Irwin/McGraw-Hill © Andrew F. Siegel, 1997 and 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

Irwin/McGraw-Hill © Andrew F. Siegel, 1997 and 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, …

Irwin/McGraw-Hill © Andrew F. Siegel, 1997 and  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 2.0 Statistical Concepts and Language How Many Variables?

Irwin/McGraw-Hill © Andrew F. Siegel, 1997 and  Population Consist of all the items or individuals about which you want to reach conclusions  Sample The portion of a population selected for analysis 2.1 The Difference Between the Population and a Sample

Irwin/McGraw-Hill © Andrew F. Siegel, 1997 and  Population parameter A measure that describes a characteristics of a population  Sample statistics A measure that describes a characteristics of a sample 2.2 The Difference Between the Parameter and a Statistics

Irwin/McGraw-Hill © Andrew F. Siegel, 1997 and 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

Irwin/McGraw-Hill © Andrew F. Siegel, 1997 and 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

Irwin/McGraw-Hill © Andrew F. Siegel, 1997 and Sampling Methods  Type of Sampling Method Probability Sampling Simple Random Sampling Stratified Sampling Cluster Sampling Systematic Sampling Nonprobability Sampling Convenience Sampling

Irwin/McGraw-Hill © Andrew F. Siegel, 1997 and Sampling Methods  Probability Sampling Simple Random Sampling every item from a frame has the same chance of selection as every other item.

Irwin/McGraw-Hill © Andrew F. Siegel, 1997 and 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

Irwin/McGraw-Hill © Andrew F. Siegel, 1997 and 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.

Irwin/McGraw-Hill © Andrew F. Siegel, 1997 and 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.

Irwin/McGraw-Hill © Andrew F. Siegel, 1997 and 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.