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1.3 Po pulations, Samples, and Sampling Techniques

1.3 Po pulations, Samples, and Sampling Techniques. Chapter 1 (Page 38). (Page 38) Population – the set of all objects or individuals of interest or the measurements obtained from all objects or individuals of interest. Sample – a subset of the population (Page 39)

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1.3 Po pulations, Samples, and Sampling Techniques

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  1. 1.3 Populations, Samples, and Sampling Techniques Chapter 1 (Page 38)

  2. (Page 38) Population – the set of all objects or individuals of interest or the measurements obtained from all objects or individuals of interest. Sample – a subset of the population (Page 39) Parameters – descriptive numerical measures computed from an entire population Statistics – descriptive numerical measures computed from a sample

  3. When a teacher wants to know the common height of freshmen students in YUC, she gets only a sample of 200 first year students. When a housewife buys a sack of rice, she examines only a handful of rice from the sack to find out whether it is of good quality or not. When a researcher wants to know the IQ of students in the international high schools, she gets a sample of 50 first to fourth year students from each of the international high schools in Yanbu.

  4. (Page 39) Statistical Sampling Techniques – methods that use selection techniques based on chance selection Non-statistical Sampling Techniques – methods of selecting samples using convenience, judgment or other nonchance processes. Convenience Sampling – techniques that selects the items from the population based on accessibility and ease of selection

  5. (Page 40) Statistical Sampling Methods – (probability sampling) allow every item in the population to have a chance of being included in the sample. (Page 40 - 41) • Simple Random Sampling – items from a population has an equal chance of being selected. • Stratified Random Sampling – items are selected from each stratum (group) using the simple random sampling.

  6. (Page 42 – 43) 3. Systematic Random Sampling – selecting every kth item in the population after a randomly selected starting point between 1 and k. 4. Cluster Sampling – method in which the population is divided into clusters that are intended to be mini-population.

  7. 1.4 Data Types and Data Measurement Levels Chapter 1 (Page 44)

  8. (Page 45) Quantitative Data – measurements whose values are numerical. Qualitative Data – measurements is categorical. Sample Exercises: • Amount of time it takes to assemble a simple puzzle. • Number of students in a first-grade classroom. • Rating of newly elected politician: excellent, good, fair, poor. • State in which a person lives.. • Population in a particular area of the US. • Age of a cancer patient. • Color of a car entering in a parking lot.

  9. Additional Exercise: • Most frequent use of microwave oven. (reheating, defrosting, warming) • Number of consumers who refuse to answer a telephone survey. • The door chosen by a mouse in a maze experiment. (A, B, or C) • The winning time for a horse in a derby. • The number of children who are reading above grade level.

  10. (Page 45) Time-Series Data – a set of consecutive data values observed at successive points in time. Example: yearly enrollment , daily sales, quarterly production Cross-Sectional Data – set of data values observed at a fixed point in time. Example: annual income of household for year 2000, average salary of teachers at YUC for year 2009

  11. (Page 45 - 46) Data Measurement levels • Nominal Data – lowest form of data assigning codes to categories. • Ordinal Data – data elements are rank-ordered on the basis of some relationship with the assigned values indicating this order. • Interval Data – data items can be measured on scale and the data have ordinal properties. • Ratio Data – have a true zero point.

  12. (Page 47) Example 1 – 1 Categorizing Data News and World Report Step 1. Identify each factor in the data set. Step 2. Determine whether the data are time- series or cross-sectional. Step 3. Determine which factors are quantitative or qualitative data. Step 4. Determine the level of data measurement for each factor.

  13. (Page 47)

  14. (Page 48)

  15. (Page 43)

  16. (Page 43) Exercises 1-32, 1-34, 1-35 1-32 Population – all objects or individuals Sample – subset of population 1-34 a. Cluster Random Sampling b. Stratified Random Sampling c. Convenience Sampling 1-35 not on chance selection/convenience sampling

  17. (Page 43) Additional Exercises: 1 – 38, 1 – 42, 1 – 43 Answers: 1-38 Statistics 1-42 Statistics 1-43 a. Cluster/Stratified Random Sampling b. Simple Random Sampling c. Systematic Random Sampling d. Stratified Random Sampling

  18. (Page 48)

  19. (Page 48) Exercises : 1-49, 1-51, 1-55 1-49 a. time-series 1-55 a. nominal b. cross sectional b. ratio c. time-series c. nominal d. cross sectional d. ratio e. ratio 1-51 a. ordinal f. nominal b. nominal g. ratio c. ratio d. nominal

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