Chapter 1: Intro and Data Collection Flashcards
What a manager needs to know about statistics
- to know how to properly present information
- to know how to draw conclusions about populations based on sample information
- to know how to improve processes
- to know how to obtain reliable forecasts
what is the historical growth of statistics
- growth and development of modern stat
(needs of gov. to collect data on its citizenry) - development of the mathematics of probability theory
- evolution of electronic computing
Define population
is the collection of things under consideration
universe
define sample
a portion of the population selected for analysis
define parameter
a summary measure computed to describe a characteristic of the population
define statistics
a summary measure computed to describe a characteristic of the sample
define descriptive statistics
collection, presentation and characterization of data
define inferential statistics
drawing conclusions about the population based on the SAMPLE’s DATA
define data
set of numbers that represent some type of metric or measure
define information
what the data means
what is descriptive statistics
and provide examples
how data is organized, described and presented
statistics such as: mean(average), variance, range etec
organized and presented using:
tables, charts etc
what is inferential statistics
drawing conclusions about he population based on the samples’ data
What is involved in descriptive statistics
- data collection (eg survey)
- Data presentation (tables and graphs)
- Data Characterization (sample mean)
What is involved in inferential statistics
- estimation (eg. estimate the population mean weight using the sample mean weight)
- Hypothesis testing
(eg. test the claim that the population mean weigh tis 120 lbs)
drawing conclusions and /or making decisions concerning a population based on sample results
Why is data needed\?
- to provide input to survey
- to provide input to study
- to measure performance of service or production process
- to evaluate conformance to standards
- to assist in formulating alternative courses of action
- to satisfy curiosity
What are different types of data sources
- primary (data collection)
observation, survey, experimentation - Secondary (data compilation)
print or electronic
what are types of random variables
- categorical (qualitative)
2. Numerical (Quantitative)
what are the two branches of numerical data
- discrete (counting)
2. Continuous (measuring)
what is the definition of random variables
definition of possible outcomes of interest for an experiment
what are the reasons for drawing a sample
- less time consuming
- less costly to administer than a census
- less cumbersome and more practical to administer than a census of the targeted population
What are the types of sampling methods
- non-probability samples
2. probability samples
what are the non-probability samples
- judgement
- Quota
- chunk
What are the probability samples
- simple random
- systematic
- stratified
- cluster
Define judgement
need
define quota
need
define chunk
need
define simple random
every individual or item from the frame has an equal chance of being selected
- maybe with replacement or without replacement
- samples obtained from table of random numbers or computer random number generators
define systematic
decided on a sample size: n
divided frame of N individuals into groups of k individuals
k = N/n
randomly select one individual from the 1st group
- select every k-th individual thereafter
N = 64 n = 8 k = 8
define stratified
- population is divided into 2 or more groups according to some common characteristic
- simple random sample selected from each group
- into 2 or more samples are combined into one
define cluster
- population is divided into several “clusters” each representative of the population
- simple random sample selected from each
- the samples are combined into 1
what are the advantages and disadvantages of probability samples - simple random sample and systematic sample
- simple to use
- may not be a good representation of the populations underlying characteristic s
what are the advantages and disadvantages of probability sampling - stratified sample
- ensures representation of individuals across the entire population
what are the advantages and disadvantages of probability sampling - cluster sample
- more cost effective
- less efficient (need larger sample to acquire the same level of precision)
What are the key questions to evaluating survey worthiness
- what is the purpose of the survey?
- Is the survey based on a probability sample
- Coverage error - appropriate frame?
- Non- response error - followed up?
- Measurement error - good questions elicit good responses
- sampling error - always exists reduced by taking large samples
what are types of sampling errors
- coverage error - excluded from frame
- Non-response error
- Sampling error - chance differences from sample to sample
- Measurement error - bad questions
