1. Statistical Concepts and Language
l Chapter 2 l
Statistical Concepts and Language
2.1 The Difference Between the Population and a Sample 2.2 The Difference Between the Parameter and a Statistics 2.3 Measurement Levels 2.4 Sampling Methods

2. 2.0 Statistical Concepts and Language
Data Set:
Measurements of items
e.g., Yearly sales volume for your 23 salespeople
e.g., Cost and number produced, daily, for the past month
Elementary Units:
The items being measured
e.g., Salespeople, Days, Companies, Catalogs, …
A Variable:
The type of measurement being done
e.g., Sales volume, Cost, Productivity, Number of defects, …

3. 2.0 Statistical Concepts and Language How Many Variables?
Univariate data set: One variable measured for each elementary unit
e.g., Sales for the top 30 computer companies.
Can do: Typical summary, diversity, special features
Bivariate data set: Two variables
e.g., Sales and # Employees for top 30 computer firms
Can also do: relationship, prediction
Multivariate data set: Three or more variables
e.g., Sales, # Employees, Inventories, Profits, …
Can also do: predict one from all other variables

4. 2.1 The Difference Between the Population and a Sample
Consist of all the items or individuals about which you want to reach conclusions
Sample
The portion of a population selected for analysis

5. 2.2 The Difference Between the Parameter and a Statistics
Population parameter
A measure that describes a characteristics of a population
Sample statistics
A measure that describes a characteristics of a sample

6. 2.3 Measurement Levels Qualitative Variable: Categories
Nominal Variable: categories without meaningful ordering
e.g., State, Type of business, Field of study
Can count
Ordinal Variable: Categories with meaningful ordering
e.g., The ranking of favorite sports, the order of people's place in a line, the order of runners finishing a race
Can rank, count

7. 2.3 Measurement Levels Quantitative Variable: Interval and Ratio
Interval Variable: like ordinal except we can say the intervals between each value are equally split
e.g., temperature
Can add, rank, count, without true zero
Ratio Variable: interval data with a natural zero point
e.g., Time and weight
Can add, rank, count, with true zero

8. 2.4 Sampling Methods Type of Sampling Method Simple Random Sampling
Probability Sampling
Simple Random Sampling
Stratified Sampling
Cluster Sampling
Systematic Sampling
Nonprobability Sampling
Convenience Sampling

9. 2.4 Sampling Methods Probability Sampling
Simple Random Sampling
every item from a frame has the same chance of selection as every other item.

10. 2.4 Sampling Methods Probability Sampling
Stratified Sampling
Subdivide the N items in the frame into separate subpopulations (strata). A stratum is defined by some common characteristic, e.g.: gender or year in school. Conduct simple random sampling within each strata and combine the results

11. 2.4 Sampling Methods Probability Sampling
Cluster Sampling
Divide the N items in the frame into clusters that contain several items. Clusters are often naturally occurring designations, such as counties, election districts, city blocks, households, or sales territories. Then take a random sample of one or more clusters and study all items in each selected cluster.

12. 2.4 Sampling Methods Probability Sampling
Systematic Sampling
Partitioned the N items in the frame into n groups of k items, where
and round k to the nearest
integer.
Then choose the first
item to be selected
at random from the first k items in the frame. Then, select the
remaining items by taking every kth item thereafter.

13. 2.4 Sampling Methods Nonprobability Sampling
Convenience/Accidental Sampling
Items selected are easy, inexpensive, or convenient to sample. For example, if you were sampling tires stacked in a warehouse, it would be much more convenient to sample tires at the top of a stack than tires at the bottom of a stack.