Statistical analysis of the count and profitability of air conditioners

This article presents the statistical analysis of the number and profitability of air conditioners in an Egyptian company. Checking the same distribution for each categorical variable has been made using Kruskal–Wallis test.


a b s t r a c t
This article presents the statistical analysis of the number and profitability of air conditioners in an Egyptian company. Checking the same distribution for each categorical variable has been made using Kruskal-Wallis test.
& 2018 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Experimental features
Observations on the number of air conditioners that sold in the company for six different types of air conditioners and its profits. Data source location The data was obtained from one of the air conditioner company in Egypt Data accessibility All the data are available this data article

Value of the data
Data are useful in calculating the appropriate quantities of each type of air conditioner. The data could be used as one of vital tools in assessing air conditioners companies competitiveness.
Data analysis can be useful in detecting more and fewer types of demand by consumers. Data can be useful in identifying the most profitable species in the organization. Data can be used to monitor compliance with the decisions and strategy necessary to determine the price of air conditioning.
Data can be expanded to include behavioral attitudes and customer preferences types of air conditioners.

Data
This is a simple data set that summarizes the performance of a small AC company who went out of business shortly after March 2013. Considering this is a small business that eventually failed. The data in this article represent 1058 units of air conditioner that sailed from July 2007 to March 2013 in an Egyptian company called Pure technology, we decomposed these units as The ISM frequency data on traditional vs. modern views is used, that found in Hunter and Takane [1], the data were as follows ( Table 1): The author collected the data from an Egyptian air conditioner Company called Pure Technology. Where we make the cases constrained (G) is: 1. Sex of the client (M ¼Male, F¼Female and C¼ company) 2. Cordon (the where that the client live) of the client (Y ¼Yes and N¼ No) 3. Season of the sale (summer, winter, autumn and spring).
In addition, the variables constrained (H) is: 1. 1.5 HP/b represent the air condition with power 1.5 horse and it is hot and cold 2. 2.25 HP/b represent the air condition with power 2.25 horse and it is hot and cold 3. 3HP/b represent the air condition with power 3 horse and it is hot and cold 4. 1.5 HP/c represent the air condition with power 1.5 horse and it is cold 5. 2.25 HP/c represent the air condition with power 2.25 horse and it is cold 6. 3 HP/c represent the air condition with power 3 horse and it is cold Moreover, the matrix G was as follows ( Table 2): The column constrained was making by combining between the power of the unit measuring by HP and kind of this unit (cold only or cold and hot) and the matrix H was as follows (Table 3): The H matrix represent combination between (1.5 HP, 2.25HP, 3HP) and the type of air conditioner (b, c). For example for the air conditioner, 1.5HP/b it takes 1 at the column 1.5HP and the column b. otherwise it takes 0 In addition, the next table indicate the profit of the sales units of air conditioner at different cases (Table 4).  Table 2 The cases constrained matrix G.   (The data represent the constrained that found in variables, we get it from Table 1).  Spring  G1  G2  G3  G4  G5  G6  G7  G8  G9   0  0  1  0  1  0  0  1  0  0  0  1  0  1  0  0  0  1 (The data represent the constrained that found in cases, we get it from Table 1) where:    Descriptive statistics was used to summarize the data and to provide plots for proper visualization and understanding. SPSS version 24 and Excel version 2013 were used for the analyses in this paper. The data set is summarized in Table 5.
The information in Table 5 shows that more people prefer the 3HP/c air conditioner that has the most sales of any other type of air conditioner. The type of air conditioner with the highest sold units is 3HP/c, although the number of users of this type of air conditioner is not the highest, but on average, customers purchased as many units of this type. This is reasonable because, in the true sense,  In addition, the boxplot representing the mean amount of sales in the various air conditioners types is displayed in Fig. 7.
The impact of the current air conditioner is also being identified in the plot provided in Fig. 7. The mean count in each air conditioner type with their respective 95% Confidence Interval (C.I) is displayed in Table 6.  The 95% confidence interval plot for the mean of the amount deposited in the various air conditioner types is displayed in Fig. 8.

Checking the normality distribution of the data
Kolmogorov-Smirnov test is used to check the normality distribution of the data. Where the null hypothesis refer to the count of air conditioner is distributed normally versus the alternative hypothesis that refer to the count of air conditioner is not distributed normally. Table 7 indicates the results as follows:   to the analysis of economic data such as in econometric models. The null hypothesis refer to the distribution is the same across the classified variable versus the alternative hypothesis, which assumed that the distribution is not the same across the classified variable. However, SPSS version 24 was used for the Kruskal-Wallis. Also, the level of significance used for all the analyses is 0.05. The result is displayed in Tables 8,  9 and 10.

Transparency document. Supporting information
Supplementary data associated with this article can be found in the online version at https://doi. org/10.1016/j.dib.2018.05.035.