Critical Analysis of Data Issues in Aviation Industry - Report

This report provides a critical analysis of data analysis techniques, specifically focusing on correlation, regression, and time series methods within the context of the aviation industry. The introduction highlights the significance of data analysis in making informed business decisions, emphasizing its role in inspecting, cleansing, and transforming data to extract useful insights. The analysis delves into the application of correlation for identifying relationships between data sets, regression for forecasting and understanding variable relationships, and time series analysis for predicting future trends. The report offers examples from the aviation sector, demonstrating how these techniques aid in predicting passenger expectations, determining sales, and ensuring safe travel. It also addresses the limitations and challenges associated with each method, such as the potential for incorrect data entry, the need for expert knowledge in time series analysis, and the importance of selecting the right model. The conclusion underscores the effectiveness of these tools in improving business operations and decision-making, ultimately enhancing sales and overall company performance. References to academic journals and online resources support the analysis.

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Table Of Content
 Introduction
 Critical analysis of the issue within Correlation
 Critical analysis of the issue within Regression
 Critical analysis of the issue within time series
 Conclusion
 References

Introduction
 Data analysis is the basic term used or inspecting, cleansing and transforming the data
with an aim to discover the useful information so that business will make better decision
in their future.
 The present PPT is based upon the critically analysis of issue involved in regression,
correlation and time series.
 Further, the evaluation is undertaken with the reference of Aviation industry and how it
helps to make better decision for the welfare of a firm.

Critical analysis of the issue within
Correlation
 Correlation is used to identify the relationship
between the data sets in a business and such
type of analysis is used in financial analysis,
along with supporting business decision
making.
 With the help of such analysis, business leaders
makes better predictions based upon the pattern
in data sets.

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 For example, to identify the changing
expectation of passengers in the industry uses
such methods to forecast customer’s needs
and then offer the product accordingly.
 On the other side, if analysts did not find any
relationship, it means that the data set is
wrongly entered, however there could be a
non-linear relationship, but it does not mean
that there is no relationship.

Critical analysis of the issue within
Regression
 This is used for small business which helps to determine the factor which are
generally ignores by the data analyst. Further, regression analysis is considered the
biggest used statistical method that allows the business to examine the relationship
between two or more variables.
 The biggest advantage of using this tool is for forecasting and derives the
relationship between two more variables. For example, sales depend upon profit
within the Aviation industry.

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 In the context of aviation industry, it can be
stated that industry identify the customer
service calls which can be dropped within last
year. So that can be stated that with the help of
this analysis, companies are able to determine
the sales and future changes.

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 Also, it is analyzed that aviation industry also
ensure the safe and secure way of travel and to
provide effective services, analyst perform
regression analysis which assist to predict the
overall sales within a business.

Critical analysis of the issue within time
series
 Time series is another important business
analysis tool which helps to predict the future
of sales and turnover. It also allow
management to examine timely pattern in data
and determine the trend within business
metrics.

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 However, on the critically note it can be analyzed that the tool is not easy to use and for
that experts will be hired which is consider costly method for the industry. Apart from this,
it is also analyzed that there is a problem with accurately identifying the correct model to
identify the data. This in turn causes wrong results within a business. That is why, business
who have experts only uses such technique because it creates positive results otherwise lead
to wrong outcome.

Conclusion
 By summing up above it has been concluded that such tools assist the business to operate
in an effective manner and also make effective decision for the welfare of a firm.
 Through correlation and regression, business analyst may easily predict the sales and
improve the overall performance of a company. Therefore, such analysis assists to solve
the issue of sales and profit.

Reference
Books and Journals
 Aggarwal, R. and Ranganathan, P., 2017. Common pitfalls in statistical analysis: Linear
regression analysis. Perspectives in clinical research. 8(2). p.100.
 Gamboa, J.C.B., 2017. Deep learning for time-series analysis. arXiv preprint
arXiv:1701.01887.
 Mills, T.C., 2019. Applied time series analysis: A practical guide to modeling and forecasting.
Academic press.
 Syazali, M. and et.al., 2019. Retracted: Partial correlation analysis using multiple linear
regression: Impact on business environment of digital marketing interest in the era of industrial
revolution 4.0. Management Science Letters. 9(11). pp.1875-1886

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 Zheng, J. and et.al., 2017. LD Hub: a centralized database and web interface to perform LD
score regression that maximizes the potential of summary level GWAS data for SNP heritability
and genetic correlation analysis. Bioinformatics. 33(2). pp.272-279.
Online
 The advantage of regression analysis and forecasting. 2020. [Online]. Available through: <
https://smallbusiness.chron.com/advantages-regression-analysis-forecasting-61800.html>.