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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 Populations, 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) Parameters – descriptive numerical measures computed from an entire population Statistics – descriptive numerical measures computed from a sample
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.
(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
(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.
(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.
1.4 Data Types and Data Measurement Levels Chapter 1 (Page 44)
(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.
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.
(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
(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.
(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.
(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
(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
(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