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Courses » Total Quality Management - II

Unit 5 - Week 4 : Factorial Design

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Week 1 : Quality Engineering, Quality Function Deployment

Week 2 : Analysis of Variance

Week 3 : Introduction to Design of Experiments

Week 4 : Factorial Design

Lecture 16:

Factorial Designs - II Lecture 17:

Factorial Designs - III Lecture 18:

Factorial Designs - IV Lecture 19:

Factorial Designs - V Lecture 20:

Factorial Designs - VI Quiz : Assignment-04 Assignment-04 (Solution)

Due on 2019-02-27, 23:59 IST.

1) 1 point

2) 1 point

3) 1 point

Assignment-04

The due date for submitting this assignment has passed.

As per our records you have not submitted this assignment.

Which of the following statement is TRUE?

I. Bartlett's test is sensitive to departures from normality. That is, if your samples come from non-normal distributions, then Bartlett's test may simply be testing for non-normality.

II. Dunnett’s Test compares variance from several experimental groups against a control group variance to see if there is a difference.

III. Tukey's test compares the means of every treatment to the means of every other treatment; that is, it applies simultaneously to the set of all pairwise comparisons μ_i-μ_j and identifies any difference between two means that is greater than the expected standard error.

I and II I and III II and III

All the statements are TRUE No, the answer is incorrect.

Score: 0

Accepted Answers:

I and III

You have carried out a Kruskal-Wallis test. There are significant differences between the three groups you are testing. How might you conduct your pairwise comparisons?

Use the Wilcoxon test.

Use a t-test.

Use the Mann Whitney test.

None of the above. Post hoc analyses cannot be carried out with non-parametric data.

No, the answer is incorrect.

Score: 0

Accepted Answers:

Use the Mann Whitney test.

In a factorial design, a main effect is the __________.

the combined effect of the independent variables on the dependent variable.

A project of In association with

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Week 5 : Factorial Design of Experiments

Week 6 : Design and Analysis Topics for Factorial and Fractional Factorial Designs

Week 7 :Factorial and Fractional Factorial Designs Introduction to Linear Regression Models

Week 8 : Fitting Regression Models

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Total Quality Management - II- Week 4 Feedback

4) 1 point

5) 1 point

6) 1 point

7) 1 point

interaction effect of the independent variables and their effect on the dependent variable.

the effect of each independent variable on the dependent variable.

interaction of the independent variables.

No, the answer is incorrect.

Score: 0

Accepted Answers:

the effect of each independent variable on the dependent variable.

To find out whether noise affects the capability to solve the assignment of Quality management, one group solve the assignment in a silent room and another group solve the

assignment in a noisy room. The group solving problems in the silent room completes 22 problems in one hour and the group solving problems in the noisy room completes 15 problems in one hour. In this experiment, the independent variable is ____________ and the dependent variable is _____________.

The difficulty of the problem; the noise level in the room The noise level in the room; the difficulty of the problem The noise level in the room; the number of problems solved The number of problems solved; the noise level in the room No, the answer is incorrect.

Score: 0

Accepted Answers:

The noise level in the room; the number of problems solved If an interaction is significant, _____.

Tukey HSD tests of marginal means should be conducted main effects are unimportant

Tukey HSD tests should not be used a t-test should be conducted No, the answer is incorrect.

Score: 0

Accepted Answers:

Tukey HSD tests should not be used

Under what circumstances would you use a non- parametric test?

When you do not really understand a parametric test.

When you think your sample, size is too big.

In a pilot study.

When your data does not meet the assumptions for a parametric test.

No, the answer is incorrect.

Score: 0

Accepted Answers:

When your data does not meet the assumptions for a parametric test.

Factorial designs may be classified as all of the following EXCEPT _____.

correlations

between-subjects designs within-subjects designs mixed designs No, the answer is incorrect.

Total Quality Management - II - - Unit 5 - Week 4... https://onlinecourses.nptel.ac.in/noc19_mg19/un...

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8) 1 point

9) 1 point

10) 1 point

Score: 0

Accepted Answers:

correlations

An ANOVA procedure is applied to data obtained from 5 samples, where each sample contains 9 observations. The degrees of freedom for the critical value of F are

5 numerator and 9 denominator degrees of freedom 4 numerator and 8 denominator degrees of freedom 45 degrees of freedom

4 numerator and 40 denominator degrees of freedom No, the answer is incorrect.

Score: 0

Accepted Answers:

4 numerator and 40 denominator degrees of freedom

Assuming no bias, the total variation in a response variable is due to error (unexplained

variation) plus differences due to treatments (known variation). If known variation is large compared to unexplained variation, which of the following conclusions is the best?

There is no evidence for a difference in response due to treatments.

There is evidence for a difference in response due to treatments.

There is significant evidence for a difference in response due to treatments.

The treatments are not comparable.

No, the answer is incorrect.

Score: 0

Accepted Answers:

There is significant evidence for a difference in response due to treatments.

In the statistical model on which the one–way ANOVA is predicated, it is assumed that

in the population, there is homogeneity of variance across treatment groups.

there is always some basis for pairing the scores in any two groups.

participants under different conditions are tested by different experimenters.

in the population, there is homogeneity of covariance or sphericity.

No, the answer is incorrect.

Score: 0

Accepted Answers:

in the population, there is homogeneity of variance across treatment groups.

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