Copyright 2014, 2011, and 2008 Pearson Education, Inc. 1-1

Transcription

1 1-1

2 Statistics for Business and Economics Chapter 1 Statistics, Data, & Statistical Thinking 1-2

3 Contents 1. The Science of Statistics 2. Types of Statistical Applications in Business 3. Fundamental Elements of Statistics 4. Processes 5. Types of Data 6. Collecting Data: Sampling and Related Issues 7. Critical Thinking with Statistics 1-3

4 Learning Objectives Introduce the field of statistics Demonstrate how statistics applies to business Introduce the language of statistics and the key elements of any statistical problem Differentiate between population and sample data Differentiate between descriptive and inferential statistics Introduce the key elements of a process Identify the different types of data and data-collection methods Discover how critical thinking through statistics can help improve our quantitative literacy 1-4

5 1.1 The Science of Statistics 1-5

6 What Is Statistics? 1. Collecting Data e.g., Survey 2. Presenting Data e.g., Charts & Tables 3. Characterizing Data e.g., Average Data Analysis T/Maker Co. Why? Decision- Making T/Maker Co. 1-6

7 What Is Statistics? Statistics is the science of data. It involves collecting, classifying, summarizing, organizing, analyzing, and interpreting numerical information. 1-7

8 1.2 Types of Statistical Applications in Business 1-8

9 Application Areas Economics Forecasting Demographics Engineering Construction Materials Sports Individual & Team Performance Business Consumer Preferences Financial Trends 1-9

10 Statistics: Two Processes Describing sets of data and Drawing conclusions (making estimates, decisions, predictions, etc. about sets of data based on sampling) 1-10

11 Statistical Methods Statistical Methods Descriptive Statistics Inferential Statistics 1-11

12 Descriptive Statistics 1. Involves Collecting Data Presenting Data 50 $ Characterizing Data Purpose Describe Data 0 Q1 Q2 Q3 Q4 X = 30.5 S 2 =

13 Inferential Statistics 1. Involves Estimation Hypothesis Testing Population? 2. Purpose Make decisions about population characteristics 1-13

14 1.3 Fundamental Elements of Statistics 1-14

15 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 1-15

16 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 1-16

17 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 1-17

18 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 1-18

19 1.4 Processes 1-19

20 Process A process is a series of actions or operations that transforms inputs to outputs. A process produces or generates output over time. 1-20

21 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. 1-21

22 1.5 Types of Data 1-22

23 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. 1-23

24 Types of Data Types of Data Quantitative Data Qualitative Data 1-24

25 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

26 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). $ Credit 1-26

27 1.6 Collecting Data 1-27

28 Obtaining Data 1. Data from a published source 2. Data from a designed experiment 3. Data from an observationally study 1-28

29 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 1-29

30 Samples A representative sample exhibits characteristics typical of those possessed by the population of interest. A simple 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. 1-30

31 Random Sample Every sample of size n has an equal chance of selection. 1-31

32 Random Number Generators Most researchers rely on random number generators to automatically generate the random sample. Random number generators are available in table form, and they are built into most statistical software packages. 1-32

33 Importance of Selection How a sample is selected from a population is of vital importance in statistical inference because the probability of an observed sample will be used to infer the characteristics of the sampled population. 1-33

34 Random Sampling Stratified random sampling used when the experimental units associated with the population can be separated into two or more groups of units. Cluster sampling sample natural grouping of experimental units and collect data from all experimental units within each cluster 1-34

35 Random Sampling Systematic sampling systematically selecting every kth experimental unit from a list of all experimental units. Randomized response sampling useful when the questions of a pollster are likely to elicit false answers. 1-35

36 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. 1-36

37 1.7 Critical Thinking with Statistics 1-37

38 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. 1-38

39 Real-World Problem 1-39

40 Statistical Computer Packages 1. Typical Software SPSS MINITAB Excel 2. Need Statistical Understanding Assumptions Limitations 1-40

41 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 1-41

42 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 1-42

43 Key Ideas Types of Data 1. Quantitative (numerical in nature) 2. Qualitative (categorical in nature) 1-43

44 Key Ideas Data-Collection Methods 1. Observational (e.g. survey) 2. Published source 3. Designed experiment 1-44

45 Key Ideas Types of Random Samples 1. Simple Random Sample 2. Stratified random sample 3. Cluster sample 4. Systematic sample 5. Random response sample 1-45

46 Key Ideas Problems with Nonrandom Samples 1. Selection bias 2. Nonresponse bias 3. Measurement error 1-46