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l Chapter 2 lStatistical 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

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2.0 Statistical Concepts and LanguageData 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, …

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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 LanguageHow Many Variables?

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Population Consist of all the items or individuals about which you want to reach

conclusionsSample

The portion of a population selected for analysis

2.1 The Difference Between the Population and a Sample

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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

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2.3 Measurement LevelsQualitative 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

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2.3 Measurement LevelsQuantitative 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

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2.4 Sampling MethodsType of Sampling Method

Probability Sampling Simple Random Sampling Stratified Sampling Cluster Sampling Systematic Sampling

Nonprobability Sampling Convenience Sampling

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2.4 Sampling MethodsProbability Sampling

Simple Random Sampling every item from a frame has the same chance of selection as

every other item.

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2.4 Sampling MethodsProbability 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

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2.4 Sampling MethodsProbability 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.

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2.4 Sampling MethodsProbability 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.

Nk

n

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2.4 Sampling MethodsNonprobability 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.