Transcript Populations, Samples, and Sampling Techniques

1.3 Populations, Samples,
and Sampling Techniques
Chapter 1
(Page 38)
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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
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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.
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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
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Statistical Sampling Methods – (probability
sampling) allow every item in the
population to have a chance of being
included in the sample.
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1. Simple Random Sampling – items from a
population has an equal chance of being
selected.
2. Stratified Random Sampling – items are
selected from each stratum (group) using
the simple random sampling.
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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
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Quantitative Data – measurements whose values are
numerical.
Qualitative Data – measurements is categorical.
Sample Exercises:
1.
2.
3.
4.
5.
6.
7.
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:
1.
2.
3.
4.
5.
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.
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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
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Data Measurement levels
1. Nominal Data – lowest form of data
assigning codes to categories.
2. Ordinal Data – data elements are rank-ordered
on the basis of some relationship with the
assigned values indicating this order.
3. Interval Data – data items can be measured on
scale and the data have ordinal properties.
4. Ratio Data – have a true zero point.
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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 timeseries or cross-sectional.
Step 3. Determine which factors are
quantitative or qualitative data.
Step 4. Determine the level of data
measurement for each factor.
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Exercises 1-32, 1-34, 1-35
1-32
1-34
1-35
Population – all objects or
individuals
Sample – subset of population
a. Cluster Random Sampling
b. Stratified Random Sampling
c. Convenience Sampling
not on chance
selection/convenience sampling
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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
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Exercises : 1-49, 1-51, 1-55
1-49 a. time-series
b. cross sectional
c. time-series
d. cross sectional
1-51 a. ordinal
b. nominal
c. ratio
d. nominal
1-55 a. nominal
b. ratio
c. nominal
d. ratio
e. ratio
f. nominal
g. ratio