1. Chapter 6 Introductory Statistics and Data

2. What is Statistics? Numerical facts
Science that helps us collect, analyze, and interpret data and then make decisions

3. 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

4. Genetic Code of Statistics
Descriptive Statistics: Methods or techniques used to summarize data
Graphs: frequency
Central Tendency: mean, median, mode
Dispersion: std, deviation, variance

5. 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.

6. 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

7. 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

8. Random Sampling RAND() [Excel] Go to Calc > Random Data
> Uniform

9. Stratified Sampling

10. 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

11. 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)

12. Data Quantitative Variables and Data
Discrete
Continuous
Qualitative/Categorical Variables and Data

13. Data Collection
Control cards
Data Collection Sheet

14. Database
Relational database
Object-oriented database