Introduction to Statistical Data Analysis

Statistics is basically a science that involves data collection, data interpretation and finally, data validation. Statistical data analysis is a procedure of performing various statistical operations. It is a kind of quantitative research, which seeks to quantify the data, and typically, applies some form of statistical analysis. Quantitative data basically involves descriptive data, such as survey

Applications of Statistics in Business and Economics

In today’s global business and economic environment, anyone can access vast amounts of statistical information. The most successful managers and decision makers understand the information and know how to use it effectively. In this section, we provide examples that illustrate some of the uses of statistics in business and economics. 1. Accounting Public accounting

Types of Data

Data are the facts and figures collected, analyzed, and summarized for presentation and interpretation. All the data collected in a particular study are referred to as the data set for the study. Table 1.1 shows a data set containing information for 60 nations that participate in the World Trade Organization. The World Trade Organization

Data Sources

Data can be obtained from existing sources, by conducting an observational study, or by conducting an experiment. 1. Existing Sources In some cases, data needed for a particular application already exist. Companies maintain a va­riety of databases about their employees, customers, and business operations. Data on employee salaries, ages, and years of experience can

Descriptive Statistics

Most of the statistical information in the media, company reports, and other publications consists of data that are summarized and presented in a form that is easy for the reader to understand. Such summaries of data, which may be tabular, graphical, or numerical, are referred to as descriptive statistics. Refer to the data set

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Statistical Inference

Many situations require information about a large group of elements (individuals, compa­nies, voters, households, products, customers, and so on). But, because of time, cost, and other considerations, data can be collected from only a small portion of the group. The larger group of elements in a particular study is called the population, and the

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Data Analytics

Because of the dramatic increase in available data, more cost-effective data storage, faster computer processing, and recognition by managers that data can be extremely valuable for understanding customers and business operations, there has been a dramatic increase in data-driven decision making. The broad range of techniques that may be used to support data-driven decisions

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Big Data and Data Mining

With the aid of magnetic card readers, bar code scanners, and point-of-sale terminals, most organizations obtain large amounts of data on a daily basis. And, even for a small local restaurant that uses touch screen monitors to enter orders and handle billing, the amount of data collected can be substantial. For large retail companies,

Computers and Statistical Analysis

Statisticians use computer software to perform statistical computations and analyses. For example, computing the average time until recharge for the 200 batteries in the Rogers Industries example (see Table 1.5) would be quite tedious without a computer. End-of-chapter appendixes cover the step-by-step procedures for using Microsoft Excel and the statistical package JMP to implement

Ethical Guidelines for Statistical Practice

Ethical behavior is something we should strive for in all that we do. Ethical issues arise in statistics because of the important role statistics plays in the collection, analysis, presenta­tion, and interpretation of data. In a statistical study, unethical behavior can take a variety of forms including improper sampling, inappropriate analysis of the data,

Summarizing Data for a Categorical Variable

1. Frequency Distribution We begin the discussion of how tabular and graphical displays can be used to summarize categorical data with the definition of a frequency distribution. FREQUENCY DISTRIBUTION A frequency distribution is a tabular summary of data showing the number (frequency) of observations in each of several nonoverlapping categories or classes. Let us

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Summarizing Data for a Quantitative Variable

As defined in Section 2.1, a frequency distribution is a tabular summary of data showing the number (frequency) of observations in each of several nonoverlapping categories or classes. This definition holds for quantitative as well as categorical data. However, with quantitat­ive data we must be more careful in defining the nonoverlapping classes to be

Summarizing Data for Two Variables Using Tables

Thus far in this chapter, we have focused on using tabular and graphical displays to summarize the data for a single categorical or quantitative variable. Often a manager or decision maker needs to summarize the data for two variables in order to reveal the relationship—if any—between the vari­ables. In this section, we show how

Summarizing Data for Two Variables Using Graphical Displays

In the previous section we showed how a crosstabulation can be used to summarize the data for two variables and help reveal the relationship between the variables. In most cases, a graphical display is more useful for recognizing patterns and trends in the data. In this section, we introduce a variety of graphical displays

Data Visualization: Best Practices in Creating Effective Graphical Displays

Data visualization is a term used to describe the use of graphical displays to summarize and present information about a data set. The goal of data visualization is to communicate as effectively and clearly as possible, the key information about the data. In this section, we provide guidelines for creating an effective graphical display,

Measures of Location

1. Mean Perhaps the most important measure of location is the mean, or average value, for a variable. The mean provides a measure of central location for the data. If the data are for a sample, the mean is denoted by x; if the data are for a population, the mean is denoted by

Measures of Variability

In addition to measures of location, it is often desirable to consider measures of variability, or dispersion. For example, suppose that you are a purchasing agent for a large manufacturing firm and that you regularly place orders with two different suppliers. After several months of operation, you find that the mean number of days

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Measures of Distribution Shape, Relative Location, and Detecting Outliers

We have described several measures of location and variability for data. In addition, it is often important to have a measure of the shape of a distribution. In Chapter 2 we noted that a histogram provides a graphical display showing the shape of a distribution. An important numerical measure of the shape of a

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Five-Number Summaries and Boxplots

Summary statistics and easy-to-draw graphs based on summary statistics can be used to quickly summarize large quantities of data. In this section we show how five-number sum­maries and boxplots can be developed to identify several characteristics of a data set. 1. Five-Number Summary In a five-number summary, five numbers are used to summarize the

Measures of Association Between Two Variables

Thus far we have examined numerical methods used to summarize the data for one vari­able at a time. Often a manager or decision maker is interested in the relationship between two variables. In this section we present covariance and correlation as descriptive measures of the relationship between two variables. We begin by reconsidering the

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