1. BUS304 – Data Collection1 Chapter 1 Data Collection  Descriptive Statistics  Tools that collect, present and describe data Collecting Data Characterizing Data Presenting Data survey observation experiments etc Mathematical description of data: e.g. average housing price; stock price volativity.

2. BUS304 – Data Collection2 Population and Samples  A statistic research always starts with a question:  What is the average starting salary for a business major?  How is the housing price in San Diego area?  Are the college textbooks too expensive?  Who is a more valuable player? Reggie Bush or Vince Young?  What else?  Population: -- All the items that are of interest  Sample: -- A subset of the population (or say, part of the population) a b c d ef gh i jk l m n o p q rs t u v w x y z Population Sample b c g i n o r u y How to determine? -- Check whether it covers all the items of interest Exercise: Determine the population for each question on the left How to determine? -- Check whether it covers all the items of interest Exercise: Determine the population for each question on the left

3. BUS304 – Data Collection3 Sampling:  Techniques to select only part of the population to conduct the study  The result will be less reliable than that from studying the population  But sometimes it is more reasonable to use sample than use population  Less time consuming  Less costs  Sometimes, study is destructive. e.g. matches  Think of some examples of sampling.

4. BUS304 – Data Collection4 Sampling Techniques  Non-Statistical Sampling  Samples are selected at convenience  Results will be subject to bias  Examples: Ask a friend, a neighbor, etc. A survey on the Internet; Judges.  Statistical Sampling  Use probability theory to guide the selection  Sampling bias can be estimated (we will learn how to estimate later the semester)  We will learn four techniques in this category.

5. BUS304 – Data Collection5 Four Statistical Sampling Techniques  Simple random Sampling  The most basic statistical sampling method.  Select at random  Dice, Card, Random number generator (calculator, Excel) Exercise:  Use random number generator in Excel to select a sample of ten NBA players and find out the average heights. Simple Random Systematic Stratified Cluster

6. BUS304 – Data Collection6 Four Statistical Sampling Techniques  Systematic Sampling  A simplified version of simple random sampling  Select a random start, and then go by equal space  Question: how to determine the interval so that everyone has a chance to be selected? Formula: Interval = Population size / sample size Simple Random Systematic Stratified Cluster

7. BUS304 – Data Collection7 Systematic sampling exercise  Use systematic sampling technique to select 10 NBA players and find out the average height.  Think? How many random numbers you need to generate?

8. BUS304 – Data Collection8 Four Statistical Sampling Techniques  Stratified Sampling  Divide the population into subgroups  Use simple random sampling method (or systematic sampling) to select from each group  Combine to form one big sample  Think: what is the benefit of using stratified sampling? More representative Simple Random Systematic Stratified Cluster

9. BUS304 – Data Collection9 Stratified sampling exercise  Use stratified sampling technique to select a sample of 10 NBA players, including 2PFs, 2SFs, 2SGs, 2PGs, and 2Cs.  Find out the average weight.

10. BUS304 – Data Collection10 Four Statistical Sampling Techniques  Cluster Sampling  Divide the population into subgroups -- called “clusters”.  Randomly select some subgroups (not all!)  In each selected subgroup, use random sampling technique to select sub- samples  Combine the sub-samples to form one aggregate sample  Think: when we use cluster sampling? (e.g. market research, select towns first) Simple Random Systematic Stratified Cluster

11. BUS304 – Data Collection11 Clustered Sampling Technique  Use each NBA team as a cluster  Randomly select 5 teams to conduct the study  In each of the selected teams, select 2 players  Combine them into an aggregate sample of ten.  Think, how many times do you need to use the Random Number Generator?  Discuss the difference between cluster sampling technique and stratified sampling technique.

12. BUS304 – Data Collection12 Compare different techniques  Simple random sampling and systematic sampling:  Need to know the population size  Doesn’t care about the composition of the population  Stratified sampling:  Use the information about the population composition to control sample  The sample can be more representative to the population  Cluster sampling:  Generally used when you have a geographically distributed population  Divide the population into several geographical areas  Randomly select some areas (not all) to study – cost saving.  Sometimes, a combination of techniques can be used.

13. BUS304 – Data Collection13 Discussion  Which sampling techniques should be used for (or are used in) the following studies? – discuss the potential bias of the techniques. 1.NBC wants to conduct an opinion poll to understand people’s opinion on Hillary Clinton’s chance of being selected as president in 2008. 2.CSUSM wants to collect opinions about how the junior faculty members teach their classes 3.Policemen want to detect drunk drivers to prevent potential accidents. 4.Oscar judges determine the best pictures of the year. 5.Fans vote for the NBA all-star team. 6.American Citizens vote for president.

14. BUS304 – Data Collection14 Summary In today’s lecture:  Two important concepts: Population and Sample  Four Sampling Techniques: Simple Random Sampling Systematic Sampling Stratified Sampling Cluster Sampling