GB513M1: Chart Analysis Report on Business Data and Solutions

This document provides a comprehensive solution to the GB513M1 assignment, focusing on business chart analysis and data interpretation. The assignment includes the creation and analysis of a pie chart and bar graph illustrating domestic boarding data for top US airlines. It also involves the calculation and interpretation of descriptive statistics, including mean, standard error, median, standard deviation, variance, kurtosis, skewness, range, minimum, maximum, and sum, applied to nonfuel mineral production data. Furthermore, the solution presents a histogram analyzing bar soap sales, interpreting sales trends and volumes. The report emphasizes the importance of these visualizations in business decision-making, offering insights into sales patterns, data distribution, and potential outliers, all presented in a structured Word document format as required by the assignment brief.

Running head: GB513M1 CHART BUSINESS SOLUTION 1
GB513M1: Chart Business Solution
Name
Institution
Date

GB513M1 CHART BUSINESS SOLUTION 2
GB513M1: Chart Business Solution
Question 1
Pie Chart
81.1
79.4
72.6
56.3
43.3
37.8 31.5
Domesti c Boarding (Millions)
Southwest Airlines Delta Airlines American Airlines United Airlines
Northwest Airlines US Airways Continental Airlines
Figure 1: Pie chart of Domestic Boarding in a Recent Year (Top 7 Airlines in the USA)
Bar Graph
Domestic Boarding (Millions)
0
10
20
30
40
50
60
70
80
90
Domestic Boarding in a Recent Year
Southwest Airlines Delta Airlines American Airlines United Airlines
Northwest Airlines US Airways Continental Airlines
Airlines
Domestic Boarding (Millions)
Figure 2: Domestic Boarding in a Recent Year (Top 7 Airlines in the USA)

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Question 2
a) Descriptive Statistics
Summary Statistics
Value
($billions)
Mean 3.61
Standard Error 0.50
Median 2.98
Standard Deviation 1.92
Sample Variance 3.70
Kurtosis 3.58
Skewness 1.78
Range 7.27
Minimum 1.68
Maximum 8.95
Sum 54.2
Count 15
Table 1: A summary Statistics of nonfuel mineral Production
b) Definitions
i) Mean is a measure of central tendency that shows the average value of the
data that is determined by summing up all values and dividing by the number
of values.
ii) The standard error is a measure of dispersion that refers to the estimated
standard
deviation of the sampling distribution. It measures the variation of the sample
about the mean of the population.
iii) Median is a measure of central tendency that shows the 50th percentile or
middle value of a sorted data.
iv) Standard Deviation is a measure of dispersion that determines the average
distance between an individual observation and the mean of the data.

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v) Sample Variance is a measure of dispersion that shows the average of the
squared deviations from the sample mean.
vi) Kurtosis measures the ‘heavy-tailed ness’, or ‘light-tailedness’ of the data in
relation to the normal distribution. Accordingly, higher values of kurtosis
suggest heavy tails or the presence of outliers, and vice versa (Westfall,
20154).
vii) Skewness is the measure of the direction and degree of asymmetry in the
distribution of the data (Vetter, 2017).
viii) The range is the difference between the maximum and the minimum data
value.
The minimum is the least data value.
ix) Maximum is the largest data value.
x) The sum is the total of data values
xi) The count is the number of available (non-missing) data values
c) Comment on Descriptive Statistics
Table 1 indicates that the average value of nonfuel mineral production in the US in
2008 among the 15 leading states is $3.61 billion. Given that the standard error of the data
is $0.5 billion (a small standard error), there is less spread in the sampling distribution
(Kaliyadan & Kulkarni, 2019). The median of the production value is $2.98 billion. Since
the mean is more than the median, it implies that the data is asymmetrical and skewed to
the right. As shown in Table 1, the standard deviation and variance of the data are huge
relative to the mean. Besides, high values of standard deviation and variance indicating
high variability in the data, they suggest the presence of outliers in the data. The kurtosis
of the data is 3.58. Since the kurtosis is greater than 3, the dataset has heavier tails than a

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normal distribution (Kaur & Stoltzfus, 2018). Therefore, the data assumes a leptokurtic
distribution and confirms the presence of large outliers suggested by standard deviation
and variance (Chissom, 2012). Data’s skewness is 1.78. Similarly, being a positive value,
the value of skewness confirms the right skewness of the data confirmed by the
relationship between the mean and the median as highlighted above (Cain & Yuan, 2016).
Accordingly, median, mean, standard deviation, variance, and skewness indicate that the
data is asymmetric, leptokurtic, skewed to the right, has outliers, and does not follow a
normal distribution.
The difference between the highest mineral producing leading state ($8.95 billion)
and the lowest producing leading state ($1.68 billion) in the nonfuel mineral production is
$7.27 billion. As intimated by the sum of the mineral production, the total nonfuel
mineral production in 2008 in the US among the leading states was $54.2 billion. The
total number of states examined was 15 as indicated by the number of states.
Question 3
a) Histogram
10 15 20 25 30 35 40 More0
14
8
19
8
2 1 0
Histogram
Bins
Frequency
Figure 3: A histogram of Sales of bars per Week

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b) Interpretation of Figure 3:
Figure 3 summarizes the value of bar soap sales in millions. The histogram shows that
in the year under consideration, 19 weeks had sales ranging between 20 and 25 million.
This accounted for the highest sales volume in the year. Besides, Figure 3 demonstrates
that no week had sales between 0 and 10 million bars of soap. This implies that even the
week with the lowest sales had at least 10 million bars of soap sold. Only one week had
sales of between 35 and 40 million. Accordingly, the information provided by Figure 3
could be used by Procter & Gamble to find out the reason behind the high sales and
promote them in the future. Besides, the weeks with low sales can be identified to avoid
overproduction.

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References
BIBLIOGRAPHY Cain, M. K., & Yuan, K.-H. (2016). Univariate and multivariate skewness and kurtosis
for measuring nonnormality: Prevalence, influence, and estimation. Behavior Research
Methods, 49(5), 1716–173.
Chissom, B. S. (2012, March 12). Interpretation of the Kurtosis Statistic. Retrieved from
Tandfonline: https://www.tandfonline.com/doi/abs/10.1080/00031305.1970.10477202
Kaliyadan, F., & Kulkarni, V. (2019). Types of Variables, Descriptive Statistics, and Sample
Size. Indian Dermatology Online Journal, 82–86.
Kaur, P., & Stoltzfus, J. (2018). Descriptive statistics. International Journal of Academic
Medicine, 4(11), 60-63. Retrieved from http://www.ijam-web.org/article.asp?
issn=2455-5568;year=2018;volume=4;issue=1;spage=60;epage=63;aulast=Kaur
Vetter, T. (2017, November 12). Descriptive Statistics: Reporting the Answers to the 5 Basic
Questions of Who, What, Why, When, Where, and a Sixth, So What? Retrieved from
LWW: https://journals.lww.com/anesthesia-analgesia/fulltext/2017/11000/
Descriptive_Statistics___Reporting_the_Answers_to.48.aspx
Westfall, P. H. (20154, August 11). Kurtosis as Peakedness, 1905 – 2014. R.I.P. Retrieved
from NCBI: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4321753/#