EDA Flashcards by Ma Theresa Valencia

'’The data gathering, a systematic method of
collecting and measuring data’’ from different
sources of information in order to provide answers to relevant questions

Methods of Data Collection

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Person who conducts the inquiry

Investigator

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Person who helps in collecting information

Enumerator

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Three Basic Methods of Collecting Data

● Retrospective Study
● Observational Study
● Designed Experiments

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'’Use the population or sample of the historical data’’ which had been archived over some period of time

Retrospective Study

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'’Process or population is observed’’ and the
quantities of interests are recorded

Observational Study

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'’Deliberate or purposeful changes’’ in the
controllable variables of the system or process is
done

Designed Experiment

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'’Method of asking respondents’’ some
well-constructed questions. An efficient way of
collecting information and easy to administer
wherein a wide variety of information can be
collected

Surveys

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Where information is collected

Respondent

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'’Method is convenient and economical’’ but the
inferences made based on the findings are not so reliable. Also called judgment or subjective sampling

Non-probability Sampling

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Most common types of non-probability
sampling

Convenience Sampling
Purposive Sampling
Quota Sampling

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'’Researchers use a device in obtaining the information’’ from the respondents which favors the researchers but can cause bias to the respondents

Convenience Sampling

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Selection of respondents is ‘‘predetermined according to the characteristics of interest’’ made by the researcher

Purposive Sampling

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proportion of groups are considered

Quota sampling

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Two Types of Quota Sampling

Proportional quota sampling
Non-proportional quota sampling

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'’Major characteristics of the population’’ by
sampling a proportional amount of each is
represented

Proportional quota sampling

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'’Bit less restrictive’’. A minimum number of
‘‘sampled units in each category is specified
and not concerned’’ with having numbers
that match the proportions in the population

Non-proportional quota sampling

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Every member of the population is ‘‘given an equal chance to be selected’’ as a part of the sample
Not biased; reliable than non probability

Probability Sampling

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Types of Probability Sampling

Simple Random Sampling
Stratified Sampling
Cluster Sampling
Systemic or Systematic Random Sampling

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'’Basic sampling technique’’ where a group of
subjects (a sample) is selected for study from a
larger group (a population)

Simple Random Sampling

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'’Obtained by taking samples’’ from each stratum or sub-group of a population

Stratified Sample

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Sampling technique where the ‘‘entire population is divided’’ into groups, or clusters, and a random
sample of these clusters are selected

Cluster Sampling

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'’Tool to develop an experimentation strategy’’ that maximizes learning using minimum resources
Technique needed to identify the “vital few” factors in the most efficient manner and then directs the process to its best setting to meet the ever-increasing demand for improved quality and increased productivity

Design of Experiments (DOE)

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Six stages to be carried out for the design of
experiments

● Describe
● Specify
● Design
● Collect
● Fit
● Predict

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''Identifying objectives'' and important factors that is relevant in carrying out the experiment

Decribe

Determining the ''best setting'' in accomplishing the objective of the experiment

Specify

''Design model'' process that will be used in the experiment and conduct ''initial run test ór initial trial''

Design

generate and record data runs

Collect

''Validate the result of the trial through conducting ''additional runs to confirm if objectives were achieved

Fit

''Branch of mathematics concerned with theories of uncertainty'', ways of measuring uncertainty and the application of techniques involving uncertainty

Probability

Branch of mathematics that examine and investigate ways to process and analyzed the data gathered

Statistics

Two Major Area of Statistics

1. Descriptive Statistics 2. Inferential Statistics

Includes method concerned with collecting, organizing, summarizing and presenting data ''without drawing inferences about a large group''

Descriptive Statistics

- Also called as statistical or inductive statistics - Methods concerned with the analysis of a subset of data ''leading to predictions and inferences about the entire set of data''

Inferential Statistics

Statistical Terms

-Population - Sample - Parameter - Statistics - Constant - Variable

''Refers to the totality'' of objects, persons, places, things used in particular study

Population

any subset of population or few members of a population (elements obtaion from population)

