Data Collection and Descriptive Analysis of Coca Cola's Carbon Emission Data from 2011-2017

This article presents the data collection and descriptive analysis of Coca Cola's carbon emission data from 2011-2017. The article discusses the data cleaning process, descriptive statistics, histograms, and categorical variables. The inferential analysis includes chi-square test, correlation, and regression analysis.

Data collection
For the current research the data has been collected for Coca Cola company which are located in
the different countries for the time period 2011- 2017. A separate excel sheet has been prepared
for the data. The data cleaning process are performed in excel such as the missing value, deleting
the variables not required for the analysis etc. Once the data was cleaned it was imported into
SPSS for further analysis. The categorical variables were coded in SPSS.
Data analysis (descriptive)
As shown in Table 1, the descriptive statistics for the continuous variable includes the median,
mean, mode, minimum, maximum, standard deviation, Skewness and the Kurtosis. The table
below shows the results of the descriptive statistics for the continuous variable included in the
data set. The mean value of the first variable is 62.58 and the standard deviation for the mean is
230.37 which suggests that values are far from the mean as there is high variability in the data
set. Higher standard deviation infer higher variation in the data set and vice a versa. The results
for the other variables can also be interpred in the similar way. The minimum and maximum
values are also given. For the first variable the minimum is 5 and the maximum is 999, which
also infer high variation in the data set(Bryman, A. and Bell, 2011; Saunders, Lewis, &
Thornhill, 2009).
Statistics
CC11.1 CC11.3 CC11.5 C1 CC11.5 C2 CC11.5 C3
N Valid 36 36 36 36 36
Missing 0 0 0 0 0
Mean 62.5833 229580.6286 347633.1811 177270.9172 171046.7917
Median 5.0000 999.0000 999.0000 999.0000 999.0000
Mode 5.00 999.00 999.00 999.00 999.00
Std. Deviation 230.37093 460014.8856
5
1062861.274
74
378705.7726
3
984598.0349
0
Variance 53070.764 21161369501
7.061
11296740893
46.597
14341806222
5.159
96943329032
3.077
Skewness 4.049 2.183 4.869 2.526 5.997
Std. Error of
Skewness
.393 .393 .393 .393 .393
Minimum 5.00 999.00 999.00 999.00 .00
Maximum 999.00 1702060.00 6105629.00 1474765.00 5913631.00
Percentiles
25 5.0000 999.0000 999.0000 999.0000 999.0000
50 5.0000 999.0000 999.0000 999.0000 999.0000
75 10.0000 278048.4700 192057.9300 179205.1800 999.0000

Statistics
CC11.5 C4 CC12.2 C5 CC12.2 C6
N Valid 36 36 36
Missing 0 0 0
Mean 1022.5694 10.1772 55.2222
Median 999.0000 6.8400 -1.0000
Mode 999.00 .00a -1.00
Std. Deviation 1578.06826 11.06356 232.14861
Variance 2490299.445 122.402 53892.978
Skewness 5.385 2.099 4.051
Std. Error of
Skewness
.393 .393 .393
Minimum .00 .00 -1.00
Maximum 9931.00 51.00 999.00
Percentiles
25 499.5000 2.1275 -1.0000
50 999.0000 6.8400 -1.0000
75 999.0000 15.0750 1.0000
a. Multiple modes exist. The smallest value is shown
Furthermore the C12. 2 C6 is the dependent variable which is the percentage change in the
carbon emission for the firms included in the data set. The results show that the average change
is 55 % whereas the median is -1. This show high difference in the two central tendencies. This
is may be because of extreme values in the data set. The mean is affected by the extreme values
whereas the median is not. The minimum value for the same is -1, which indicates decrease of 1
% from the previous year.
Histograms
The skewness of the variables in the data set has been measured by the histogram and the figures
are shown below.

For this variable, it is less skewed or the value of skewness is negative. The variable is not
normally distributed.

In case of this variable also the histogram suggests a left skewed data or most of the data points
are to the left of the average value.

In fact for all the continuous variables in the data set, the histogram are left skewed. None of the
variable shows the typical normal distribution.
Categorical variable
There are two types of categorical variables. The first one is the nominal where the order of the
category does not matter. The second one is the ordinal where the categories are in proper order.
In the current research all the variables are nominal. Results are discussed below.

8%
92%
Highest level of authority
senior manager
board
When asked about the highest level of authority, 92 % of the firms said that the board is the
highest level of authority which takes the matters of climate change and carbon emission.
10%
29%
62%
Internal price on carbon
Yes
No (but planning)
No plan
Similarly for 62 % of the firms there is no carbon price. Only 9 % of the firms have the internal
price on carbon and 29 % are planning to implement.

