Tom 401 Test 2

Front 1

Statistics

Back 1

The science concerned with “the collection, organization, analysis, interpretation, and presentation of data

Front 2

experiment

Back 2

a process that results in some outcome.

Front 3

outcome

Back 3

a result that we observe

Front 4

sample space

Back 4

The collection of all possible outcomes of an experiment

Front 5

Probability

Back 5

the likelihood that an outcome occurs

Front 6

Probability Properties

Back 6

Label the n outcomes in a sample space as O1, O2, … On, where Oi represents the ith outcome in the sample space.

Front 7

event

Back 7

a collection of one or more outcomes from a sample space

Front 8

Calculating Probabilities

Back 8

Rule 1: The probability of any event is the sum of the probabilities of the outcomes that compose that event.

Rule 2: The probability of the complement of any event A is P(Ac) = 1 – P(A).

Rule 3: If events A and B are mutually exclusive, then P(A or B) = P(A) + P(B)

Rule 4: If two events A and B are not mutually exclusive, then P(A or B) = P(A) + P(B) – P(A and B)

Front 9

Conditional probability

Back 9

the probability of occurrence of one event A, given that another event B is known to be true or have already occurred

P(A|B) = P(A and B)/P(B)

Front 10

random variable, X

Back 10

a numerical description of the outcome of an experiment. Formally, a random variable is a function that assigns a numerical value to every possible outcome in a sample space.

Front 11

probability distribution, f(x),

Back 11

is a characterization of the possible values that a random variable may assume along with the probability of assuming these values.

Front 12

cumulative distribution function, F(x),

Back 12

specifies the probability that the random variable X will assume a value less than or equal to a specified value, x, denoted as P(X ≤ x).

Front 13

binomial distribution

Back 13

the probability of obtaining exactly x “successes” in a sequence of n identical experiments, called trials.

Front 14

Poisson Distribution

Back 14

Allows the sample size to become very large and the probability of success or failure to become very small while the expected value remains constant

Front 15

Probability Density Function

Back 15

A curve that characterizes outcomes of a continuous random variable, and is described by a mathematical function f(x).

Probabilities are only defined over intervals.

Front 16

standard normal distribution

Back 16

If a normal random variable has a mean μ = 0 and a standard deviation σ = 1,

It is represented by z

Front 17

Calculating Normal Probabilities

Back 17

z= (x-u)/s

Front 18

Exponential Distribution

Back 18

models the time between randomly occurring events, such as the time to or between failures of mechanical or electrical components.

Front 19

Simple Random Sampling

Back 19

Every item in the population has an equal probability of being selected.

Front 20

Stratified Sampling

Back 20

The population is partitioned into groups, or strata, and a sample is selected from each group

Front 21

Systematic Sampling

Back 21

Every nth (4th, 5th, etc.) item is selected.

Front 22

Cluster Sampling

Back 22

A population is partitioned into groups (clusters) and a sample of clusters is selected. Either all elements in the chosen clusters are included in the sample or a random sample is taken from each of them.

Front 23

Judgment Sampling

Back 23

Expert opinion is used to determine the sample.

Front 24

Sampling Error

Back 24

occurs naturally and results from the fact that a sample may not always be representative of the population, no matter how carefully it is selected.

Only way to reduce sampling error is to take a larger sample from the population

Front 25

Systematic Error

Back 25

result from poor sample design and can be reduced or eliminated by careful planning of the sampling study

Front 26

Population

Back 26

a complete set of collection of objects of interest

Front 27

Sample

Back 27

a subset of objects taken from the population

Front 28

mode

Back 28

occurs most frequently

Front 29

Range

Back 29

simplest measure of dispersion and is computed as the difference between the ex and min values in the data set

Front 30

variance

Back 30

a measure of dispersion that depends on all the data. The larger the variance, the more the data are "spread out" from the mean, and the more variability one can expect in the observations

Front 31

Skewness

Back 31

the lack of symmetry of data

If the mean and median are equal, then the distribution is symmetrical.

If the mean is greater than the median, then the distribution is skewed to the right.
Vice Versa

Front 32

Coefficient of skewnesss

Back 32

If CS is positive, the distribution of values is positively skewed; if negative, it is negatively skewed

The closer CS is to zero, the less the degree of skewness

Front 33

kurtosis

Back 33

refers to peakedness of a histogram

Front 34

Sampling Distribution

Back 34

the distribution of a statistic for all possible samples of a fixed size

Front 35

Confidence Intervals

Back 35

An interval estimate of a population parameter that also specifies the likelihood that the interval contains the true population parameter

Front 36

Idea Generation (Product Development Phase 1)

Back 36

New or redesigned product ideas should incorporate customer needs and expectations

Front 37

Preliminary Concept Development (Product Development Phase 2)

Back 37

New ideas are studied for feasibility

Front 38

Product/Process Development (Product Development Phase 3)

Back 38

If an idea survives the concept stage, the actual design process begins by evaluating design alternatives and determining engineering specifications for all materials, components, and parts.

