EDAAAAA Flashcards by Lara Yzabelle Barrera

✓ science that deals with techniques for collecting, presenting, analyzing and drawing conclusions from data
✓ science of data

Statistics (Singular)

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✓ numerical descriptions by which we enhance understanding of data
✓ summary measures used to describe a sample

statistics (Plural)

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What is statistics?
Ott and Longnecker (2016)

statistics is the science of learning from data
✓defining the problem
✓collecting the data
✓summarizing the data
✓analyzing the data
✓interpreting the results
✓communicating the results

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Why study statistics?

✓statistics provides a more formal way of organizing information
than relying on anecdotes and personal experience
✓more and more things are now measured numerically
✓results of experiments must be expressed quantitatively
✓statistics provides an objective way of measuring variation and uncertainty

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Role of statistics in engineering

✓engineering practice involve collecting, working with, and using data in the
solution of a problem, so knowledge of statistics is just as important to the
engineer as knowledge of any of the other engineering sciences
✓statistical methods are a powerful tool in model verification, designing new
products and systems, improving existing designs, and designing,
developing, and improving production operations
✓statistical methods are used to help us describe and understand variability
✓statistical thinking can give us a useful way to incorporate this variability
into our decision-making processes

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is the entire collection of objects or
outcomes about which data are collected. According to
Johnson and Bhattacharyya (2011), it is the complete
collection of units about which information is sought
➢A statistical population is the set of measurements
corresponding to the ______ of interest.

Population

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Statistical Inquiry
Almeda et al. (2010)

✓Identify the problem
✓Plan the study
✓Collect the data
✓Explore the data
✓Analyze the data and interpret the results
✓Present the results

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size has the same chance of being
selected from the population

random sample

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is a subset of the population containing the
observed objects or the outcomes and the resulting data.

sample

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Methods of selecting a sample

simple random sampling,
systematic sampling,
cluster sampling, and
stratified sampling

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is a characteristic or attribute which varies from one entity to another entity, e.g. age, tensile strength, no. of buildings

variable

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is one which quantifies an element of a
sample or population, e.g. strength of concrete, height of building

quantitative variable

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A quantitative variable that can assume only a finite or countably
infinite number of possible values (usually integers) is called

discrete variable

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is one which classifies/identifies/describes
an element of a sample or population, e.g. sex of an engineer, type of construction materials

qualitative variable

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A quantitative variable that can theoretically assume any value in a specified interval (i.e., continuum) is called a

continuous variable

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➢ data categories are mutually exclusive
➢data categories have no logical order
➢ex. Religion, civil status

Nominal or Classificatory Scale

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process of assigning numbers to characteristics
according to a defined rule

Measurement

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➢possesses the characteristics of ordinal scale
➢equal unit scale, no true zero point
➢ratios of magnitude are not meaningful
➢ex. gpa, achievement test score

Interval Scale

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Also called as scales of measurement

Levels of Measurement

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➢data categories are mutually exclusive
➢data categories have some logical order
➢defined to order or rank or data categories are scaled according to the amount of the particular characteristics they possess
➢ex. grading system, t-shirt size

Ordinal or Ranking Scale

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➢highest level
➢possesses the characteristics of ordinal scale
➢equal unit scale, true zero point
➢ex. height, weight

Ratio Scale

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▪ uses either all or a sample of the historical process data from some
period of time.
▪ quickest and easiest way to collect engineering process data, often
provide limited useful information for controlling and analyzing a
process
▪ Using historical data always involves the risk that, for whatever reason,
some of the important data were not collected or were lost or were
inaccurately transcribed or recorded.
▪ Consequently, historical data often suffer from problems with data
quality. These errors also make historical data prone to outliers

Retrospective study

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the engineer observes the process or
population, disturbing it as little as possible, and records the
quantities of interest.
▪ These studies are usually conducted for a relatively short time
period, sometimes variables that are not routinely measured can
be included

Observational study

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▪ the engineer makes deliberate or purposeful changes in controllable
variables (called factors) of the system, observes the resulting system
output, and then makes a decision or an inference about which
variables are responsible for the changes that he or she observes in the
output performance

Designed experiments

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is a process of obtaining a representative sample from a homogeneous (more or less the same characteristic under study) population in which each sample of size n has an equal chance of being selected; either in sampling with replacement or sampling without replacement.

Simple Random Sampling

the ratio of the sample size to the population size

sampling fraction

is a process of obtaining a representative sample (sample clusters for one-stage sampling or units sampled in clusters for two-stage sampling) from a homogeneous population of clusters already existed/defined.

Cluster Sampling

is a process of obtaining a representative sample from a homogeneous population with uniform time/space/interval. In this procedure, the sampling frame is divided into consecutive segments

Systematic Sampling

is a process of obtaining a representative sample (units in every stratum) from a heterogeneous population formed into homogeneous groups (strata). In selecting sampling units in every stratum, proportional allocation (each stratum contributes to the sample proportional to its size in the population) is used.

Stratified Sampling

underrepresents or overrepresents the characteristic of the population.

biased sample

occurs due to the behavior of the interviewer or respondent.

response bias

may occur if confusing or leading questions are asked.

wording effect bias

may occur if the person selected for the interview cannot be contacted or refuses to answer.

nonresponse bias

occurs if part of the target population is left out of the selection process.

Undercoverage bias

occurs if not part of the target population is included in the selection process.

Overcoverage bias

a well-defined process or planned inquiry undertaken to obtain new facts or to confirm or deny the results of the previous studies

Experiment

includes the plan and actual procedure of conducting the experimen

Experimental Design

is a variable that attempts to explain differences among responses; also called as independent variable

Explanatory variable

is the smallest unit (person, animal, building, item, object, etc.) to which a given treatment is applied

Experimental unit

are the values of a factor used in the experiment

Levels

is a variable whose effect on the response is of interest in the experiment; either quantitative factor or qualitative factor

Factor

are the factor-level combinations used in the experiment

Treatments

is a variable whose effect on the response cannot be separated from the effect of the treatments

Confounding variable

is a group of homogeneous (similar in characteristics) experimental units

Block

is a technique used to control the effects of factors that you can control easily

Blocking

there is only one population of interest, and a single sample is drawn from it; can be used to determine whether a process meets a certain standard.

One-sample experiment

is a technique used to balance the effects of factors you cannot easily control; used to average out the effects of extraneous factors on responses

Randomization

is said to be present when one factor produces a different pattern of responses at one level of a second factor that it does at another level

Interaction effect

is the variation in responses among the treatments

Between-treatment variation

refers to the number of times a treatment is applied to the experimental units in an experiment

Replication

there are two or more populations of interest, and a sample is drawn from each population; the usual purpose is to make comparisons among populations; each process corresponds to a separate population, and the measurements made on the specimens from a particular process are considered to be a simple random sample from that population

Multisample experiment

is the variation in responses at each treatment

Within-treatment variation

In many multisample experiments, the populations are distinguished from one another by the varying of one or more factors that may affect the outcome; each combination of the factors for which data are collected defines a population, and a simple random sample is drawn from each population; its purpose is to determine how varying the levels of the factors affects the outcome being measured.

Factorial experiments