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DAC Statistical Methods & Analytics Flashcards

6 cards from real DAC practice questions. Tap to flip, then mark Knew It or Still Learning โ€” missed cards come back until you master them.

Read the first 6 DAC Statistical Methods & Analytics flashcards as text
  1. What does the Central Limit Theorem state?

    Answer: As sample size increases, the sampling distribution of the mean approaches a normal distribution

    The Central Limit Theorem states that with a sufficiently large sample size, the distribution of sample means will approximate a normal distribution regardless of the population's shape.

  2. What is multicollinearity in multiple regression analysis?

    Answer: High correlation among two or more independent variables in the model

    Multicollinearity occurs when predictor variables in a regression model are highly correlated with each other, making it difficult to isolate their individual effects.

  3. Which type of sampling ensures every member of a population has an equal chance of being selected?

    Answer: Simple random sampling

    Simple random sampling gives every member of the population an equal and independent probability of selection, minimizing selection bias.

  4. What is a Type I error in hypothesis testing?

    Answer: Rejecting a true null hypothesis

    A Type I error (false positive) occurs when the null hypothesis is actually true but the test incorrectly rejects it, with probability equal to the significance level alpha.

  5. In data analytics, what is the interquartile range (IQR) used for?

    Answer: Identifying the middle 50% of data and detecting outliers

    The IQR is the difference between the 75th and 25th percentiles, representing the spread of the middle 50% of data and commonly used in outlier detection.

  6. What does a regression coefficient (slope) represent in a simple linear regression model?

    Answer: The expected change in the dependent variable for a one-unit increase in the independent variable

    The regression slope coefficient quantifies how much the predicted outcome changes for each one-unit increase in the predictor variable, holding other factors constant.