Business Decision Making Project Part 2

TrayQuan Hawkins

May 29, 2017

 

 

 

 

 

 

 

 

 

Statistical Analysis

Any data collected concerning a particular field of study needs to be adequately analyzed so as to come up with deductive information. The data can be presented in a particular format, leaving the analyst at the discretion of choosing between varieties of either the descriptive or inferential statistical methods of data analysis. Each of these aspects is capable of producing results similar to the other when seeking a common thing of interest. The primary difference is that each of the methods of analysis focuses on mainly a unique value of the data, applying a formula only applicable to such a sector (Investopedia, 2017).

If data were collected related to the K-Mart firm, the following types of descriptive statistics would be used for making a summary of that data. The types include mode, standard deviations, mean and median. Others such as kurtosis and skewness are still very useful though not very common in the calculations. Descriptive statistics have been crucial in understanding the nature of the data set under study. Using these methods, the hard-to- understand quantities are broken down into easy and understandable and useable bite-sized descriptions (Trochim, 2006). The large data is brought together from the full range and averaged to produce a simple and understandable overall reflection of the situation.

All the types of descriptive statistics are classified into two broad categories. First is the measure of central tendency. This class consists of the mean, mode and the median. On the other hand, a measure of variability which is the second broad category includes variance or standard deviation, maximum and minimum variables, skewness and the kurtosis. With these descriptive statistics, there is an adverse use of tables, general discussions and graphs to aid in promoting overall understanding of the analyzed data set. Each of the categories has a concept that they bring to light which is unique from the other measures.

First, the measures of central tendency are useful in describing the core positions regarding the data set. Through the median, mean or mode, analysis can be made for the data set to establish the most occurring and recurrent data patterns. Such information is then analyzed to provide the feedback that is needed for the firm and provide the position of the company under such a circumstance. The measure of spread, which is variability, analyses the nature of data spread-out. The statistics offer the range in which the set of data can be relevant to the situation being analyzed. The shape and spread of the data are communicated through a range, absolute deviation, quartiles, and variance (Trochim, 2006).

Apart from descriptive statistics, the company can use the inferential statistics option for their data analysis if they happened to collect any data. This method involves comparing the different information presented in the dataset. The researcher uses this method to compare the different groups of data and in the process come up with various and most probable predictions (Labs, 2017). This category of analysis moves beyond the simple characterization, and description of the data presented and aims at drawing conclusions as per the current data collected. Such findings are then used to come up with strategies to solve an impending challenge faced by the organization.

Inferential statistics of data analysis involves two broad categories. The first is the t-test. This is a simple analysis for comparing the data means. The category is further divided into one-sample t-test, dependent-samples also called the paired-samples t-test and the independent-sample t-test. The means from different data sets are used for analysis whereby the difference between these means is then divided by a particular variance measure to provide the information required. A one-sample t-test is used on a known sample population. The conditions for its use are first, having one data set or one mean of interest, and second, when the average for the entire population to be compared is known (Labs, 2017).

When using the independent-samples t-test, a comparison between two separate and non-related samples is made. The condition for using this inferential method is that the data set being analyzed differently from each other. Also, the information in the data set should not be overlapping. The groups which provided the data should be different, not a single group. Dependent samples t-test is used for comparing data set from either related groups or same category over a specified period of time. The condition for its use is having some data provided by the same team of researchers but over a period.

The second broad category in the inferential statistics is the analysis of variance (ANOVA). This is an analysis of means. The effectiveness of the group is that it can analyses and compare several means at the same time. Through this analysis, the effects of different factors of the same measure are also determined (Labs, 2017). There is some complication when using the formula thus the method is suitable for expert analysts to avoid errors who are trained to deal with statistics. ANOVAs are of different types.

The first, category is a one-way ANOVA. This class involves a comparison of three or more levels of a similar dimension. The others include the within-groups or repeated measures, and factorial ANOVA. The third portion of inferential statistics is the regression analysis. This method involves making predictions about various outcomes by some predictor variables (Labs, 2017). Data from the inclusive variables are collected to enable the determination of the contribution of the predictor variable about the outcome. This produces the regression model through which a prediction can be derived by inputting the individual data to the predictor variable.

Performing a trend analysis is one significant step in determining the accuracy of the data provided, especially to the accounts. The importance of this study includes showing the trend and cycles of the times when expenditure and income are high (Accounts, 2017). The other function is the determination of the due accounts payable and offers some assistance in making of cash flow projections. Also, trend analysis provides an understanding of the efficiency of the company management of the accounts payable as well as availing timely payment discounts.

Other transactions that are not part of accounts payable are re-classed to their accurate accounts in the monthly entries. Finally, the analysis helps in identifying vendors through research to check that business terms turn to be favorable and that purchase team has no derivation of personal benefits from the company purchase costs (Accounts, 2017).

 

 

 

 

 

 

 

References

Accounts, A. (2017). The Importance of Trend Analysis. Advanced Accounts .

Investopedia. (2017). Descriptive Statistics. Investopedia Academy .

Labs, C. (2017). Inferential Statistics. Baseline Help Center .

Trochim, W. M. (2006). Descriptive Statistics. Research Methods Knowledge Base .

 

 

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