Pages: 17

Understanding real-world decision dilemmas is covered in the first section of this chapter. The recognition of the decision support system as a tool for decision-making is presented in the next section. The various decisions that businesses make are described in this section. Next, business statistics and their application to businesses are provided. The research is divided into three categories: exploratory, conclusive, and modelling, each of which is described with an example. The steps that make up the research process are listed next in clear order. Before beginning to read this chapter, many statistical tools are briefly described because it is about business statistics utilising Excel.

Pages: 52

The layout of an Excel sheet is described at the beginning of this chapter. The Excel sheet’s editing options are then presented. Next, examples are used to show how to employ formulas in Excel operations. The formatting in Excel is then explained as a key feature. Next, examples are used to illustrate how @SUM is used. The user will frequently need to copy the formula to various cells, as seen in the following section. Then, examples of the SUMIF and SUMIFS syntax are given. Examples are used to teach the syntax for COMBIN and FACT. Utilising appropriate examples, the uses of the MAX, MIN, MAXIFS, and MINIFS functions are demonstrated. To analyse data, the ROUND, ROUNDUP, ROUNDDOWN, and INT functions are provided. Using suitable examples, the use of the RANK.AVG and RANK.EQ functions is shown. The implementation of the Hide and Unhide commands for Excel sheets’ columns and rows is then shown. The PROB function and filter function are seen in action at the conclusion.

Pages: 34

The syntax of COUNT is explained at the beginning of this chapter with an appropriate example. Following that, a comparison of the COUNT and COUNTA functions is given along with an example. Missing data will be present in survey results, and the COUNTBLANK function is used to analyse them. The COUNTIF function is used to count the cells in an Excel sheet according to a set of criteria. COUNTIFS is used to handle the case where more than one criterion needs to be used to count the cells in Excel. The construction of frequencies for a given collection of data in an Excel sheet is handled by the frequency function. A histogram is a visual depiction of a set of data’s frequencies at certain discrete data points. An example utilising the histogram function is used to show this.

Pages: 40

The average of a given set of data is a crucial business strategy. This chapter’s introduction uses a realistic business example to illustrate the syntax of the AVERAGE function. Following the presentation of the differences between the AVERAGE function and AVEDEV function, the AVEDEV function is demonstrated using an example. The AVERAGEA function for various business applications is then demonstrated. Using a user-generated Excel sheet, the weighted average of a given collection of data is discussed. The mean of the grouped data is computed at the end utilising an Excel sheet created by the user.

Pages: 35

The first section of this chapter explains the median, which is the midpoint observation in a set of data. The process for calculating the median is then described. Grouped data are computed differently than ungrouped data. Additionally, the approach for calculating the group of the data’s median is described. A measure of central tendency is the mode. The maximum frequency of a given set of data (observations) is its mode. The method of computing the mode of grouped data with an example using an Excel function is described after the computation of the mode of ungrouped data using that function. Excel functions are used to illustrate percentile, which is defined as a value in the provided range of values so that a specified percentage of observations fall below that value. A random variable’s quartile value is its position in relation to one of five percentiles: the minimum value, the 25th percentile, the 50th percentile, the 75th percentile, and the maximum value of the total frequency. Excel functions are used to demonstrate this using examples.

Pages: 50

The topic of how a spread functions when the means of two samples are the same is introduced in this chapter. Useful examples are used to illustrate how to use the range and coefficient of range functions in Excel. The idea of average deviation is then introduced, along with a demonstration utilising an Excel function. The standard deviation is defined, followed by illustrations of the STDEV.P and STDEV.S functions. The calculation of the standard deviation for grouped data is demonstrated using a user-defined Excel sheet. An illustration of the STDEVA function, which calculates the standard deviation of the observations in a sample of a population that includes logical values and text, is given. An example is used to illustrate the STDEVPA function, which determines the standard deviation of the observations of the entire population, including logical values and text from the STDEVA function. The computation of the coefficient of variation using a custom Excel sheet is shown at the end.

Pages: 29

First introduced in this chapter is the idea of skewness and its significance. Next, a business example is used to show the approach to data analysis for ungrouped data based on Karl Pearson’s coefficient of skewness. The steps to analyse grouped data using Pearson’s coefficient of skewness are then listed. An example is used to demonstrate how to calculate Bowley’s coefficient of skewness for analysing ungrouped data. The method for applying Bowley’s coefficient of skewness to analyse grouped data for a specific set of data is then presented. A section to analyse kurtosis for various types of data is presented towards the conclusion.

Pages: 37

This chapter starts with an Excel analysis of a binomial distribution. Then, a Poisson distribution using Excel is used to demonstrate the various applications of Poisson distributions. The following section describes the domains in which exponential distributions are used, along with their theory and application to real-world situations using Excel. The breadth of normal distribution, standard normal distribution, and related theories for practical application using Excel are covered in the section that follows. A distribution that is frequently utilised is uniform distribution. Its practical applications utilising Excel are shown. Using business applications, the scope of the t distribution and its theory for practical Excel implementation are demonstrated. The methods for calculating confidence intervals for large and small samples using Excel commands are described at the conclusion.

Pages: 30

This chapter starts with an analysis of the normal distribution, which is the sampling distribution of the mean when the population is infinite. A normal distribution with a finite population multiplier in the variance is used in the theory of a sampling distribution of the mean when the population is finite. The range of the t distribution’s sampling of the mean and its practical application are then covered when the normal population variance is unknown. The next part explains the chi-square distribution and how it applies to a sampling distribution. The study of the F distribution is presented at the conclusion, along with its extension to the sampling distribution.

