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This document contains preface to Business Statistics.
The order of the day in business is data. One can strongly say that today’s business is driven by data. Success of every business is enormously dependent on data. Good decisions are driven by data. In all aspects of our lives and importantly in the business context, an amazing diversity of data is available for inspection and analytical insight. Business Managers and professionals are increasingly required to justify decisions on the basis of data. They need statistical model-based decision support systems. Knowledge in business statistics enables one to intelligently collect, analyse and interpret data relevant to their decision-making. Statistical concepts and statistical thinking enable you to solve problems in a diversity of contexts besides adding substance to decisions and reducing guess work.
In the previous chapter, it was discussed how to enter data and simple formulae in excel sheet. Now let’s explore the numerical data through their properties. One important and basic property of numerical data is Measures of location. Measures of Location summarize a list of numbers by a “typical” value. The three most common measures of location are the mean, the median, and the mode is also known as Measures of Central Tendency. They are also classed as summary statistics. A fundamental task in many statistical analyses is to estimate a location parameter for the distribution; i.e. to find the central value that best describes the data.
The Measures of Association refers to the collection of statistical tool that measure the strength of relationship or association between two variables. In any business an analysis of data which includes two or more quantitative variable is quite common. Such type of analysis is needed to describe the numerical features of the association. Bivariate analysis is one of the simplest forms of quantitative (statistical) analysis which involves in describing the relationship between two variables. Covariance, Bivariate Correlation, Simple regression and Chi square are few techniques used to describe the Statistical association existing between two variables.
Decisions that are made with the help of data and information have a more reliable solution than otherwise. Most situations call for a sample for data collection. Let us understand testing of hypothesis in decision making through a simple example.
In the last chapter, we have discussed the ways of testing means for a large sample. The means can also be tested if the sample size is lesser than 30. A student’s T test is a versatile tool for testing means. The simplest form of experiment that can be done can adopt the use of T test.
Testing of Means discussed the possibility of comparing the equality of 2 means from the same population or from different populations. The tools studied in the previous chapters may not be applicable in case of 3 or more groups. When means of two or more populations are to be compared for equality, Analysis of Variance or ANOVA is used. ANOVA can be applied for Normal data, which is interval or ratio scale and the grouping variable is nominal data
Business situation often do not have data in normal distribution or may not fulfill the scale consideration. However testing hypothesis may be inevitable for decision making. Non-parametric test or Distribution free test can be adopted in the place of parametric test. Non parametric test like Mann-Whitney U test, chi square test, Wilcoxon rank sum test, Kruskal-Wallis test can be used for any type of data and nominal scales as well.
As mentioned in the previous chapter, we know that correlation is useful to determine the strength of the relationship between two variables. While regression can be used to predict the value of one variable through the known value of other variable to which it is related. This is one of the most commonly used techniques in the area of business research
Forecasting is predicting the future occurrence. Forecasts help us in many areas in decision making. It is the first step in production planning. Further, it helps in aggregate planning, capacity planning and manpower resource planning. Forecasting helps to keep inventories low and save costs.
Statistical Process Control (SPC) is a method of quality control which is applied in order to monitor and control a process. SPC is an analytical decision making tool which indicates whether a process is working correctly or not.
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