UNIVERSITY of SOUTH AUSTRALIA 

Introduction

In quantitative methods a systematic empirical observation through statistical, mathematical and computational techniques are important components. Reliability of the data is important in quantitative methods. Data accuracy is affected by a variety of factors which range from the choice of the collection methods to biasness. Data is important in improving several aspects of business it is therefore imperative for any business to carry out quantitative research. The data provided in the appendices can is helpful in determining the relationships between and among variables. Discussions In appendix 1 the main aim of the analysis is to have a good representation of the values in a set. Correlated comparative values have been used for the purposes of coming up with a value or a set of values to choose from that can be used to the advantage of a firm. The repeated values in a sample help one to determine the more important values that are reliable and accurate. Therefore the different prices charged for different brands can be weighed to come up with the right price by comparing the values. There are three major and common ways of finding a reliable value in a data set.

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One of them is identifying the most recurrent value in a data set. One way is to identify the value that tends to recur or rather the repetitive value. The recurrent value in every brand is a reliable value that we can base our prices on. The second way is adding all the values or prices in every brand and dividing by the number of values added. This way we come up with a value that standardizes all the other values across the brands hence coming up with a standard prize for every brand. The standard value ensures that the company does not overcharge or undercharge thus maintaining the profitability gap. Again we can identify the center value in the data set hence arriving at a designated value that can be regarded as the price. This should be carried on all the brands available. Q-Casa can use the above methods to come up with a suitable price for their products.

Any unusual pricing identified which can be either over or under the anticipated pricing can therefore be ruled out. The reliability of the methods used is determined by how much the values arrived at deviates from the anticipated price or the prices among them that corresponds. A pricing bracket which is a notional range of prices that consumers are ready and willing to pay for can be determined from these methods. The values arrived at from each criteria form a range that the firm can choose from. In appendix 3 the analysis involved checking the cost of brands against their advertising costs. Finding an agreeable price that correlates with a reliable cost of advertising is the main objective of this analysis. A firm looking forward to making profits focuses on cutting down costs. It is therefore in the best interest of the company to consider a price that is not going to attract less costs of advertising. Finding a correlation between the price per listing and the cost of advertising can be made easier by using a scatter graph. The graph plots the price per listing on the horizontal axis while the cost of advertising is placed on the y axis. The numerical values representing the price per listing and the cost of advertising are then matched against each other as they were on the table using dots. This forms a scatter of dots on the graph that can be used to interpret data statistically. The dots tend to join or merge at a certain point. This meeting point where many dots tend to fall is the suitable area to place a reliable pricing that attracts a reasonable cost.

The measure of how close the dots are fitted on the same line is determinable using the scatter graph. It also shows the level at which both variables tend to converge which forms a point of agreement. This format of determining how close certain values of different variable might be correlated is reliable. By drawing a line across the point where the points converge can help us determine the suitable price and the reasonable cost. The average cost of advertising lies between $45 and $50 is reasonable. Considering the advantages of advertising the average cost would be insignificant when compared to the volumes that can be sold with the help of advertisements. The method cannot be reliably used for a listing priced at $300 because it would place the scatter dots of the correlation at a level that would not be regarded as suitable. This would mean doing away with the cost of advertising plotted against it.Appendix 4 analysis seeks to compare the prices of different brands on different countries.

The company needs to pick a combination that would attract more profits. This involves choosing a brand to export to a country and make a bigger profit margin from each one of the brands. Making a preferable combination of the brands and the countries would ensure the firm enjoys a comparative advantage over their competitors. The data enables us to place probabilities of prices of the different types of listings that the company deals with. Italy highly values the designer listing followed by the classic and tends not to consider much about luxury. Croatia similarly tends to value designer listing the most which is closely followed by the classic and luxury ends the list. Unlike other countries Greece highly regards luxury as compared to classic and Designer. Czech Republic and Hungary have less demand on the listing types offered by the company and this can be shown by the value they attach to the listings.Using 100% stacked column chart we can narrow down to determining the probabilities in terms of percentages. For instance we can easily identify by how much percentage that we approximate Italy would consider buying classic or designer over luxury.

Italy would consider designer by 40% and Classic by 32% against 28% of luxury whereas Greece would probably consider Luxury by 42% against designer 30% and Classic 28%.ConclusionIn conclusion, quantitative methods study is an important tool that is helpful in sorting data and interpreting it. Q-Casa can therefore use the details interpreted through critical analysis of the data provided to adopt with a profitable policy for the organization. A reliable source of data can ensure a reliable interpretation and hence influence suitable policies for the organization.

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