Chapter 6
Introductory Statistics and Data
What is Statistics?
• Numerical facts
• Science that helps us collect, analyze, and interpret
data and then make decisions
Genetic Code of Statistics
• Dataset: a group of numbers or information
– Element: a member of the dataset
– Variable: a characteristic common to all members
– Observation: the value of a variable for an element
• Dataset: all FIU students
– Element: you!
– Variable: GPA
– Observation: 4.00
Genetic Code of Statistics
• Descriptive Statistics: Methods or techniques used to
summarize data
– Graphs: frequency
– Central Tendency: mean, median, mode
– Dispersion: std, deviation, variance
Population vs. Sample
• Population (Universe): the set of ALL elements
• Sample: a group of elements selected from a population
• Inferential (Inductive) Statistics: Process of drawing
information from sample observations and making
conclusions about the population
• Census: Colleting data from every element of a target
population
• Sample survey: Colleting data from elements in a sample
• Parameter: a numerical measurement of a population
characteristic
• Statistic: a numerical measurement of a sample char.
Sampling Techniques
• Sampling with replacement: an element was returned to
population after observation/measurement was made
• Sampling without replacement: an element was NOT
returned to population after observation/measurement was
made
Sampling Techniques
• Random Sampling: Each element of the population has
equal chance of being included in a sample
• Sequential Sampling: Elements are collected in
sequence
• Stratified Sampling: Elements are selected proportionally
from each process (source)
• Sample size: Number of elements in a sample
Random Sampling
• RAND() [Excel]
• Go to Calc
> Random Data
> Uniform
Stratified Sampling
www.socialresearchmethods.net/.../sampstrt.gif
Data
• Datum (singular)
• Data Characteristics:
– Center: value represents the location of the middle of
the dataset
– Variation: measure of the difference among the
elements of a dataset
– Distribution: shape of how the elements scatter
– Outliers: elements located far away from most elements
Data
• Qualitative, categorical, or attribute data
• Quantitative data
• Nominal-Scale: data are divided into categories (yes/no)
• Ordinal-Scale: data are divided into categories that can be
ranked, sub-division is not meaningful (ranking)
• Interval-Scale: categorical order is inherent, sub-division is
meaningful, no absolute zero point (IQ, F)
• Ratio-Scale: categorical order is inherent, sub-division is
meaningful, and with absolute zero point (weight, length)
Data
• Quantitative Variables and Data
– Discrete
– Continuous
• Qualitative/Categorical Variables and Data
Data Collection
• Control cards
• Data Collection Sheet
Database
• Relational database
• Object-oriented database