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Statistical and Probabilistic Analysis Flashcards

7 cards from real MS-DS Master of Data science practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.

Read the first 7 Statistical and Probabilistic Analysis flashcards as text
  1. The Kolmogorov-Smirnov test is used to compare which of the following?

    Answer: An empirical distribution to a reference distribution, or two empirical distributions

    The KS test measures the maximum difference between cumulative distribution functions, used for goodness-of-fit or two-sample comparisons.

  2. If X ~ Binomial(n=10, p=0.5), what is Var(X)?

    Answer: 2.5

    Variance of a Binomial is np(1-p) = 10 × 0.5 × 0.5 = 2.5.

  3. In multiple hypothesis testing, the Bonferroni correction adjusts the significance threshold by:

    Answer: Dividing α by the number of tests

    Bonferroni sets the per-test threshold to α/m (where m is the number of tests) to control the family-wise error rate.

  4. A researcher notices that residuals from a regression model increase in spread as fitted values increase. This pattern indicates:

    Answer: Heteroscedasticity

    Heteroscedasticity is non-constant variance in residuals, often visible as a fan shape in residual plots.

  5. The Central Limit Theorem guarantees that the sampling distribution of the mean approaches normality as n increases, regardless of population shape. Which condition is essential?

    Answer: The samples must be independent and identically distributed with finite variance

    CLT requires i.i.d. sampling and finite population variance; sample size of ~30 is often sufficient for most distributions.

  6. Maximum Likelihood Estimation (MLE) selects parameters that:

    Answer: Maximize the probability of observing the given sample under the assumed model

    MLE finds parameters θ that maximize L(θ|data), the likelihood of the observed data under the parametric model.

  7. Given two events A and B where P(A) = 0.5 and P(B|A) = 0.4, what is P(A ∩ B)?

    Answer: 0.2

    By the multiplication rule, P(A ∩ B) = P(B|A) × P(A) = 0.4 × 0.5 = 0.2.