Lean Six Sigma - Tsa Ii

Front 1

What is the difference between the capability indices (C) and performance indicators (P)

Back 1

C uses short term data and P uses long term data.

Front 2

What values can be entered in an SQL table in order to obtain the Sigma Quality Level?

Back 2

Sigma Quality Yield & Defects Per Million Opportunity (DPMO).

Front 3

Within the Sigma Quality Level Table, what amount of shift is built into the table values?

Back 3

One and a half standard deviations.

Front 4

2-Bin

Back 4

A type of pull system using two “bins” (a bin could be a card, or tote, or physical location, etc). The basic mechanics are as follows. Two bins of material are located at a station. When the first bin is emptied, the operator sends the empty bin to a location to be refilled, and begins working from the second bin. The first bin will be returned full prior to the emptying of the second bin.

Front 5

DPO

Back 5

Defects Per Opportunity

Front 6

DPMO

Back 6

Defects Per Million Opportunities

Front 7

SQL

Back 7

Sigma Quality Level

Front 8

Also known as an Ishikawa or Fishbone diagram

Back 8

Cause and Effect Diagram

Front 9

A technique for narrowing many possibilities to a few through an iterative selection process. Also called Multivoting.

Back 9

Nominal Group Technique (NGT)

Front 10

A technique for grouping large amounts of information into themes.

Back 10

Affinity Diagram

Front 11

An ordered bar chart ranking categorical data.

Back 11

Pareto Chart

Front 12

Explain the 80/20 rule

Back 12

20% of the inputs (x's) drive 80% of the variation

Front 13

A graphical representation of continuous data that is divided into quartiles

Back 13

Box Plot

Front 14

The three dimensions of variation studied during a Multi-Vari study

Back 14

Positional, Cyclical, Temporal

Front 15

What is the purpose of a cause-and-effect matrix (C&E Matrix)?

Back 15

To relate process inputs to process outputs

Front 16

Those items that go down the side of a cause-and-effect matrix

Back 16

Process steps and inputs

Front 17

The items that go across the top of a cause-and-effect matrix

Back 17

Output Variables

Front 18

What is the final result or output from a cause-and-effect matrix

Back 18

A Pareto Chart

Front 19

FMEA

Back 19

Failure Modes & Effects Analysis

Front 20

Three types of FMEAs

Back 20

Process, System, Design

Front 21

What is the source of the input to a FMEA?

Back 21

C&E Matrix, Pareto Chart

Front 22

What three areas make up the Risk Priority Number (RPN) in a FMEA?

Back 22

Severity, Occurence, Detection

Front 23

What is the output or result of performing an FMEA

Back 23

A Control Plan. Putting controls in place for critical steps, and a prioritized list of critical steps

Front 24

What is a Hypothesis Test?

Back 24

A procedure whereby one of two mutually exclusive and exhaustive statements about a population parameter is concluded. Information from a sample is used to infer something about a population from which the sample was drawn.

Front 25

What is PGA?

Back 25

A series of steps (practical, graphical, analytical) used to help get to know the data.

Front 26

What are the 3 steps of PGA?

Back 26

Practical, Graphical, Analytical

Front 27

Practical (PGA)

Back 27

Looking at the data series and understanding how the data was collected, if the data is continuous or attribute and if there is anything that stands out or is unusual about the data.

Front 28

Graphical (PGA)

Back 28

Graphing the data to see relationships and patterns. Typical graphs might be boxplots, run charts, histograms, and Pareto charts.

Front 29

Analytical (PGA)

Back 29

Calculate the sample statistics, mean, median, standard deviation, and normality.

Front 30

Confidence Interval

Back 30

A range of values, calculated from a data set, that gives an assigned probability that the true value lies within that range.

Front 31

Confidence Limit

Back 31

End points of a confidence interval

Front 32

Confidence Level

Back 32

the amount of confidence we have in any assertion that we make

Front 33

What determines whether or not to use statistical differences in hypothesis testing?

Back 33

The amount of variability, how much change is acceptable, and the amount of risk.

Front 34

Null Hypothesis

Back 34

Represents the conclusion that population parameters from two groups are the same.

Front 35

Alternative Hypothesis

Back 35

States that the Null Hypothesis is wrong. Represents the conclusion that population parameters from two groups are not the same.

Front 36

Alpha Risk

Back 36

(producer's risk) Assumes that the Null Hypothesis is not true, when it really is. The risk is that we say there is a change when there is not one.

Front 37

Beta Risk

Back 37

(consumer's risk) assumes that the Null Hypothesis is true when it is not. The risk of missing a change when there is really one.

Front 38

On a 2 t-Test, if the p-value is less than .05...

Back 38

The Null Hypothesis must be rejected. There is a statistical difference between two processes (machines, etc)

Front 39

On a 2 t-Test, if the p-value is greater than .05...

Back 39

The Null Hypothes cannot be rejected. There may not be a statistical difference between two processes (machines, etc).

Front 40

Hypothesis Testing: Type I Error

Back 40

You conclude that there is a difference when there really isn't. (reject Null Hypothesis)

Front 41

Hypothesis Testing: Type II Error

Back 41

You conclude that there is no difference when there really is. (The Null Hypothesis is not rejected)

Front 42

ANOVA

Back 42

Analysis of Variance

Front 43

Analysis of Variance (ANOVA)

Back 43

Used in cases where there are more than two groups. ANOVA tests for differences in average (mean) performance. Similar to a t-test.

Front 44

Paired t-Test

Back 44

A paired t-test recognizes and accounts for the fact that there is another factor at play.

Front 45

Chi-Square Test for Independence

Back 45

Chi-Square tests can tell us if there is a relationship between the counts we see and the factor whereby the counts are divided. (observed data minus expected data)

Front 46

Input Variables (x's) are also called...

Back 46

Predictor variables, or independent variables

Front 47

Output Variables (y's) are also called...

Back 47

Response variables, or dependent variables

Front 48

What type of data does linear regression use?

Back 48

Continuous (both x and y)

Front 49

Define independent and dependent variables

Back 49

Y or the response is dependent. X is independent.

Front 50

What does the value r describe on a scatter plot?

Back 50

r, describes how much of linear relationship exists between two variables. (The Pearson correlation coefficient)