South Korea
India
China
-0.4-0.200.20.40.60.811.2
-22.17%-16.81%-16.49%-12.84%-11.03%-10.84% -9.09% -4.18% -0.47% 6.13% 10.57%
18.93% 26.00%
77.53%
94.06%
% change in value

(b) Graphical technique for comparing the percentage value
Country % change
South Africa -22.17%
United
Kingdom -16.81%
Italy -16.49%
Canada -12.84%
United States -11.03%
France -10.84%
Iran -9.09%
Australia -4.18%
Germany -0.47%
Russia 6.13%
Japan 10.57%
Saudi Arabia 18.93%
South Korea 26.00%
India 77.53%
China 94.06%
3

This graphical presentation is based on percentage change in figures of CO2
emission. Above table figures out 15 nation's figures and fluctuation in level of CO2
emission. Nations named Australia, South Africa, United Kingdom, Italy, Canada, United
States, France, Iran, Australia and Germany has controlled their CO2 emissions figures
by implementing new rules, modifying existing policies and legislation which are
dedicated towards saving environment, minimize CO2 emission and other environment
friendly activities. Where as China, India, South Korea, Saudi Arabia, Japan and Russia
are countries which are careless about such serious matter. China and India with
largest population in world are top concretes responsible for increasing CO2 emissions
(increase of 94.06% and 77.53% respectively in china and India).
(c) Comparing a & b:
From combined comparison of figures presented in point (a) and (b) it has been
analysed that here among 15 top CO2 producer countries in 2009 and 2013, only 9
countries have successfully minimised their CO2 emissions by implementing strict rules
and policies to minimise use of vehicles producing CO2 and by increasing the habits of
cycling and use of electric vehicles (Bickel and Lehmann, 2012). In point (a) only two
year comparison of each country's figures of CO2 emissions displayed whereas in point
(b) change in values in percentage terms are presented which provides easiness in
analysis of figures.
2. ANALYSIS OF TIME OF ASSEMBLY
(a) Frequency Distribution and relative frequency distribution:
Frequency Distribution: This is a representation which help to show the
number of observation inside a specific interval either in tabular or graphical format.
This is mainly used within a statistical context that is linked with charting of normal
distribution (Dittmar and Duchin, 2015).
Classes Frequency
35 - 45 3
45 - 55 5
55 - 65 10
4

65 - 75 17
75 - 85 3
85 - 95 1
95 - 105 1
Total 40
Relative Frequency: It is a type of distribution that focus to count the total
population and frequency for a sub group of individuals. There is a systematic
experimental approach in this method in which experiment have to be repeated again
and again in order to extract the best outcome from these trail as it is a experiment and
it is possible to get different relative frequency.
Frequency Relative frequency
3 0.60
5 0.50
10 0.59
17 5.67
3 3.00
1 1.00
1 0.03
40 11.38
(b) Cumulative frequency distribution:
Cumulative Frequency: It is described as a complete operating frequency. The
intensity of an component in a set represents the number of elements throughout the
set. This could also be described up to the present stage as the amount of all preceding
frequencies (Hair Jr and et.al., 2015). If the importance of the cumulative frequency
shows the amount of components in the information set underneath the present value, it
is essential to analyse information. The cumulative frequency is indeed when using
diagrams such as histograms to define information.
Classes Frequency Relative frequency Cumulative-frequency
35 - 45 3 0.60 3
5

45 - 55 5 0.50 8
55 - 65 10 0.59 18
65 - 75 17 5.67 35
75 - 85 3 3.00 38
85 - 95 1 1.00 39
95 - 105 1 0.03 40
40 11.38
Relative Cumulative Frequency:
Cumulative-frequency Cumulative relative frequency
3 0.60
8 1.10
18 1.69
35 7.35
38 10.35
39 11.35
40 11.38
(c) Histogram: A histogram of frequency is just such as bar chart, in which the
horizontal axis provides the interval of objects concerned and the vertical axis provides
the number of objects falling on each stage. It therefore provides for each of the ranges
the frequency or the amount of occurrences information points (Hutton, Jiang and
Kumar, 2014).
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(d) Ogive:
An ogive chart is a plot used to display accumulated frequency range in statistical
data. It enables us to predict rapidly number of measurements which are less than or
identical to a specific value. This graph mainly define the cumulative frequencies within
frequency distribution and it help to create a continuous frequency curve. It enables us
to predict rapidly the amount of findings below or equivalent to a specific value ( Thiem,
2014).
Ogive of cumulative frequency
Classes Cumulative frequency
35 - 45 3
45 - 55 8
55 - 65 18
65 - 75 35
75 - 85 38
85 - 95 39
95 - 105 40
7

