The Where, Why, and How of Data Collection

Business Statistics Business statistics consists of a set of tools and techniques that are used to convert data into meaningful information for a business environment.

Objective in Business Statistics Describe Descriptive Statistics Compare Relate Inferential Statistics

Descriptive Statistics Descriptive Statistics consists of the tools and techniques designed to describe data, such as charts, graphs, and numerical measures.

Descriptive Statistics (Figure 1-2: Histogram)

Descriptive Statistics AVERAGE The sum of all the values divided by the number of values. In equation form: where: N = number of data values xi = ith data value

Inferential Statistics Inferential Statistics consists of techniques that allow a decision-maker to reach a conclusion about characteristics of a larger data set based upon a subset of those data

Two Basic Categories of Statistical Inference Tools Estimation Hypothesis Testing

Data Types Primary Data Secondary Data Those that are collected by you or another person with whom you are closely associated. Secondary Data Those that are collected and compiled by an outside source or by someone in your organization who may later provide access to the data to other users.

Tools for Collecting Data Experiments Telephone Surveys Mail Questionnaires Direct Observation and Personal Interview

An experiment is any process that generates data as its outcome. Experiments An experiment is any process that generates data as its outcome.

Major Steps for a Telephone or Written Survey Define the Issue Define the Population of Interest Develop Survey Questions Pre-test the Survey Determine the Sample Size and Sampling Method Select Sample and Administer

Surveys Demographic questions Closed-ended questions Open ended questions

Populations and Samples A population is a set of specific data values on all objects or individuals of interest.

Populations and Samples A sample is a subset of the population.

Parameters and Statistics Descriptive numerical measures calculated from the entire population are called parameters. Corresponding measures for a sample are called statistics.

Sampling Techniques Non-statistical sampling techniques refer to those methods of sampling using influence, judgement, or other non-chance processes. Example: Convenience sampling -- sample from the population based upon accessibility and ease of selection.

Sampling Techniques Statistical sampling techniques refer to those methods of sampling that use selection techniques based upon chance selection.

Statistical Sampling Types of statistical sampling include: Simple Random Sampling Stratified Random Sampling Systematic Sampling Cluster Sampling

Statistical Sampling Simple random sampling refers to a method of selecting items from a population such that every possible sample of a specified size has an equal chance of being selected.

Statistical Sampling Stratified random sampling refers to a sampling method in which the population is divided into subgroups called strata so that each population item belongs to only one strata. The objective is to form strata such that the population values of interest are as much alike as possible.

Stratified Sampling Example (Figure 1-13) Population Financial Institutions Stratified Population

Stratified Sampling Example (Figure 1-13) Population Cash holdings of All Financial Institutions in the United States Financial Institutions Stratified Population

Stratified Sampling Example (Figure 1-13) Population Cash holdings of All Financial Institutions in the United States Financial Institutions Stratified Population

Stratified Sampling Example (Figure 1-13) Population Cash holdings of All Financial Institutions in the United States Financial Institutions Stratified Population Large Institutions Stratum 1 Select n1

Stratified Sampling Example (Figure 1-13) Population Cash holdings of All Financial Institutions in the United States Financial Institutions Stratified Population Large Institutions Medium Size Institutions Stratum 1 Select n1 Stratum 2 Select n2

Stratified Sampling Example (Figure 1-13) Population Cash holdings of All Financial Institutions in the United States Financial Institutions Stratified Population Large Institutions Medium Size Institutions Small Institutions Stratum 1 Select n1 Stratum 2 Select n2 Stratum 3 Select n3

Statistical Sampling Systemic random sampling refers to a sampling technique that involves selecting the kth item in the population after randomly selecting a starting point between 1 and k. The value of k is determined as the ratio of the population size over the desired sample size.

Statistical Sampling Cluster sampling refers to a method by which the population is divided into groups, or clusters, that are each intended to be mini-populations. A random sample of m clusters is selected.

Cluster Sampling Example (Figure 1-14) Mid-Level Managers by Location for Morrison-Knudsen Construction Company 25 105 20 36 152 76 37 Algeria California Alaska New York Idaho Mexico Australia Illinois Scotland Florida 42 22 52

Cluster Sampling Example (Figure 1-14) Mid-Level Managers by Location for Morrison-Knudsen Construction Company Illinois Scotland Florida 42 22 52 All members selected from these clusters

Quantitative and Qualitative Data Data that are numeric and which define value or quantity are quantitative data. Data whose measurement scale is inherently categorical are qualitative data.

Time Series Data and Cross-Sectional Data Time series data consist of a set of ordered data values observed at successive points in time. Cross-sectional data are a set of data values observed at a fixed point in time.

Data Measurement Levels Nominal Data Ordinal (Rank) Data Interval Data Ratio Data

Data Level Hierarchy (Figure 1-15) Highest Level Complete Analysis Ratio/Interval Data Measurements Higher Level Mid-level Analysis Rankings Ordered Categories Ordinal Data Categorical Codes ID Numbers Category Names Lowest Level Basic Analysis Nominal Data