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Statistical Inference and Regression Models
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Avg score: 50% • Most missed: “Point out the wrong statement with respect to FDR.”

MCQs on probability and statistics, basics of statistical inference, regression models, distributions and likelihood, binary and count outcomes and residual variations.

Statistical Inference and Regression Models
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25 Questions

1. What is the purpose of multiple testing in statistical inference?
2. Which of the following theorem states that the distribution of averages of iid variables, properly normalized, becomes that of a standard normal as the sample size increases?
3. Usually replacing the standard error by its estimated value does change the CLT.
4. Which of the following component is involved in generalized linear models?
5. Point out the wrong statement with respect to FDR.
6. Which of the following random variables are the default model for random samples?
7. The expected value or _______ of a random variable is the center of its distribution.
8. Which of the following is the correct formula for total variation?
9. Chebyshev’s inequality states that the probability of a “Six Sigma” event is less than ___________
10. Which of the following is a property of likelihood?
11. Which of the following goal is incorrectly represented in the below figure?
Find the causal goal from the given diagram
12. Which of the following outcome is odd man out in the below figure?
Kappa diagram
13. Which of the following things can be accomplished with linear model?
14. Bernoulli random variables take (only) the values 1 and 0.
15. Which of the following function can be replaced with the question mark in the below figure?
Levelplot diagram/>
16. Which of the following tool is used for constructing confidence intervals and calculating standard errors for difficult statistics?
17. Which of the following condition should be satisfied by function for pmf?
18. Which of the following of a random variable is a measure of spread?
19. Which of the following inequality is useful for interpreting variances?
20. Linear models are the most useful applied statistical technique.
21. CLT is mostly useful as an approximation.
22. How many components are present in generalized linear models?
23. Power is the probability of rejecting the null hypothesis when it is true.
24. Which of the following can be useful for diagnosing data entry errors?
25. Cumulative distribution functions are used to specify the distribution of multivariate random variables.

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