Conceptual Test
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
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)
Which of the following may be consequences of one or more of the CLRM assumptions being violated? i) The coefficient estimates are not optimal ii) The standard error estimates are not optimal iii) The distributions assumed for the test statistics are inappropriate iv) Conclusions regarding the strength of relationships between the dependent and independent variables may be invalid.
(ii) and (iv) only
(i) and (iii) only
(i), (ii), and (iii) only
(i), (ii), (iii), and (iv)
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