Business Statistics A DecisionMaking Approach 8 th Edition

Chapter Goals After completing this chapter, you should be able to: n n Describe key data collection methods Know key definitions: Population vs. Sample Primary vs. Secondary data types Qualitative vs. Quantitative data Time Series vs. Cross-Sectional data n Explain the difference between descriptive and inferential Statistics n Describe different sampling methods n Construct and interpret graphs Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall 1 -2

Procedures of Statistics n Descriptive procedures n n Collecting, presenting, and describing data Inferential procedures n Drawing conclusions and/or making decisions concerning a population based only on sample data Goal: Convert data into meaningful information! Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall 1 -3

Descriptive Procedures n Collect data n e. g. , Survey, Observation, Experiments n Present data n n e. g. , Charts and graphs Describe data n e. g. , Sample mean = Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall 1 -4

Inferential Procedures n Making statements about a population by examining sample results Sample statistics (known) Population parameters Inference Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall unknown, but can be estimated from sample evidence 1 -5

Techniques for Inferential Procedures Drawing conclusions and/or making decisions concerning a population based on sample results. n Estimation n n e. g. , Estimate the population mean weight using the sample mean weight Hypothesis Testing n e. g. , Use sample evidence to test the claim that the population mean weight is 120 pounds Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall 1 -6

Procedures for Collecting Data (MKTG 300 course and Video Clip #14: Samples and Surveys) Data Collection Procedures Experiments Written questionnaires Telephone surveys Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall Direct observation and personal interview 1 -7

Population vs. Sample Population a b Sample cd b ef gh i jk l

Populations and Samples n A population is the entire collection of things under consideration and referred to as the frame n n n The sampling unit is each object or individual in the frame A parameter is a summary measure computed to describe a characteristic of the population A sample is a subset of the population selected for analysis n A statistic is a summary measure computed to describe a characteristic of the sample drawn from the population Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall 1 -9

Why Sample? n Less time consuming than a census n Less costly to administer than a census n It is possible to obtain statistical results of a sufficiently high precision based on samples Strive for representative samples to reflect the population of interest accurately! Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall 1 -10

Sampling Techniques Nonstatistical Sampling Convenience Statistical Sampling Simple Random Systematic Judgment Stratified Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall Cluster 1 -11

Nonstatistical Sampling n Convenience n n Collected in the most convenient manner for the researcher Judgment n Based on judgments about who in the population would be most likely to provide the needed information Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall 1 -12

Statistical Sampling n Items of the sample are chosen based on known or calculable probabilities Statistical Sampling (Probability Sampling) Simple Random Stratified Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall Systematic Cluster 1 -13

Simple Random Sampling n n n Every possible sample of a given size has an equal chance of being selected Selection may be with replacement or without replacement The sample can be obtained using a table of random numbers or computer random number generator Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall 1 -14

Stratified Random Sampling (Please Watch Video Clip #14: Samples and Surveys) n Divide population into subgroups (called strata) according to some common characteristic n e. g. , gender, income level n Select a simple random sample from each subgroup n Combine samples from subgroups into one Population Divided into 4 strata Sample Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall 1 -15

Systematic Random Sampling n n Decide on sample size: n Divide ordered (e. g. , alphabetical) frame of N individuals into groups of k individuals: k=N/n Randomly select one individual from the 1 st group Select every kth individual thereafter N = 64 n=8 First Group k=8 Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall 1 -16

Cluster Sampling n n Divide population into several “clusters, ” each representative of the population (e. g. , county) Select a simple random sample of clusters n All items in the selected clusters can be used, or items can be chosen from a cluster using another probability sampling technique Population divided into 16 clusters. Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall Randomly selected clusters for sample 1 -17

Cluster vs. Stratified Sampling Cluster Sampling Stratified Sampling Elements of a population are randomly The entire population is divided into even selected to be a part of groups (clusters). segments (strata). Members from randomly selected clusters are a part of this sample. Individual components of the strata are randomly considered to be a part of sampling units. Homogeneity is maintained between clusters Homogeneity is maintained within the strata. Heterogeneity is maintained with the clusters. Heterogeneity is maintained between strata. The clusters are divided naturally. The strata division is primarily decided by the researchers or statisticians. The key objective is to minimize the cost involved and enhance competence. The key objective is to conduct accurate sampling along with properly represented population. Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall 1 -18

Data Types (Please see the Video Clip Introduction to Variables) Data Qualitative (Categorical) Quantitative (Numerical) Examples: n n n Marital Status Political Party Eye Color (Defined categories) Discrete Examples: n n Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall Number of Children Defects per hour (Counted items) Continuous Examples: n n Weight Voltage (Measured characteristics) 1 -19

Data Types n Time Series Data n n Ordered data values observed over time Cross Sectional Data n Data values observed at a single point in time Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall 1 -20

Data Types Sales (in $1000’s) 2003 2004 2005 2006 Atlanta 435 460 475 490 Boston 320 345 375 395 Cleveland 405 390 410 395 Denver 260 270 285 280 Time Series Data Cross Sectional Data Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall 1 -21

Data Measurement Levels (Please see the Video Clip: Scales of Measurement) Measurements e. g. , temperature Rankings Ordered Categories e. g. , age range 25 -34 Categorical Codes e. g. , ID Numbers, gender Ratio/Interval Data Ordinal Data Nominal Data Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall Highest Level Complete Analysis Higher Level Mid-level Analysis Lowest Level Basic Analysis 1 -22

Steps to Categorizing Data 1. 2. 3. 4. Identify each factor in the data set Determine if the data are time-series or cross-sectional Determine which factors are quantitative and which are qualitative Determine the level of data measurement n e. g. , nominal, ordinal Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall 1 -23

Chapter Summary n Reviewed key data collection methods n Introduced key definitions: Population vs. Sample Primary vs. Secondary data types Qualitative vs. Quantitative data Time Series vs. Cross-Sectional data n Examined descriptive vs. inferential procedures n Described different sampling techniques n Reviewed data types and measurement levels Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall 1 -24

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