Front 39

Full Scale Production (Product Development Phase 4)

Back 39

Once the design is approved and the production process has been set up, the company releases the product to manufacturing or service delivery teams

Front 40

Market Introduction (Product Development Phase 5)

Back 40

The product is distributed to customers

Front 41

Market Evaluation (Product Development Phase 6)

Back 41

Ongoing product development process that relies on market evaluation and customer feedback

Front 42

Concurrent Engineering

Back 42

a process in which all major functions involved with bringing a product to market are continuously involved with product development form conception through sales

Front 43

Design for Six Sigma (DFSS)

Back 43

represents a structured approach to product development and a set of tools and methodologies for ensuring that goods and services will meet customer needs and achieve performance objectives, and that the processes used to make and deliver them achieve high levels of quality.

Front 44

DFSS four principal activities

Back 44

Concept development
Detailed design
Design optimization
Design verification

Front 45

Concept development

Back 45

the process of applying scientific, engineering, and business knowledge to produce a basic functional design that meets both customer needs and manufacturing or service delivery requirements.

Front 46

Innovation

Back 46

involves the adoption of an idea, process, technology, product, or business model that is either new or new to its proposed application.

Front 47

Theory of Inventive Problem Solving (TRIZ)

Back 47

Developed by a Russian patent clerk who studied thousands of submissions, and observed patterns of innovation common to the evolution of scientific and technical advances.
He recognized that these concepts could be taught, and he developed some 200 exercises to foster creative problem solving.

Front 48

Axiomatic design

Back 48

based on the premise that good design is governed by laws similar to those in natural science.

Front 49

Independence Axiom

Back 49

good design occurs when the functional requirements of the design are independent of one another.

Front 50

Information Axiom

Back 50

good design corresponds to minimum complexity.

Front 51

Quality Function Deployment (QFD)

Back 51

is a planning process to guide the design, manufacturing, and marketing of goods by integrating the voice of the customer throughout the organization.

Front 52

Four Linked Houses of Quality

Back 52

Customer Requirements
v
Technical Requirements
v
Component Characteristics
v
Process Operations
v
Quality Control Plan

Front 53

Nominal dimensions

Back 53

the ideal dimension or target value that manufacturing seeks to meet

Front 54

tolerance

Back 54

permissible variation, recognizing the difficulty of meeting a target conistently

Front 55

tolerance design

Back 55

involves determining the permissible variation in a dimension

Front 56

Taguchi Loss Function

Back 56

Measures quality as the variation from the target value of a design specification, and then translates that variation into an economic "loss function" that expresses the cost of variation in monetary terms

Front 57

Reliability

Back 57

the probability that a product, piece of equipment, or system performs its intended function for a stated period of time under specified operating conditions.

Front 58

Key elements of reliability

Back 58

Probability

Time

Performance

Operating conditions

Front 59

Functional failure

Back 59

failure that occurs at the start of product life due to manufacturing or material detects

Front 60

Reliability failure

Back 60

failure after some period of use

Front 61

Inherent reliability

Back 61

predicted reliability determined by the design of the product or process.

Front 62

Achieved reliability

Back 62

actual reliability observed during use.

Front 63

Failure Rate

Back 63

Number of failures/Total unit operating hours

Front 64

mean time to failure (MTTF).

Back 64

For items that must be replaced when a failure occurs, the reciprocal of the failure rate

Front 65

mean time between failures (MTBF)

Back 65

For repairable items

Front 66

Product Life Characteristics Curve

Back 66

shows the instantaneous failure rate at any point in time (referred to as a "bathtub" curve)

Front 67

Infant Mortality Period

Back 67

early failure period

Front 68

reliability function, R(T),

Back 68

characterizes the probability of survival to time T.

Front 69

Series system

Back 69

all components must function or the system will fail.

Front 70

Parallel system

Back 70

uses redundancy. The system will successfully operate as long as one component functions

Front 71

Robust design

Back 71

designing goods and services that are insensitive to variation in manufacturing processes and when consumers use them

Front 72

Design failure mode and effects analysis (DFMEA)

Back 72

identification of all the ways in which a failure can occur, to estimate the effect and seriousness of the failure, and to recommend corrective design actions.

Front 73

Fault Tree Analysis

Back 73

a method to describe combinations of conditions or events that can lead to a failure.

Front 74

Design for Manufacturability

Back 74

the process of designing a product for efficient production at the highest level of quality

Front 75

Design for Excellence

Back 75

- an emerging concept that includes many design-related initiatives such as concurrent engineering, design for manufacturability, design for assembly, design for environment, and other “design for” approaches

Front 76

Life testing

Back 76

run devices until failure occurs

Front 77

Accelerated life testing

Back 77

overstress devices to reduce time to failure

Front 78

Highly accelerated life testing

Back 78

focused on discovering latent defects that would not otherwise be found through conventional methods.