INTRODUCTION AND DATA COLLECTION

a. Why a manager needs to know about statistics
b. The growth and development of modern statistics
c. Statistical thinking and modern statistics
d. Descriptive versus inferential statistics
e. The need for data
f. Data source
g. Types of data and measurement scales


a. Why a manager needs to know about statistics
Managers need to know :
How to properly present and describe information
How to draw conclusion about large population based on information from sample
How to improve processes
How to obtain reliable forecasts of variable of interest.

b. The growth and development of modern statistics
It can be traced to three separate phenomena :
The needs of the Government to collect data on its citizenry
The development of the mathematics of probability theory
The advent of the computer
Profoundly changed in the last 30 years

c. Statistical thinking and modern statistics
Statistical thinking can be defined as thought processes
that focus on ways to understand, manage, and reduce
variation

d. Descriptive versus inferential statistics
Descriptive statistics can be defined as those methods involving the collection, presentation, and characterization of a set of data in order to describe the various features of that set of data properly.
Inferential statistics can be defined as those methods that make possible the estimation of the characteristic of a population or the making of a decision concerning a population based on sample results.
A population is the totality of items or things under consideration
A sample is the portion of the population that is selected for analysis
A parameter is a summary measure that is computed to describe a characteristic of an entire population
A statistic is a summary measure that is computed to describe a characteristic from only a sample of the population

e. The need for data
Data are needed to :
Provide the necessary input to a survey
Measure performance in an ongoing service or Production processes
Assist in formulating alternative courses of action in a decision-making process
Satisfy our curiosity

f. Data source
The data collector is the primary source
The data compiler is the secondary source
Four main reasons for collecting data :
to provide input to a research study
To measure performance
To enhance decision making
To satisfy our curiosity

g. Type of data and Measurement scales
Types of data
Two types of characteristics of random variable :
Categorical random variables yield categorical responses
Numerical random variables yield numerical responses

Types of measurement scales :
Nominal scale
Ordinal scale
Interval scale
Ratio scale
The need for operational definitions
An operational definition provides a meaning to a concept or variable that can be communicated to other individuals.
It is something that has the same meaning yesterday, today and tomorrow to all individuals
Types of samples
Non-probability sample and probability sample
Non-probability sample such as judgment sample, quota sampling and chunk sampling.

A probability sample is one in which the subjects of the sample
are chosen on the basis of known probabilities.
Simple random sample
Systematic sample
Stratified sample
Cluster sample

There are four types of survey error :
Coverage error or specification bias
Non-response error
Sampling error
Measurement error

A man with one watch always knows what time it is
A man with 2 watches always searches to identify the correct one
A man with 10 watches is always reminded of the difficulty in measuring time