26%
57%
17%
Public influence on climate change
yes
direct engagment
others
Among the firms included in the data set, 57 % of the firms said that they are directly engaged to
promote the climate change for the public whereas 26 % said they are engaged but do not specify
how they are engaged(ACCA, 2009; Winn, Kirchgeorg, Griffiths, Linnenluecke, & Günther,
2011).
14%
86%
Risk management plan for future
3-6 years
4-6 years
The results from the future risk management shows that 86 % of the firms have plan for 4-6
years whereas 14 % of the firms have the risk management plan only for 3 to 6 years.

84%
16%
Are your products with low carbon
Yes
No
When asked whether the Coca Cola products are with low carbon emission, 84 % of firms said
that the product is produced with low carbon emission. The difference in the products in terms of
the carbon emission is may be because of the different rules and regulation for environment in
different countries.
Data analysis (inferential)
Chi square test
The chi square test is used to test whether there is statistically significant difference in the
continuous variable for the different categories. For chi square test it is important to have one
categorical variable. For the current case the chi square is used to test whether there is significant
different in the carbon emission of Coca Cola in different countries. Results from the chi square
test suggest that the value of chi square is not statistically significant, so the null hypothesis for
the chi square cannot be rejected. In other words, the carbon emission for Coca Cola is same in
all the countries.
Chi-Square Tests
Value df Asymp. Sig.
(2-sided)
Pearson Chi-
Square
4.454a 6 .616
Likelihood Ratio 4.908 6 .556
N of Valid Cases 34
a. 13 cells (92.9%) have expected count less than 5.
The minimum expected count is .35.

Correlation
The correlation analysis helps to analyze the relationship between the two variable on the basis
of the correlation coefficients. Variables are positively related if the correlation coefficient is
positive and the variables are negatively correlated if the correlation coefficient is negative. The
relationship between the dependent and the independent variables in the current case is also
examined on the basis of the correlation analysis and the results are presented in the table below.
Correlations
CC12.2 C6 CC1.1 CC2.2c CC3.2
CC12.2 C6
Pearson
Correlation
1 .013 .212 .053
Sig. (2-tailed) .943 .384 .785
N 34 34 19 29
CC1.1
Pearson
Correlation
.013 1 -.161 .144
Sig. (2-tailed) .943 .487 .441
N 34 36 21 31
CC2.2c
Pearson
Correlation
.212 -.161 1 .183
Sig. (2-tailed) .384 .487 .499
N 19 21 21 16
CC3.2
Pearson
Correlation
.053 .144 .183 1
Sig. (2-tailed) .785 .441 .499
N 29 31 16 31
The results shows that the dependent variable is positively correlated with all the independent
variables included in the study. However the correlation coefficients are not significant at 5 %
significance level(ACCA, 2009; Guo, 2014).
Regression analysis
The impact of the independent variables on the dependent variable is measured in by the
regression analysis and for the current case the results from the analysis are discussed below.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of
the Estimate
1 .500a .250 .025 .98176

a. Predictors: (Constant), CC3.2, CC1.1, CC2.2c
The model summary shows that the R squared is 0.25 which means that 25 % of the variation in
the dependent variable is due to the change in the independent variables included in the data set.
The rest of the variation is due to some other factors.
ANOVAa
Model Sum of
Squares
df Mean Square F Sig.
1
Regression 3.219 3 1.073 1.113 .389b
Residual 9.639 10 .964
Total 12.857 13
a. Dependent Variable: CC12.2 C6
b. Predictors: (Constant), CC3.2, CC1.1, CC2.2c
The ANOVA table shows the F statistic value, which decided whether the cumulative impact of
the independent variable on the dependent variable is significant or not. In this case the F statistic
is not statistically significant.
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) -5.048 3.392 -1.488 .168
CC1.1 .892 1.045 .240 .853 .413
CC2.2c .386 .373 .294 1.033 .326
CC3.2 1.108 1.045 .298 1.061 .314
a. Dependent Variable: CC12.2 C6
Finally the results for the correlation analysis is shown in the table above and the results show
that all the independent variables included in the data set have positive impact on the dependent
variable (carbon emission). This is because the regression coefficient is positive. However none
of the regression coefficients are statistically significant(Gasbarro & Pinkse, 2015; Winn et al.,
2011).
References

ACCA. (2009). High-impact Sectors: the Challenge of Reporting on Climate Change.
Bryman, A. and Bell, E. (2011). Business Research Methods 3e. Oxford University Press.
Gasbarro, F., & Pinkse, J. (2015). Corporate Adaptation Behaviour to Deal With Climate
Change: The Influence of Firm‐Specific Interpretations of Physical Climate Impacts.
Corporate Social Responsibility and Environmental Management, 23(3).
Guo, Y. (2014). Climate Change Disclosure : Determinants and impact. University of Hawai.
Saunders, M., Lewis, P., & Thornhill, A. (2009). Research methods for business students (5th
editio). Harlow: Pearson Education Limited.
Winn, M., Kirchgeorg, M., Griffiths, A., Linnenluecke, M. K., & Günther, E. (2011). Impacts
from climate change on organizations: a conceptual foundation. Business Strategy and the
Environment, 20(3), 157–173.