Chapter 1: Statistics, Data, and Statistical Thinking
Chapter 1: Statistics, Data, and Statistical Thinking 1.1 The Science of Statistics Statistics-science of using data to describe characteristics of a group of objects & to make inferences about the group. (Involves collecting, classifying, summarizing, organizing, analysing, & interpreting info & data) 1.2 Types of Statistical Applications in Business Descriptive Statistics- uses numbers & graphs to look at patterns in data set (summarizes info & present s in convenient form)
Four Elements of Descriptive Statistical Problems: 1. Identify population or sample (collection of experimental units) 2. Identify variable(s) 3. Collect data 4. Describe data Inferential Statistics- uses sample data to make estimates, decisions, predictions, or other generalizations about a larger set of data Five elements of Inferential Statistical Problems: 1. Identify population (collection of all experimental units) 2. Identify variable(s) 3. Collect sample data (subset of population) 4. Inference about population based on sample 5. Measure of reliability for inference *Examples where statistics are used in business: data mining, e-commerce, forecasting, investment analysis, marketing, pricing strategies 1.3 Fundamental Elements of Statistics Unit- objects on which measurements are recorded Population- set of all units of interest (all employed workers in the USA) Parameter-numerical characteristics of a population Variable- characteristic or property of an individual experimental unit (age, gender, income, etc.) Measurement- process used to assign numbers to variables of individual population units (scale ratings) Census- measuring a variable for every experimental unit of a population Sample- subset of the units of a population (any set of output produced by a process) Estimator- numerical characteristics of the sample Statistical Inference- estimate, prediction or some other generalization about a population based on information contained in a sample (use information contained in sample to learn about larger population) Readability- statement about degree of uncertainty associated with statistical inference (how good inference is)
1.4 Processes Process- series of actions or operations that transforms inputs to outputs (generates output over time) Black box- process whose operations or actions are unknown or unspecified
1.5 Types of Data Qualitative data- categorical data usually used to denote various categories Quantitative data- numerical data that are often measured in units 1.6 Collecting Data -generally, data can be obtained in four different ways: 1. from a published source – data set of interest has already been collected for you and is available (website, book, journal, etc.)
2. from a designed experiment the study – researcher exerts strict control over units in (treatment & control groups)
3. from a survey – researcher samples a group of people, asks questions and records responses 4. Collected observationally- researcher observes experimental units in natural setting and records variables of interest Representative sample-exhibits characteristics typical of those possessed by the population of interest Random sample- of n experimental units is a sample selected from the population in such a way that every different sample of size n has an equal chance of being selected -data is expensive to acquire, but computation is cheap once the infrastructure is in place 1.7 The Role of Statistics in Managerial Decision Making Statistical thinking- involves applying rational thought and the science of statistics to critically assess data and inferences. Fundamental to the thought process is that variation exists in population and process data Selection bias- sample not representative of population because a subset of the population has no chance of being selected for the sample Nonresponse bias- respondents refusal to participate may be related to the response variable Measurement error- response measured and recorded for an individual unit is not correct
Four Elements of Descriptive Statistical Problems: 1. Identify population or sample (collection of experimental units) 2. Identify variable(s) 3. Collect data 4. Describe data Inferential Statistics- uses sample data to make estimates, decisions, predictions, or other generalizations about a larger set of data Five elements of Inferential Statistical Problems: 1. Identify population (collection of all experimental units) 2. Identify variable(s) 3. Collect sample data (subset of population) 4. Inference about population based on sample 5. Measure of reliability for inference *Examples where statistics are used in business: data mining, e-commerce, forecasting, investment analysis, marketing, pricing strategies 1.3 Fundamental Elements of Statistics Unit- objects on which measurements are recorded Population- set of all units of interest (all employed workers in the USA) Parameter-numerical characteristics of a population Variable- characteristic or property of an individual experimental unit (age, gender, income, etc.) Measurement- process used to assign numbers to variables of individual population units (scale ratings) Census- measuring a variable for every experimental unit of a population Sample- subset of the units of a population (any set of output produced by a process) Estimator- numerical characteristics of the sample Statistical Inference- estimate, prediction or some other generalization about a population based on information contained in a sample (use information contained in sample to learn about larger population) Readability- statement about degree of uncertainty associated with statistical inference (how good inference is)
1.4 Processes Process- series of actions or operations that transforms inputs to outputs (generates output over time) Black box- process whose operations or actions are unknown or unspecified
1.5 Types of Data Qualitative data- categorical data usually used to denote various categories Quantitative data- numerical data that are often measured in units 1.6 Collecting Data -generally, data can be obtained in four different ways: 1. from a published source – data set of interest has already been collected for you and is available (website, book, journal, etc.)
2. from a designed experiment the study – researcher exerts strict control over units in (treatment & control groups)
3. from a survey – researcher samples a group of people, asks questions and records responses 4. Collected observationally- researcher observes experimental units in natural setting and records variables of interest Representative sample-exhibits characteristics typical of those possessed by the population of interest Random sample- of n experimental units is a sample selected from the population in such a way that every different sample of size n has an equal chance of being selected -data is expensive to acquire, but computation is cheap once the infrastructure is in place 1.7 The Role of Statistics in Managerial Decision Making Statistical thinking- involves applying rational thought and the science of statistics to critically assess data and inferences. Fundamental to the thought process is that variation exists in population and process data Selection bias- sample not representative of population because a subset of the population has no chance of being selected for the sample Nonresponse bias- respondents refusal to participate may be related to the response variable Measurement error- response measured and recorded for an individual unit is not correct
