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 Data Characteristics: Datum (singular) 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