stat exam

Looking at your full study guide, we can break it down into flashcards by concept, formula, distribution, and tips. Here’s an estimate based on your content:

Week 1 – Introduction & Why Statistics Matters (~8 flashcards)

  1. Q: Why is statistics important?
    A: Interprets data, explains variation, supports decisions. Example: Stephen Jay Gould used distribution shape to understand survival odds.

  2. Q: What caused the Challenger disaster?
    A: O-ring failure in Solid Rocket Motor; illustrates predictive engineering data.

  3. Q: Define random sample.
    A: Every individual has an equal chance of selection.

  4. Q: Define stratified random sample.
    A: Population divided into groups; random samples taken from each.

  5. Q: Define cluster sample.
    A: Entire groups (clusters) are randomly selected.

  6. Q: Define systematic sample.
    A: Every k-th member of the population is selected.

  7. Q: Keyword tip for identifying stratified sampling?
    A: “Divide into groups.”

  8. Q: Keyword tip for identifying simple random sampling?
    A: “All members have equal chance.”

Week 2 – Descriptive Statistics & Graphs (~6 flashcards)

  1. Q: Classify customer satisfaction: Quantitative/Qualitative & Subtype?
    A: Qualitative, Ordinal

  2. Q: Classify energy consumption: Quantitative/Qualitative & Subtype?
    A: Quantitative, Continuous

  3. Q: Classify number of defective items: Quantitative/Qualitative & Subtype?
    A: Quantitative, Discrete

  4. Q: Graph for identifying outliers using quartiles?
    A: Boxplot

  5. Q: Graph for visualizing correlation between two variables?
    A: Scatterplot

  6. Q: How do outliers affect mean vs median/IQR?
    A: Affect mean, not median or IQR

Week 3 – Central Tendency, Variation & Z-scores (~8 flashcards)

  1. Q: Measures of central tendency are also called?
    A: Location parameters

  2. Q: Purpose of trimmed mean / moving average?
    A: Smooth out fluctuations in data

  3. Q: Measure of variation sensitive to outliers?
    A: Range

  4. Q: Measure of variation hard to interpret?
    A: Variance

  5. Q: Measure of variation resistant to outliers?
    A: IQR

  6. Q: Empirical Rule percentages?
    A: 68% 1 SD, 95% 2 SD, 99.7% 3 SD

  7. Q: Z-score formula & meaning?
    A: z = (x – μ)/σ; measures how far a value is from mean in SD units

  8. Q: Symmetric ≠ Normal; Kurtosis = 0 ≠ skewness = 0?
    A: True; symmetry doesn’t imply normality

Week 4 – Probability (~10 flashcards)

  1. Q: Basic probability formula?
    A: P(A) = # favorable outcomes / total outcomes

  2. Q: Complement rule formula?
    A: P(A′) = 1 – P(A)

  3. Q: Union (OR) formula?
    A: P(A ∪ B) = P(A) + P(B) – P(A ∩ B)

  4. Q: Intersection (AND, independent) formula?
    A: P(A ∩ B) = P(A) × P(B)

  5. Q: Conditional probability formula?
    A: P(A|B) = P(A ∩ B) / P(B)

  6. Q: Bayes’ Theorem formula?
    A: P(A|B) = [P(B|A) × P(A)] / P(B)

  7. Q: Permutation vs combination?
    A: nPr = n!/(n–r)! (order matters), nCr = n!/[r!(n–r)!] (order doesn’t matter)

  8. Q: Key tip for probability questions?
    A: Define sample space first; update with new evidence (Bayesian view)

  9. Q: Probability of events requires careful counting?
    A: Yes, consider constraints like max dice value, specific outcomes

Week 5 – Probability Distributions (~5 flashcards)

  1. Q: Binomial distribution scenario?
    A: Fixed # trials, success/failure, independent (e.g., 10 students submitting on time)

  2. Q: Geometric distribution scenario?
    A: # of trials until first success (e.g., 5th attempt)

  3. Q: Hypergeometric distribution scenario?
    A: Successes without replacement (e.g., tagged fish)

  4. Q: Poisson distribution scenario?
    A: # of events in fixed interval (e.g., customers per hour)

  5. Q: Red Bead activity illustrates which distribution?
    A: Hypergeometric

Week 6 – Sampling & Normality (~4 flashcards)

  1. Q: How to check normality with Backward Empirical Rule?
    A: Compare observed % within 1, 2, 3 SDs with 68-95-99.7 rule

  2. Q: Exponential distribution scenario?
    A: Waiting time between Poisson events

  3. Q: Central Limit Theorem?
    A: Sample mean → Normal as n increases; standard error = σ/√n

  4. Q: Larger sample size effect?
    A: Reduces variability (smaller SE)

Week 7 – Confidence Intervals (~4 flashcards)

  1. Q: CI formula for mean?
    A: x̄ ± z*σ/√n

  2. Q: CI formula for proportion?
    A: p̂ ± z*√[p̂(1–p̂)/n]

  3. Q: Effect of sample size ≥ 30 on distribution choice?
    A: Can use z-distribution instead of t-distribution

  4. Q: CI interpretation?
    A: “We are 95% confident the population mean or SD lies within this interval”

Additional Tips Flashcards (~5 flashcards)

  1. Q: For z-scores, what must you check?
    A: Correct mean and SD

  2. Q: Frequency table setup?
    A: Find min/max, divide range into 5 bins, count frequencies

  3. Q: Expected value formula (e.g., roulette)?
    A: E(X) = Σ(Payout × Probability)

  4. Q: Column bet probability on roulette?
    A: 12/38

  5. Q: Normality checks practical methods?
    A: IQR/S method, histogram, empirical rule