Total Quality Management Set 12 MCQs

Q1 | What is the value at the position of (III)?

0.04

0.85

0.15

0.033

Q2 | What are residuals?

Residuals are the differences between the observed and expected dependent variable scores

Serendipitous findings

Extreme scores

Uncontrolled variables

Q3 | An unbiased dice is rolled once. The probability of getting a number greater than 4 is:

1/4

1/6

1/2

1/3

Q4 | Which of the following statement is true?

Only factor A is significant; factor B and the two-factor interaction are not significant.

Only factor B is significant; factor A and the two-factor interaction are not significant.

Only the two-factor interaction is significant; factor A and factor B are not significant.

Factor A and factor B is significant; the two-factor interaction is not significant.

Q5 | What is the purpose of a simple linear regression?

To predict scores on a dependent variable from scores on a single independent variable

To predict scores on an independent variable from scores on a single dependent variable

To predict scores on an independent variable from scores on multiple dependent variables

To predict scores on a dependent variable from scores on multiple independent variables

Q6 | Attribute charts may be used when:

Several characteristics can be jointly measured

When one particular quality characteristic is of importance

Specific information like process mean is required

None of these

Q7 | Assume the cholesterol levels in a certain population have mean μ = 200 and standard deviation σ = 24. The cholesterol levels for a random sample of n = 9 individuals are measured and the sample mean x-bar is determined. What is the z-score for a sample mean x = 180?

-3.75

-2.50

-0.83

2.50

Q8 | In hypothesis testing, a Type 2 error occurs when

The null hypothesis is not rejected when the null hypothesis is true.

The null hypothesis is rejected when the null hypothesis is true.

The null hypothesis is not rejected when the alternative hypothesis is true.

The null hypothesis is rejected when the alternative hypothesis is true.

Q9 | Student's t-statistic is applicable in case of:

Equal number of samples

Unequal number of samples

Small samples

All of the above

Q10 | Which of the following assumptions must be met to use an ANOVA?

There is only one dependent variable

The data must be normally distributed

There is homogeneity of variance

All of these

Q11 | Which of the following is an assumption of one-way ANOVA comparing samples fromthree ‘or more experimental treatments?

All the response variables within the k populations follow Normal distributions.

The samples associated with each population are randomly selected and are independent from all other samples.

The response variable within each of the k populations has equal variances.

All of the above.

Q12 | When conducting a one-way ANOVA, the ______________ the between-treatmentvariability is when compared to the within-treatment variability, the ______________ the FValue calculated from the data will tend to be.

smaller, larger

smaller, smaller

larger, larger

smaller, larger

Q13 | You carried out an ANOVA on a preliminary sample of data. You then collected additional data from the same groups; the difference being that the sample sizes for each group were increased by a factor of 10, and the within-group variability has decreased substantially. Which of the following statements is NOT correct?

The degrees of freedom associated with the error term has increased

The degrees of freedom associated with the treatment term has increased

The Residual Sum of Squares (SSE) has decreased

The F-Value calculated from the data (F-Value) has changed

Q14 | In the two-factor, between subjects (or two-way) ANOVA:

The three F tests always have the same power to reject the null hypothesis.

The test for an interaction always has more power than the test for a main effect.

The power of the F test is not necessarily increased by having larger samples.

The three F tests do not always have the same power to reject the null hypothesis.

Q15 | A ______________ effect(s) analysis examines mean differences at each level of theindependent variable.

main

simple main

interaction

simple interaction

Q16 | Factorial designs allow us to study both ______________ effects of the independentvariables on the dependent variables.

main and interactive

dependent and independent

symbiotic and dichotomous

rank order and correlation

Q17 | A researcher conducted a 2 x 2 completely repeated measures factorial design and planned 15 participants in each condition. How many participants would be required to conduct this experiment?

15

30

45

60

Q18 | Consider two factors A and B, each with two levels. If there is no interaction between thesetwo factors, the difference in the response variable between the two levels of factor A would be ______________ the difference between the two levels of factor B.

unrelated to

equal to

half

twice

Q19 | Dr. RNS conducted a ______________ factorial design to examine the effects of music androom temperature on participant's memory. Participants were randomly assigned to study a list of nonsense words either listening or not listening to music in either a warm or cold room.

1 x 2

2 x 4

2 x 2

4 x 4

Q20 | In factorial designs, the response produced when the treatments of one factor interact withthe treatments of another in influencing the response variable is known as

the main effect

interaction

replication

none of these

Q21 | What is the Variation due to Cache?

84%

76%

19%

5%

Q22 | What is the value at the position of (V)?

0.85

0.15

0.97

0.33

Q23 | The standard error of regression is

the square root of the variance of the error term

an estimate of the square root of the variance of the error term

the square root of the variance of the dependent variable

the square root of the variance of the predictions of the dependent variable

Q24 | What is the purpose of a multiple regression?

To predict scores on a dependent variable from scores on a single independent variable

To predict scores on an independent variable from scores on a single dependent variable

To predict scores on an independent variable from scores on multiple dependent variables

To predict scores on a dependent variable from scores on multiple independent variables

Q25 | The value of E[X] is:

52/18

28/9

52/9

28/18