Ogive curve of cumulative relative frequency:
Classes Cumulative relative
frequency
35 - 45 0.6
45 - 55 1.1
55 - 65 1.69
65 - 75 7.35
75 - 85 10.35
85 - 95 11.35
95 - 105 11.38
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(e) When data is less than 65:
From the table above, it has been determined that different class interval have
been used to define the relative frequency for number of worker working inside the
business firm in order to give best results. The exact information was farmed so that
actual results and strength can be discovered for those worker those are actually taking
time less than 65 second. In order to calculate the outcome, a proper and valid
calculation must be done according to the statistical formula such as
=countif(range,'<65). From this specific formula the result defines that there are total 16
employee those are taking less time than 65 sec in order to complete the allotted work
to weld and assemble the respective project plant.
(f) When data is more than 75:
In the above table, various class interval are used to describe the cumulative
relative frequency for the total number of employees working on a specific project of
company and their actual time take to complete the work. From the results describe the
importance among the results when the collected data was greater than 75 second. To
calculate the total number of worker taking time more than 75 second, particular formula
have been utilized that is =countif(range,”>75”). As a result it has been determined that
there were total 5 individual worker those are taking more time than 75 second in
context to finish the particular assigned work which is to weld a particular car model.
3. ESTIMATION AND TESTING SIGNIFICANCE LEVEL
(a) Graphical Descriptive Measure of two variables:
As inflation decreases Australia's spending power strength. The assessment
says the dollar's buying power reduction condition. Investors ' demands were greater to
assess the investment values in order to be able to obtain appropriate return on their
funds. Consumer-price was impacted by regular modifications in the cost of different
commodities (Meyr, Myers and Pontious, 2014). The client price index and all regular
index from 1995-2015 are provided. The assessment shall be carried out below:
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Here two variables as shown in graph are Rate of inflation and ordinaries index.
The above graph describes these two variables and from analysis it has been analysed
that in 2007 ordinaries index was maximum i.e. 6337.6 and in year 2005 inflation rate is
highest i.e. 5.9%.
(b) Scatter plot:
A scatter plot, also referred as scatter chart or graph, is two-dimensional
statistics visual representation using dots to depict the outcomes acquired for two
distinct factors-one placed along x-axis and other plotted across the y-axis. Scatter plots
are used when connection between two data sets is to be shown. Scatter plots are often
referred to as plots of correlation as they demonstrate how two parameters are linked
(Priebe and Spink, 2012).
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In inflation rate and ordinaries index case scenario, chart is not just ordinary log
of inflation rate and ordinaries index over different year, but it also visualise unique
relationship among inflation rate and ordinaries index- namely that inflation rate
increases as ordinaries index figures decreases. Also noted that such relationship isn’t
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perfect, as compare of increasing inflation rate in some years ordinaries indexes also
increased but common trend is quite strong and inflation rates are correlated with
ordinaries indexes.
(c) Numerical brief report of data:
Year
Rate of inflation
(%)
All-Ordinaries
index
Median 2005 2.5 4127.6
Mean 2005 2.69047619 3982.733333
Standard Error 1.354006401 0.260393911 269.9897018
Standard
Deviation 6.204836823 1.193274806 1237.248245
Mode #N/A 2.4 #N/A
Sample
Variance 38.5 1.423904762 1530783.22
Kurtosis -1.2 2.038281598 -1.010009362
Skewness -4.90835E-17 0.798407674 0.182747008
Range 20 5.6 4336.8
Minimum 1995 0.3 2000.8
Maximum 2015 5.9 6337.6
Sum 42105 56.5 83637.4
Count 21 21 21
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(d) Coefficient of correlation between the rate of inflation and all ordinaries index:
Under normal linear-regression assessment, correlation coefficient (or coefficient
of correlation) is a statistic indicating a link between independent variable as well as
dependent variable. The correlation coefficient is reflected by "r" and also has a
multitude or rage -1.00 to + 1.00. If correlation coefficient is a positive quantity, such as
here + 0.87 of All-Ordinaries index, this implies that whenever independent variable
increases, the dependent variable increases (Sekaran and Bougie, 2016). A negative
amount of -0.0459206557 shows an inverse association implies when independent
variable increases and vise versa, dependent variable decreases.
Rate of inflation
(%)
All-Ordinaries
index
Rate of inflation
(%) 1 0.87
All-Ordinaries
index -0.0459206557 1
Correlations
Rate of
inflation (%)
All-Ordinaries
index
Rate of inflation
(%)
Pearson Correlation 1 -.011
Sig. (2-tailed) .963
Sum of Squares and
Cross-products 32.952 -342.143
Covariance 1.648 -17.107
N 21 21
All-Ordinaries
index
Pearson Correlation -.011 1
