Question

1. If a test is robust, what does this allow us to do?

2.When we state that there is homogeneity of variance, precisely which variances are homogeneous?

3.If you underestimate sigma, the standard deviation of the population, the result is

a. the t-statstic being too small

b. a smaller probability of type i error

c. the estimated standard error being too large

d. all of the above

e. none of the above

Answer 1

1)In robustness studies for hypotheses testing is very useful the p-value,observed significance level, so if a test is robust, it allows us to assess the strength of evidence against a particular null hypothesis (H0)...besides this for robustness the outcome is significant or not is only meaningful if the assumptions of the test are met or not...

Model is considered to be robust if its output and forecasts are consistently accurate even if one or more of the input variables or assumptions are drastically changed due to unforeseen circumstances....

2)When we state that there is homogeneity of variance, precisely population variances are homogeneous...

3)

Hence None of above(E)