CLSSGB Online Practice Questions and Answers

Multiple Linear Regressions (MLR) is best used when which of these are applicable? (Note: There are 3 correct answers).

A. Non-linear relationships between the inputs X's and output Y

B. Uncertainty in the slope of the linear relationship between an X and a Y

C. Relationships between Y (output) and more than one X (Input)

D. Preventing the use of a Designed Experiment if unnecessary

E. We assume that the X's are independent of each other

To draw inferences about a sample population being studied by modeling patterns of data in a way that accounts for randomness and uncertainty in the observations is known as ____________________.

A. Influential Analysis

B. Inferential Statistics

C. Physical Modeling

D. Sequential Inference

When a Belt is analyzing sample data she should keep in mind that 95% of Normally Distributed data is within +/- 2 Standard Deviations from the Mean.

A. True

B. False

Unequal Variances can be the result of differing types of distributions.

A. True

B. False

A process can be defined as a repetitive and systematic series of steps or activities where inputs are modified or assembled to achieve a customer desired result.

A. True

B. False

As a type of measurement error, Linearity describes a change in accuracy through the expected operating range of the measurement instrument.

A. True

B. False

Cost of Poor Quality (COPQ) can be classified as either Tangible (Visible) Costs or Hidden Costs.

A. True

B. False

Lean had its origins in the development and practice of the ___________ Production System.

A. Honda

B. Toyota

C. Ford

D. Motorola

The X-Y Diagram is a tool used to identify/collate potential X's and assess their relative impact on multiple Y's.

A. True

B. False

A Linear Regression model shows an R2 (adjusted) of 0.90 and a P-value of 0.002. A Quadratic Regression model of the same data shows an R2 (adjusted) of 0.92 and a P-value of 0.000. What can you conclude?

A. A linear model would be better than a quadratic model.

B. A quadratic model would be better than a linear model.

C. Any non-linear model would fit the data well.

D. Any linear or non-linear model would fit the data well.

E. Neither a linear or non-linear model fits the data well.

F. The Residuals would be expected to be large for either a linear or quadratic.