Applied Business Statistics PDF

# Applied Business Statistics ## Chapter 1 - Statistics in Management ### 1.1 Statistics in Management - Statistical methods can be applied in any management area where data exists. (e.g. Human Resources, Marketing, Finance and Operations) - Statistics support the decision process by strengthenin...

# Applied Business Statistics ## Chapter 1 - Statistics in Management ### 1.1 Statistics in Management - Statistical methods can be applied in any management area where data exists. (e.g. Human Resources, Marketing, Finance and Operations) - Statistics support the decision process by strengthening the quantifiable basis from which a well-informed decision can be made. - Business statistics is very often 'common sense' translated into statistical terminology and formulae. ### 1.2 The Language of Statistics - **Random Variable** is any attribute of interest on which data are collected and analysed. - **Data** are the actual values (numbers) or outcomes recorded on a random variable. - **Sampling Unit** is the object being measured, counted or observed with respect to the random variable under study. - **Population** is the collection of all possible data values that exist for the random variable under study. - **Population Parameter** is a measure that describes a characteristic of a population. - **Sample** is a subset of data values drawn from a population. - **Sample Statistic** is a measure that describes a characteristic of a sample. ### 1.3 Components of Statistics - **Descriptive Statistics** condenses sample data into a few summary descriptive measures to identify profiles, patterns, relationships and trends within the data. - **Inferential Statistics** generalizes sample findings to the broader population. - **Statistical Modelling** constructs equations between variables that are related to each other. These equations can be used to estimate or predict values of one of these variables based on values of related variables. ### 1.4 Statistics and Computers - Statistical capabilities are within reach of all managers with the availability of user-friendly statistical software. ### 1.5 Statistical Applications in Management - Statistical methods can be applied in any business management area where data exists. - For example: - **Finance:** Stock market analysts use statistical methods to predict share price movements, financial analysts use statistical findings to guide their investment decisions. - **Marketing:** Marketing research uses statistical methods to sample and analyze consumer behaviour and purchasing patterns. - **Human Resources:** Statistics is used to analyze human resources issues such as training effectiveness, patterns of absenteeism and employee turnover, compensation planning and staff planning. - **Operations/Logistics:** Production managers rely heavily on statistical quality control methods to monitor both product and production processes for quality. ### 1.6 Data and Data Quality - **Data Quality** is influenced by four factors: data type, data source, method of data collection and appropriate data preparation. ### 1.7 Data Types and Measurement Scales - **Random Variable** is either qualitative (categorical) or quantitative (numeric) in nature. - **Qualitative Random Variables** generate categorical (non-numeric) response data represented by categories only. - **Quantitative Random Variable** generate numeric response data that can be manipulated using arithmetic operations. - **Discrete Data** are whole number (or integer) data. - **Continuous Data** are any numbers that can occur in an interval. #### Measurement Scales - **Nominal Data** are associated with categorical data where all the categories are of equal importance. - **Ordinal Data** are also associated with categorical data, but have an implied ranking between the different categories. - **Interval Data** are associated with numeric data and quantitative random variables. This data is generated mainly from rating scales which are used in survey questionnaires. Interval data can be treated as numeric data for the purpose of statistical analysis. - **Ratio Data** consists of all real numbers associated with quantitative random variables. Ratio data have all the properties of numbers (order, distance and an absolute origin of zero) that allow such data to be manipulated using all arithmetic operations. ### 1.8 Data Sources - **Internal Data** is sourced from within a company. It is data that is generated during the normal course of business activities. - **External Data** exists outside an organisation. - Sources can be classified as **Primary** (recorded for the first time at source) or **Secondary** (data that already exists in a processed format). ### 1.9 Data Collection Methods - **Observation** is a method of collecting data by observing a respondent or a process in action. - **Surveys** gather primary data through the direct questioning of respondents using questionnaires. - **Experimentation** is a method of collecting primary data by manipulating certain variables under controlled conditions. #### Types of Data Collection Methods - **Primary Data** is data that is recorded for the first time at source and with a specific purpose in mind. - **Surveys** are conducted through personal interviews, telephone surveys or e-surveys. - **Personal Interviews** allow probing for reasons, capturing non-verbal responses and asking more questions. - **Telephone Interviews** allow quicker contact with geographically dispersed respondents. - **E-Surveys** allow access to local, national and international target populations. - **Secondary Data** is data that already exists in a processed format. #### Advantages and Disadvantages of Data Collection Methods - **Observation:** - **Advantages:** It allows the respondent to behave naturally and reduces bias. - **Disadvantages:** It is a passive form of data collection and does not allow probing for reasons. - **Surveys:** - **Advantages:** It produces high-quality, current and accurate data. - **Disadvantages:** it is time consuming and expensive. - **Experimentation:** - **Advantages:** It produces accurate data. - **Disadvantages:** It is costly, time-consuming and may be impossible to control certain extraneous factors that can confound the results.