Posted: July 8th, 2015

# The analysis takes a stance from the first quarter of 2010 to last quarter of 2014 as indicated in the attached spread sheet. Extracted non-seasonally adjusted quarterly data for tourism

Analysis of Economic and Social Data

Question 2:

In most instances, economic and social data given are non-adjusted. It is remarkable to note that the role of economics & social statistics is to ensure that all the household consumption and national economic data on consumers and economic behavior are curbed. Seasonal adjustments are meant to ensure that the availed data is standardized and comprehends to the standards and statistical norms to curb biasness. As provided in the seasonal adjustments of the USA, it is vital to record that unemployment is a challenge to most of the economies. some economies are tirelessly making attempts to ensure that they reduce the rates of unemployment by engaging all the economic sectors and all industries in bettered productivity. Such measures are aimed atjob provision and boost of GDP. It is for such reason that the USA data is vital and subjected to seasonal adjustments to give clear picture to the Canadian seasonal adjustments with an example of tourism industry. The analysis takes a stance from the first quarter of 2010 to last quarter of 2014 as indicated in the attached spread sheet.

1. Extracted non-seasonally adjusted quarterly data for tourism:

The data has been presented on spreadsheet

1. Plotted points from the spreadsheet of part b

A graph of non-seasonally adjusted quarterly data for tourism in Canada for 5 years (2010-2014)

From the graph, there exists seasonal effect that is immense. The user of the data provided can establish that the graph is hard to understand and needs more analyses by statisticians. At such point the aspects of smoothening comes into existence in a time series model. As depicted by the graph, there is a trend in tourism industry quarterly. It indicates that the time series has a seasonal trend and most of the tourists visit the sites at some points and they reduce immensely to the trough at some point. Such introduces the cyclic trend in tourism sector. To normalize the condition it requires that smoothening of the time series be done.

d & e. Seasonally adjusted version for the tourism demand series:

It can be best established by basing the illustrations on the trend as the basic start point. It helps check the time that the preconditions for instability are bound to last, for example whether staple over time. It provides that the time series should be disaggregated to cater for long time or sudden eminent changes. The analysis can be done and adjustments made graphically, using semi-averages approach, by use of regression (least square method), and finally moving averages method. Moving averages is ideal with curved graphs. It is made possible by the fact that the graph is drawn from averages made from the four dependent variables of the four quarters. The essence of averaging is to curb the large variations noted. With the four quarters it will be made possible by summing the first four figures and averaging and the mode continues to the last four values yield an average (mylcc.co.uk). It is indicated in excel

Reference

mylcc.co.uk. (n. d). quantitative methods for business and management. Retrieved 6th July 2015 from http://www.mylcc.co.uk/studymanuals/l5/L5_QM4BM.pdf

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