2011 Pearson Education Inc Statistics for Business and

© 2011 Pearson Education, Inc

Statistics for Business and Economics Chapter 1 Statistics, Data, & Statistical Thinking © 2011 Pearson Education, Inc

Contents 1. 2. 3. 4. 5. 6. 7. The Science of Statistics Types of Statistical Applications in Business Fundamental Elements of Statistics Processes Types of Data Collecting Data The Role of Statistics in Managerial Decision Making © 2011 Pearson Education, Inc

Learning Objectives 1. 2. 3. 4. Introduce the field of statistics Demonstrate how statistics applies to business Establish the link between statistics and data Identify the different types of data and datacollection methods 5. Differentiate between population and sample data 6. Differentiate between descriptive and inferential statistics © 2011 Pearson Education, Inc

1. 1 The Science of Statistics © 2011 Pearson Education, Inc

What Is Statistics? 1. Collecting Data e. g. , Survey Data Analysis Why? 2. Presenting Data e. g. , Charts & Tables © 1984 -1994 T/Maker Co. Decision. Making 3. Characterizing Data e. g. , Average © 2011 Pearson Education, Inc © 1984 -1994 T/Maker Co.

What Is Statistics? Statistics is the science of data. It involves collecting, classifying, summarizing, organizing, analyzing, and interpreting numerical information. © 2011 Pearson Education, Inc

1. 2 Types of Statistical Applications in Business © 2011 Pearson Education, Inc

Application Areas • Economics • Engineering – Forecasting – Demographics – Construction – Materials • Sports • Business – Individual & Team Performance – Consumer Preferences – Financial Trends © 2011 Pearson Education, Inc

Statistics: Two Processes Describing sets of data and Drawing conclusions (making estimates, decisions, predictions, etc. about sets of data based on sampling) © 2011 Pearson Education, Inc

Statistical Methods Descriptive Statistics Inferential Statistics © 2011 Pearson Education, Inc

Descriptive Statistics 1. Involves • Collecting Data • Presenting Data • Characterizing Data 2. Purpose 50 $ 25 0 • Describe Data Q 1 Q 2 Q 3 Q 4 X = 30. 5 S 2 = 113 © 2011 Pearson Education, Inc

Inferential Statistics 1. Involves • Estimation • Hypothesis Testing Population? 2. Purpose • Make decisions about population characteristics © 2011 Pearson Education, Inc

1. 3 Fundamental Elements of Statistics © 2011 Pearson Education, Inc

Fundamental Elements 1. Experimental unit • Object upon which we collect data 2. Population • All items of interest 3. Variable • • P in Population & Parameter • S in Sample & Statistic Characteristic of an individual experimental unit 4. Sample • Subset of the units of a population © 2011 Pearson Education, Inc

Fundamental Elements 1. Statistical Inference • Estimate or prediction or generalization about a population based on information contained in a sample 2. Measure of Reliability • Statement (usually qualified) about the degree of uncertainty associated with a statistical inference © 2011 Pearson Education, Inc

Four Elements of Descriptive Statistical Problems 1. The population or sample of interest 2. One or more variables (characteristics of the population or sample units) that are to be investigated 3. Tables, graphs, or numerical summary tools 4. Identification of patterns in the data © 2011 Pearson Education, Inc

Five Elements of Inferential Statistical Problems 1. The population of interest 2. One or more variables (characteristics of the population units) that are to be investigated 3. The sample of population units 4. The inference about the population based on information contained in the sample 5. A measure of reliability for the inference © 2011 Pearson Education, Inc

1. 4 Processes © 2011 Pearson Education, Inc

Process A process is a series of actions or operations that transforms inputs to outputs. A process produces or generates output over time. © 2011 Pearson Education, Inc

Process A process whose operations or actions are unknown or unspecified is called a black box. Any set of output (object or numbers) produced by a process is called a sample. © 2011 Pearson Education, Inc

1. 5 Types of Data © 2011 Pearson Education, Inc

Types of Data Quantitative data are measurements that are recorded on a naturally occurring numerical scale. Qualitative data are measurements that cannot be measured on a natural numerical scale; they can only be classified into one of a group of categories. © 2011 Pearson Education, Inc

Types of Data Quantitative Data Qualitative Data © 2011 Pearson Education, Inc

Quantitative Data Measured on a numeric scale. • Number of defective items in a lot. • Salaries of CEOs of oil companies. • Ages of employees at a company. 4 943 52 21 12 120 71 © 2011 Pearson Education, Inc 8 3

Qualitative Data Classified into categories. • College major of each student in a class. • Gender of each employee at a company. • Method of payment (cash, check, credit card). $ © 2011 Pearson Education, Inc Credit

1. 6 Collecting Data © 2011 Pearson Education, Inc

Obtaining Data 1. 2. 3. 4. Data from a published source Data from a designed experiment Data from a survey Data collected observationally © 2011 Pearson Education, Inc

Obtaining Data Published source: book, journal, newspaper, Web site Designed experiment: researcher exerts strict control over units Survey: a group of people are surveyed and their responses are recorded Observation study: units are observed in natural setting and variables of interest are recorded © 2011 Pearson Education, Inc

Samples A representative sample exhibits characteristics typical of those possessed by the population of interest. A 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 selection. © 2011 Pearson Education, Inc

Random Sample Every sample of size n has an equal chance of selection. © 2011 Pearson Education, Inc

1. 7 The Role of Statistics in Managerial Decision Making © 2011 Pearson Education, Inc

Statistical Thinking 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 populations and process data. A 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 selection. © 2011 Pearson Education, Inc

Nonrandom Sample Errors Selection bias results when a subset of the experimental units in the population is excluded so that these units have no chance of being selected for the sample. Nonresponse bias results when the researchers conducting a survey or study are unable to obtain data on all experimental units selected for the sample. Measurement error refers to inaccuracies in the values of the data recorded. In surveys, the error may be due to ambiguous or leading questions and the interviewer’s effect on the respondent. © 2011 Pearson Education, Inc

Real-World Problem © 2011 Pearson Education, Inc

Statistical Computer Packages 1. Typical Software • • • SPSS MINITAB Excel 2. Need Statistical Understanding • • Assumptions Limitations © 2011 Pearson Education, Inc

Key Ideas Types of Statistical Applications Descriptive 1. Identify population and sample (collection of experimental units) 2. Identify variable(s) 3. Collect data 4. Describe data © 2011 Pearson Education, Inc

Key Ideas Types of Statistical Applications Inferential 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 © 2011 Pearson Education, Inc

Key Ideas Types of Data 1. Quantitative (numerical in nature) 2. Qualitative (categorical in nature) © 2011 Pearson Education, Inc

Key Ideas Data-Collection Methods 1. Observational 2. Published source 3. Survey 4. Designed experiment © 2011 Pearson Education, Inc

Key Ideas Problems with Nonrandom Samples 1. Selection bias 2. Nonresponse bias 3. Measurement error © 2011 Pearson Education, Inc