Conceptual Test

A visually engaging image depicting the concepts of probability sampling methods and regression analysis, featuring a blend of statistical graphs, sampling frames, and a regression equation, in a modern educational context with soft colors.

Probability Sampling and Regression Analysis Quiz

Test your understanding of probability sampling methods and regression analysis concepts with this engaging quiz! Whether you are studying statistics, preparing for a certification, or just curious about these topics, this quiz will challenge your knowledge and enhance your learning.

Key Features:

11 thought-provoking questions
Instant feedback on your answers
Suitable for students and professionals alike

11 Questions3 MinutesCreated by AnalyzingData312

Which of the following is not a probability sampling

Random Sampling

Stratified Sampling

Systematic Sampling

Quota Sampling

A Sampling frame is

A summary of the various stages involves in designing a survey

An outline view of all the main clusters in a sample

A list of all units in the population from which a sample will be selected

A wooden frame use to display tables of random variables

A simple random sampling is one in which

From a random starting point, every nth unit from the sampling frame is selected

A non-probability strategy is used, making the results difficult to generalize

The researcher has a certain quota of respondents to fill for various social groups

Every unit of the population has an equal chance of being selected

It is helpful to use a multi-stage cluster sample when:

The population is widely dispersed geographically

You have limited time and money available for travelling

You want to use a probability sample in order to generalise the results

All of the above

What effect does increasing the sample size have upon the sampling error?

It reduces the sampling error

It increases the sampling error

It has no effect on the sampling error

None of the above

The standard error is a statistical measure of:

The normal distribution of scores around the sample mean

The extent to which a sample mean is likely to differ from the population mean

The clustering of scores at each end of a survey scale

The degree to which a sample has been accurately stratified

What is the meaning of the term "heteroscedasticity"?

The variance of the errors is not constant

The variance of the dependent variable is not constant

The errors are not linearly independent of one another

The errors have non-zero mean

What would be then consequences for the OLS estimator if heteroscedasticity is present in a regression model but ignored?

It will be biased

It will be inconsistent

It will be inefficient

All of (a), (b) and (c) will be true.

Which of the following assumptions are required to show the consistency, unbiasedness and efficiency of the OLS estimator? i) E(ut) = 0 ii) Var(ut) = σ2 iii) Cov(ut, ut-j) = 0 iv) ut~N(0, σ2)

(ii) and (iv) only

(i) and (iii) only

(i), (ii), and (iii) only

(i), (ii), (iii), and (iv)