Sample

the ''descriptive measure of a characteristics of a population''

Parameter

a measure of a characteristics of sample (numerical value describe sample)

Statistic

a ''characteristic or property of a population or sample'' which is common to all members of the group (same)

Constant

a measure or characteristic or property of population or ''sample that may have a number of different values''

Variable

Types of Variables

Qualitative and Quantitative Variables

measure a quality or characteristic on each experiment unit

Qualitative Variable

measure a numerical quantity or amount on each experiment

Quantitative Variable

''No inherent orders''' ; gender & civil status

Nominal

''Inherent order'' ; educ background, grades, quality of service

Ordinal

With equal intervals; temperature

Interval

Comparing; employment time (?)

Ratio

the number of times a score or group of score (class) occurs in a population or sample

Frequency

the frequency of one score or group of scores ''divided by the total frequency of all the observations''

Relative Frequency

Describing data by the amount measured in each category

Pie Chart Bar Chart

Used for daily/weekly/monthly records; sales. Describing data by time series

Line Chart

''height of the bar represents the proportion or relative frequency'' of occurrence for a particular class or sub-interval being measured

Relative Frequency Histogram

''Process that takes raw'', ungrouped data and summarizes it in a table form. Gives you a way to organize and describe a data set

Frequency Distribution

It is a ''tabulation of data showing the frequency of occurrence'' of the different values of the variable.

ungrouped data

It is a ''tabulation of data showing the number of observations that fall in each of the classes''.

grouped data

a symbol defining the arbitrary groupings.

Class/Class Interval

the end numbers of the class or class interval.

Class Limits

difference between two successive lower class limits or two successive upper class limits.

Class Interval Size

''halfway'' between the lower limit of one class and the upper limit of the preceding. It is the exact limit.

Class Boundary

the ''midpoint'' between the upper and lower class boundaries or class limits of a class interval

Class Mark

the difference between upper and lower class boundaries of a class interval.

Class Width

the number of observations falling in a particular class.

Class Frequency (f)

the frequency of one observation or group of observations divided by the total frequency of all observations.

Relative Frequency

the frequency of any class plus the frequencies of all preceding class in a distribution.

Cumulative Frequency

a ''vertical bar graph'' that shows the frequencies of scores or classes of scores by the height of the bar.

Histogram

''a graph on which the frequencies of classes are plotted at the class mark'' and the class marks are connected by straight lines.

Frequency Polygon

Properties of Frequency Distribution

Central location Variation Kurtosis Skewness

Distribution center of value near the frequency

Central Location

Extent of spreading out of individual measures from the measure of central tendencies

Variation

Flatness or peakedness

Kurtosis

Symmetry or asymmetry from the center

Skewness

the arithmetic average of all the scores or group of scores in a distribution.

Mean

point on the scale of measurement that ''divides a series of ranked observations into halves'' such that half of the observations fall above it and the other half fall below it.

Median

point on the measurement scale with the'' maximum frequency'' in the given distribution

Mode

is a summary statistic that ''represents the amount of dispersion in a dataset''.

Measures of Variability

indicates that the data points tend to be clustered tightly around the center

Low Dispersion

signifies that they tend to fall further away

High dispersion

Why measures of variability Important?

measures of variability helps as grasp the likelihood of unusual events.

is used where it is the difference between the largest score and the smallest scores in a given distribution

Exclusive range

is used when it is difference between the exact upper limit of the class interval containing the largest score and the exact lower limit of the class interval containing the smallest score.

Inclusive range

the point in a distribution below which a specified percent of the cases of observations lie

percentile

are the values that divide a set of observations into 4 equal parts

quartile

are the values that divide a set of observations into 10 equal parts

decile

The set of all possible outcomes of a statistical experiment

sample space

Each outcome in a sample space

element, or a member, or simply a sample point.

uses a probability value based on an educated guess or estimate, employing opinions and inexact information

SUBJECTIVE METHOD

sample space with a large or infinite number of sample points are best described by a what?

statement or rule method

A subset of a sample space.

event

each repetition of experiment

trial

result of trial

outcome

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