This statistical paper discusses about analysis of statistics for the retail on for January 2017. This paper also throws light on the description of data, data type, data sources as well as measurement scale. The paper also describes the concepts of probability, data presentation and the statistical interference.
The data that has been provided is been published and distributed in Common Bank Wealth of Australia. This source of data has been mentioned in the article. The source of data that has been provided gives us the idea of growth of retail trade in the cities of Australia.
The data that is provided in the form of graph is fundamentally a time series data and it has been collected for the time span of January 2011 to January 2017 for the percentage of change in the retail trade, January 2012 to January 2016 for the data of trend and annual percentage of change and June 2010 to 2016 for depicting the annual percentage of change in between goods and services.
In this data collection the author has followed the secondary data collection method. The collected data as well as the statistical study does not depend on the primary data as primary data incorporates questionnaire and interview of the first source (O’Leary, 2013). This data collection has been done from specified journal and after studying the journal or website the author has implemented those data in form of graph and theory. Therefore, it is a secondary data collection as the article contains graph and theoretical interpretations. Quantitative as well as Qualitative data analysis has been done in this article (Clark, 2013). The article contains graph and its interpretations both in numeric as well as theoretical studies. As the article contains numeric analysis therefore it is a Quantitative analysis and as it contains theoretical interpretations like achieving the link in between confidence, tourism and retail it is also a Qualitative analysis (Pierce & Swayer, 2013). The measurement scale is based on the time span, retail trade, and change in retail trade across the cities of Australia and difference of retail trade for goods and service. The variables of the measurement scale are been highlighted in the graphical interpretation.
Considering the level of measurement scale the scales are been categorized in four parts:
Nominal level: This scale is the considered to be the primary level of measurement. In this scale the presence of numbers are used for the data classification. This scale has no order as it only helps in labelling the variables.
Ordinal level: It is the second scale for measurement. It exhibits the relationship of orders in the variables which are been observed. However this scale has order present in it but those orders are not meaningful within interval in term of statistical measurement (Pedhazur & Schmelkin, 2013).
Interval level: It is considered to be third level of the scale for measurement. The data are been classified and the orders are meaningful in this level. It helps in relating the distance to the interval when plotted on graph. This scale is equivalent but no starting point is present in this level (DeVellis, 2016).
Ratio level: This is the forth and the ultimate level of scale for measurement. The observation in this level have equal and equivalent interval. This interval also consists of a true zero. As there is a true zero therefore, this scale has the true starting point. As the scale has true starting point therefore this level is unique from the other various levels.
The time span in this article is considered to be the nominal scale of measurement as the time variable that exists only classifies the data of time interval. Time span is also constant and therefore, it is rightly adjudged to be the nominal scale. The change in retail trade, change in retail trade across cities and difference of retail trade for goods and service are all in the ratio level scale of measurement. All these three are considered to be in ratio level because they all contain the true starting level and zero is present in the scale. Moreover, they have well defined interval and the orders are also been defined excellently. Therefore, presence of definite intervals with orders and the existence of true starting points make the variables to be present in ratio level scale of measurement.
In the given article the data has been summarised both in graphical form and theoretical studies. The theoretical study explains the linkage of confidence, tourism and retail in an excellent way. As the data has been presented both in graph as well as theoretical discussion therefore it is very much helpful to comprehend about the retail trade growth of Australia.
The source of the data provided for this article is both statistical inference as well as descriptive. Data is considered to be descriptive because it gives a links in between confidence, retail and tourism. Over there it is mentioned that the link present in between the confidence of consumer and retail is not that strong because the rise of retail is only a modest percentage of 3.1. Therefore the inference drawn from here is that the condition of retail is competitive as the customers are driven by sale. The link between tourism and retail shows that activity of retail increased heavily if tourism increases. The inference drawn over here is that number of hotels should be increased and bigger airports are needed for the increase in retail trade turnover. The other statistical inference that are also been exhibited are the rise in retail by 0.4% in January. This increase in retail trade is mainly due to the contribution of households, furniture, hardware and electrical goods. But there is a fall in the departmental stores of clothing and food.
The location measured in this article is the cities of Australia. The article clearly shows the data provided is of Australia for the last six years. In this article the variance measured is in the variation of retail trade, change in retail trade across the cities of Australia and difference of retail trade for goods and service.
The probability concepts determines that if the tourism is not improving then the retail sector will depend on the household goods, furniture and electrical goods for increasing their sales. The food and the clothing department will soon going to face major crisis (DeFusco et al., 2015).
The author has nicely used the comparison of variance over here so there is no requirement for any recommendations as the graphs used over here are also having defined intervals.
Conclusively it can be said after reviewing the article that the department of food and cloth are going face major upset in terms of increasing their sales. Their increase of sales largely depends on the increase of tourism. Therefore, the main dependent source of increasing the growth of retail sector is household goods.
Clark, G. (2013). 5 Secondary data. Methods in Statistics, 57.
DeFusco, R. A., McLeavey, D. W., Pinto, J. E., Runkle, D. E., & Anson, M. J. (2015). Quantitative investment analysis. John Wiley & Sons.
DeVellis, R. F. (2016). Scale development: Theory and applications (Vol. 26). Sage publications.
O'Leary, Z. (2013). The essential guide to doing your research project. Sage.
Pedhazur, E. J., & Schmelkin, L. P. (2013). Measurement, design, and analysis: An integrated approach. Psychology Press.
Pierce, W. C., & Sawyer, D. T. (2013). Quantitative analysis. John Wiley And Sons, Inc; London; Toppon Company, Ltd; Japan.
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