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Essentials of Statistics for Business and Economics

Statistics plays a key role in the decision making process in all successful businesses. Accuracy in decision making is the word for any entrepreneur that wishes to succeed or is already successful. He/she must know their market and its need for the company’s product and deduce from that the right course of action to take. This feel of the market can be obtained from statistics. Statistics go a long way in indicating what measures need to be taken where in the enterprise; for example aligning production according to the needs of the market by taking the volume of sales into consideration. Business managers everywhere utilise statistical data to guide them make decisions on a day to day basis. This article illustrates how basic statistical concepts do go a long way in catalysing business and economics.

Data and Statistics

In the topic ‘Data and Statistics’, the importance of statistical information is studied. This is essential as it enables the learner to get a glimpse of the subject matter in action and allows them to appreciate its full potential. This in turn provides a basis from which the learner can begin to be teachable on the variety of fundamental concepts in the art and science of statistics.

Statistics is applied in various areas of business and economics. For example accounting: statistical sampling procedures are used when conducting audits. Audit staff select a subset of the financial documents being audited sample and conclude on the accuracy of the whole depending on the performance from the sample; finance, where analysts study financial data to guide investment recommendations such as price/earnings ratios and dividend yields to determine under or overpricing and make a buy, sell or hold advisement, and manufacturing: here manufacturers make use of data from point of scale data scanners from retail outlets to conduct analyses that guide future marketing practises for their various products and quality control in production where, the x-bar chart can be cited in use to monitor the output of a production process and when properly interpreted, decisions can be made as to whether the production process requires adjustment or not. Economists also use statistical information and make forecasts about the future of the economy or a part of the economy such as inflation rates.

In the same topic, ‘Data and Statistics’, a number of terms are defined and are key if the subject of statistics is to be understood. The terms defined are data (facts and figures processed and ready for presentation and interpretation). All the data in a study is the data set for that particular study. Elements are also defined; they are the subject of the data set. Variables are noteworthy pieces of information on the elements and observations are the measurements for an element.

Another concept is that of the scales of measurement. They tell the amount of information that is in the data and gives the user an idea of the most suitable data summarisation and statistical analyses. These scales of measurement include the nominal scale which has names identifying the attribute of an element; the ordinal scale which is when data is as in a nominal scale but in a meaningful order, an interval scale if data has ordinal scale properties and a fixed unit of measure is the interval between the values; and the ratio scale where the properties of data is as in interval scale and two values have a meaningful ratio. Adding functionality to statistical information is the classification of data as either categorical or quantitative; the only difference being the scale of measurement used and thus categorical variables and quantitative variables.

Descriptive Statistics: Numerical Measures

Other than using graphs and tables, another way to present statistical information is using numerical measures. Unlike the former, they have a set method to in their use and therefore more convenient when contrasting data sets. Mean and median are measures of location specifically measures of centre of data although mean is a more calculated centre while median is a sorted value that is at the middle. Mode also gives a measure of location and gives the most frequent observation. These measures are inclusive also of percentiles: data over a range, quarterlies that is breaking data into at every twenty-fifth percentile. The variability in data is another numerical measure.

Measures of variability are range that is the difference in the maximum and minimum values of data in a set; interquartile range, the difference in values not maximum or minimum and variance- distance of a data point from the mean or the deviation about the mean an standard deviation. Standard deviation is in the same units as the measurement which allows us to compare statistics directly. The measures of location and variability allow us to measure the location of individual observations from others in the same set. The empirical rules allow us to determine the data that is most likely to lie within any number of standard deviations.

In descriptive statistics, summarising data using simple math is the process of exploratory data analysis. The five number summary is used in this process and includes minimum value; first quartile; median, third quartile; and maximum value which is represented by a box plot.

Introduction to Probability

The topic introduction to probability discusses the numerical measure of the chance that an occurrence will take place. More often than not, managers base their decisions on the premise that a certain occurrence will most likely not take place thus making the determination of probability in the art and science of statistics of importance. Calculating probability is akin to an experiment where one outcome or the other is certain to occur if the other fails. Calculating probability is based on the ability to identify and count the likely outcomes of one event relative to the other occurring or not occurring thus the counting rules for multiple step experiments, combinations and permutations.

In the calculation of any probability, all outcomes of an experiment complement one another and only one of the outcomes must occur thus the basic relationship of an event that is the complement of an event. Another relationship of probability is the addition law where the probability of two events occurring implies all the immediately related events must be added to find out the probability. There is also the premise of conditional probability that means one event must occur before another one is able to occur. For instances where additional information is available, Bayes’ theorem is used to get the information that is received afterwards.

In conclusion, the three topics discussed herein above clearly show the relationship between statistical information and business and economics. In the topic data and statistics, the concepts therein are important in identifying and understanding statistics as a basis for obtaining information for decision making used in the highest echelons of business and economics. In descriptive statistics, data is processed and presented in a way that is understandable and further provides a basis for decision making in the nosiness environment; and in the topic of probability, we have seen that managers often use the likelihood of certain events not occurring to make management decisions .we have seen ho probability may be assigned and related laws.


(n.d.). Retrieved October 26, 2015, from


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