Sig. (2-tailed) .963
Sum of Squares and
Cross-products -342.143 30615662.57
1
Covariance -17.107 1530783.129
N 21 21
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(e) Simple linear regression model and the linear equation:
The above regression model and equation is showing a positive relationship
between ordinary indexes and inflation-level, as diagram is simply indicating upward
trend with no major decline.
(f) Coefficient of Determination R2 :
It help to ascertain how much variation two factors contain, or how much
variation a range of factors (explanatory variables) in a result variable explains or
accounts for. Outcomes may varies from 0 to 100% or 0.00 to 1.00. With regard to
regression-analysis, the determination of coefficient is an aggregate measure of
regression model's validity. The measurement of this coefficient is to split the r value
between two variables under simple linear regression assessment, in which r is
correlation coefficient. It makes it easier to identify how well a row of regression
matches (a.k.a., fitness goodness). A R2 value of 0 shows that line of regression
doesn't really match the set of information points as well as value of 1 shows that line of
regression lines up set of information points completely.
As per given variables coefficient of determination is R2 = 0.9892, which reflects
that there are significant differences actually exist between inflation-level and
dollar's prices.
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(g) Evaluation of significant relationship at 5% significance level:
A significance level, also referred to as alpha or α, is a test of strength of the
proof to be available in sample before rejecting null hypothesis and concluding that the
impact is statistically important. Before undertaking experiment, the investigator
determine the level of meaning.
At given 5% significant-level, correlation coefficient is measured as 0.038875116
that is lower than 0.05, such figure state that there's no significant-level difference
resides in ordinary-index and rate of inflation.
(h) Value of the standard error of the estimate (se):
The standard error is indeed the projected standard deviation or variation
measurement in a descriptive statistics distribution. A small standard error implies that
the sampling distribution has comparatively less stretched. The standard error shows
the probable precision of the mean sample compared to the mean population. The
standard error reduces with increasing sample-size and approaching population-size.
Sigma (π) is symbol of standard error, the stats are shown by a integrand. The standard
mean-error is denoted by πM. To assess standard mean-error, split the standard
deviation though square root of test-size: in which the sample-size is the standard
deviation of the initial-sample distribution and sample size is N (Vogt and Barta, 2013).
Here from standard error of provided data is 1.354006401, A lower figure of SE
implies that sample's mean is more accurately reflects actual population's mean. Higher
sample size would usually result in smaller SE. So assessed figure of standard error is
reflecting that sample mean is not so much accurately reflecting overall population's
mean.
CONCLUSION
From above study it has been articulated that Research and Statistics Methods
are essential aspect of business's decision making process. These methods also help in
evaluating industry and market scenarios. Different internal and external variables are
also analysed to detect any unfavourable circumstances for market and industry. The
whole study evaluates different case scenarios by applying different techniques for
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better interpretation and analysis. All kind of techniques defines different aspects of
research.
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REFERENCES
Books and Journals:
Berman, E. and Wang, X., 2016. Essential statistics for public managers and policy
analysts. Cq Press.
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MA.
Dittmar, A. and Duchin, R., 2015. Looking in the rearview mirror: The effect of
managers' professional experience on corporate financial policy. The Review of
Financial Studies. 29(3). pp.565-602.
Hair Jr, J.F., Wolfinbarger, M., Money, A.H., Samouel, P. and Page, M.J.,
2015. Essentials of business research methods. Routledge.
Hutton, I., Jiang, D. and Kumar, A., 2014. Corporate policies of Republican
managers. Journal of Financial and Quantitative Analysis. 49(5-6). pp.1279-
1310.
Meyr, A.J., Myers, A. and Pontious, J., 2014. Descriptive quantitative analysis of hallux
abductovalgus transverse plane radiographic parameters. The Journal of Foot
and Ankle Surgery. 53(4). pp. 397-404.
Priebe, C. S. and Spink, K. S., 2012. Using messages promoting descriptive norms to
increase physical activity. Health Communication. 27(3). pp.284-291.
Sekaran, U. and Bougie, R., 2016. Research methods for business: A skill building
approach. John Wiley & Sons.
Thiem, A., 2014. Membership function sensitivity of descriptive statistics in fuzzy-set
relations. International Journal of Social Research Methodology. 17(6). pp.625-
642.
Vogt, A. and Barta, J., 2013. The making of tests for index numbers: Mathematical
methods of descriptive statistics. Springer Science & Business Media.
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