Pages: 48

A hypothesis is an assumption about a population, and the significance of a hypothesis in sampling is discussed first. The tests for single means, which uses Z sampling statistics, are used to analyse real-world issues when the population’s mean and variance are known and its size is finite. The procedure of utilising Z sampling statistics to test hypotheses about differences between two means when population variances are known and sample sizes are large is covered in the section that follows. It is shown how to use t tests to test single-mean hypotheses when the population’s variance is unknown and the sample size is limited. The section that follows describes how to use t tests to analyse real-world situations involving the difference between two means when the variances of the population are unknown and equal and the sample size is small. The study of real-world issues is presented in the next section utilising t tests to compare two means when population variances are unknown and uneven and sample sizes are small. Research assessing real-world problem hypotheses using paired t tests is described at the end.

Pages: 19

Beginning with a business scenario, this chapter applies the chi-square test to determine the independence of categorical data. To determine if a particular distribution resembles and fits an assumed standard distribution, the goodness of fit test is performed. The data analysis in the following section uses the goodness of fit test to fit distributions. Following that, a method for using the goodness-of-fit test to fit uniform distribution is described. A section that examines the fitting of a distribution for the Poisson distribution follows. It is followed by using the goodness of fit test for exponential distribution. In real life, the normal distribution is frequently used. The analysis of fitting a specific distribution for a normal distribution is demonstrated at the end.

Pages: 67

The difference between a parametric test and a non-parametric test is discussed at the beginning of this chapter. The study of problems using a one-tailed, one-sample sign test when the sample size is small is presented in the following section. It is followed by the procedure for evaluating the two-tailed one-sample sign test when the sample size is small. When the sample size is large, the method of hypothesis testing for one-tailed and two-tailed one-sample sign tests is then described. The Kolmogorov-Smirnov test fitting for a predetermined distribution is then performed. For small samples, the run test is used to determine whether the occurrence of runs in the provided stream of symbols is random. Next, it is explained how to analyse problems using a run test to determine whether the incidence of runs in each stream of symbols is random for large samples. It is followed by an analysis of the issues using a one-tailed two-sample sign test for small samples. The procedure for using sign tests for small samples with two-tailed data is next illustrated. It is followed by a study of the method for applying one-tailed two-sample sign tests to large samples. The study of the problems using two-tailed, large-sample sign tests for hypothesis testing is presented. A median test is then offered. Next, examples of the Kruskal-Wallis test, K-sample median test, and Mann-Whitney U test are provided.

Pages: 33

Explaining the significance of the correlation coefficient is the first section of this chapter. The information is divided into grouped and ungrouped categories. There is a section that explains how to use the correlation coefficient (Pearson’s coefficient of correlation) to analyse ungrouped data. The calculation of the correlation coefficient (Pearson’s correlation coefficient) for grouped data is then included. A section with the working of the rank correlation coefficient follows. An analysis of the data is then provided using the auto-correlation coefficient. The theory of covariance, its uses, and how it operates are all illustrated at the end.

Pages: 36

Forecasting is the prediction of an occurrence, such as the demand for a product, based on historical data. The purpose of this chapter is to introduce the idea of forecasting and some of its uses. There are various forecasting techniques, which are briefly outlined. There is a section on how to create a linear regression model using formulas in the traditional manner. The creation of a linear regression model using Excel is then illustrated. For forecasting over the next few days or weeks, the moving average approach is used. Excel business examples are used to explain the analysis of data using the moving average method of forecasting. The exponential method of forecasting is used to provide predictions for the next immediate period. The working of the exponential approach of forecasting is covered in the next section. The creation of a model for multiple linear regression is provided at the end.

Pages: 46

The design and analysis of experiments in both businesses and social systems heavily rely on analysis of variance (ANOVA). The idea of design and analysis of an experiment is introduced in this chapter. Later, a description of the differences between fixed factors and random factors is given. A section on the study of problems using Excel for ANOVA with a single factor (fully randomised design) follows. Then an Excel-based random complete block design with two factors and no replications is demonstrated. The Latin square design is then used to solve problems with a single factor and two blocks without replications. It is followed by a section on using Excel to analyse issues with two factors and their interactions. The term “complete factorial experiment” refers to this layout. Finally, using a user-defined Excel sheet, Yates’ technique is used for a 2n complete factorial experiment.

Pages: 27

Graphs and charts offer an alternative to the tabular form for presenting data. This chapter explains how to create a pie chart for a specific set of data in Excel to show the relative sizes of each item in the data. The analysis of the provided data set is then demonstrated via an Excel bar chart, which will clearly indicate the relative differences between the data items proportional to their frequency. The creation of a stacked bar chart for a given set of data is then demonstrated using Excel if there are numerous instances of each item in the data collection. A section on creating a line chart for the given data set to investigate the frequencies using a piecewise linear graph is then included. The creation of a multiple-line chart for a given set of data is demonstrated at the conclusion using an Excel example if there are instances of many variables for a given instance of another variable or item in the data set.

Pages: 34

Using a specific set of management-imposed restrictions, linear programming is a mathematical programming technique that optimises the performance metric for a system of interest. The notion of linear programming and its applications are covered at the outset of this chapter. The linear programming assumptions are then listed. The linear programming model’s components are explained next. Since mathematical modelling is an art, linear programming models for many business problems are formulated. There is a section on using the graphical method for linear programming when there are just two decision variables. Next, the simplex method’s phases and implementations for solving linear programming problems are presented. The Big M method’s steps are then illustrated using examples. Later, the dual simplex approach, a special form of liner programming, is presented with examples. When the values of the model’s parameters are altered, sensitivity analysis enables you to quickly determine the outcomes of linear programming. Examples are provided to illustrate its applications. At the end, business problems are used to illustrate how to use Excel to solve linear programming problems.