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. - Statistics support the decision process by strengthening the quantifiable basis from which a well-informed decisio...

# 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. - Statistics support the decision process by strengthening the quantifiable basis from which a well-informed decision can be made. - Statistical methods can be replicated and applied consistently in similar situations. - Managers should develop an appreciation of statistical methods and thinking so they can: - Recognise situations where statistics can be applied to enhance a decision process. - Perform simple statistical analyses using Excel. - Interpret management reports expressed in numerical terms. - Critically assess the validity of statistical findings before using them in decision-making. - Initiate research studies with an understanding of the statistical methods involved. - Communicate more effectively with statistical analysts. ### 1.2 The Language of Statistics - Important terms, concepts, and symbols used in Statistics: - **Random Variable**: Any attribute of interest on which data is collected and analyzed. - **Data**: Actual values or outcomes recorded on a random variable. - **Sampling Unit**: The object being measured, counted, or observed with respect to the random variable under study. - **Population**: Collection of all possible data values that exist for the random variable under study. - **Population Parameter**: Measure that describes a characteristic of a population. - **Sample**: Subset of data values drawn from a population. - **Sample Statistic**: Measure that describes a characteristic of a sample. | Statistical Measure | Sample Statistic | Population Parameter | |---|---|---| | Mean | x̄ | μ| | Standard Deviation | s | σ | | Variance | s² | σ² | | Size | n | N | | Proportion | p̂ | ρ | | Correlation | r | ρ | ### 1.3 Components of Statistics - **Descriptive Statistics**: Condenses sample data into a few summary descriptive measures. - **Inferential Statistics**: Generalizes sample findings to the broader population. - **Statistical Modeling**: Builds models of relationships between random variables. - **Population**: All possible data values that exist for the random variable under study. - **Sample**: Subset of data values drawn from a population. ### 1.4 Statistics and Computers - User-friendly statistical software exists such as Microsoft Excel, SPSS, Splus, Minitab, NCSS, Stat graphics, SYSTAT, EViews, UNISTAT, and Stata. ### 1.5 Statistical Applications in Management - Statistical methods can be applied in any business management area where data exists. - Finance: - Stock market analysts use statistical methods to predict share price movements. - Financial analysts use statistical information to guide their investment decisions in bonds, cash, equities, property, etc. - Statistics used to assess the viability of different investment projects, project cash flows, analyze patterns of payment by debtors. - Marketing: - Marketing research uses statistical methods to sample and analyze a wide range of consumer behavior and purchasing patterns. - Market segmentation studies use statistical techniques to identify viable market segments. - Advertising research uses statistics to determine media effectiveness. - Human Resources: - Statistics used to analyze human resource issues such as training effectiveness, patterns of absenteeism and employee turnover, compensation planning, and staff planning. - Survey of employee attitudes to employment uses similar statistical methods to market research. - Operations/Logistics: - Production managers rely heavily on statistical quality control methods to monitor both product and production processes for quality and process improvement. - Statistical forecasts of future demand are used to determine machine and labor utilization over the planning period. ### 1.6 Data and Data Quality - Data quality is influenced by: - **Data Type**: Qualitative or quantitative. - **Data Source**: Internal or external. - **Method of Data Collection**: Observation, surveys, or experimentation. - **Data Preparation**: The process of cleaning and preparing data for analysis. ### 1.7 Data Types and Measurement Scales - **Qualitative Random Variable**: Generates categorical (non-numeric) response data. #### Measurement Scales - Nominal data: The categories of a qualitative random variable of equal importance. - Ordinal data: Categories of a qualitative random variable possess an implied ranking between the categories. - Interval data: Numeric data that possesses the properties of rank order and distance. - Ratio data: All real numbers associated with a quantitative random variable. ### 1.8 Data Sources - Data for statistical analysis is available from many different sources. - **Internal Data**: Sourced from within a company. - **External Data**: Sourced from outside a company. - **Primary Data**: Recorded for the first time and has a specific purpose. - **Secondary Data**: Already exists in a processed format. ### 1.9 Data Collection Methods - **Observation**: Primary data can be collected by observing a respondent or a process in action. - **Surveys**: Primary data can be collected by directly questioning respondents using a questionnaire. - **Personal Interviews**: Face-to-face contact with a respondent. - **Telephone Interviews**: Over the phone interviews. - **e-Surveys**: Use technology such as emails, the internet, and mobile to conduct surveys. - **Experimentation**: Primary data can be collected by manipulating variables in a controlled environment.