One of the applications of descriptive statistics is in grouping of large sets of data for easy understanding of trend. The case of Diamler that includes transactions in large numbers of vehicles for instance requires summaries that group sets of data for clarity. Frequency distribution tables and histograms groups data and illustrate frequencies of each group of data. Yearly demand for a particular brand of vehicle that can be grouped by seasons or months allows the company to determine the trends in seasonality of demands for its products. As a producer, frequency distribution tables, graphs and histograms helps the company to predict trend and hence determine its production capacity. This facilitates on time demand production and help to save costs of storing stock. Similarly, frequency distribution by geographical markets helps the company to understand the market capacity. This is useful in making decisions over distribution of the company’s products in its markets. These analyses help the company to make informed decision of its trend in supply (Ross, p. 10-17. Daimler, p. 1).
Measures of central tendencies are other descriptive statistics that facilitates business ‘decision-making’ processes. The mean, for example, illustrates the average values recorded over a subject in a given period. Its application in Daimler is realized through average demand for different vehicle brands across seasons. Among other factors, understanding mean distribution facilitates decisions on production capacity in order to meet the market’s demand without underproduction or underproduction. The mode, another descriptive statistics, defines the highest frequency in grouped data. The company uses it to identify peak seasons and locations for demand of each of its vehicle brands. This helps the company to maximize on its market by availing sufficient stock in its markets (Mimmack and Meyer, p. 10- 19).