Answer regression error and coin toss expectation
Regression Assumptions and Coin Toss Expectation
This problem consists of two parts:
Part 1: Multiple Choice - Regression Assumptions and Expectation
Which statement is most correct regarding the assumptions and error characteristics of Linear Regression versus Logistic Regression?
- A. In Linear Regression, errors are assumed to follow a normal distribution, while in Logistic Regression, the outcome likelihood is Bernoulli/binomial.
- B. In Linear Regression, we minimize squared errors, while in Logistic Regression, we minimize squared errors as well.
- C. In Linear Regression, residuals are always binary, while in Logistic Regression, residuals are continuous.
- D. Both Linear and Logistic Regression assume errors are homoscedastic and normally distributed.
Part 2: Expected Tosses for 3 Heads in a Row
What is the expected number of fair coin tosses needed to achieve the first occurrence of three consecutive heads?
- A. 6
- B. 8
- C. 14
- D. 20
AI interview prep
Core dilemma
For Part 1, the core dilemma is understanding the fundamental probabilistic models underlying different regression techniques and their implications for error/outcome interpretation. For Part 2, the core dilemma is setting up and solving a problem involving expected values and states, often requiring a Markov chain or recursive approach.
2 tradeoffs and 2 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Answer regression error and coin toss expectation
Regression Assumptions and Coin Toss Expectation
This problem consists of two parts:
Part 1: Multiple Choice - Regression Assumptions and Expectation
Which statement is most correct regarding the assumptions and error characteristics of Linear Regression versus Logistic Regression?
- A. In Linear Regression, errors are assumed to follow a normal distribution, while in Logistic Regression, the outcome likelihood is Bernoulli/binomial.
- B. In Linear Regression, we minimize squared errors, while in Logistic Regression, we minimize squared errors as well.
- C. In Linear Regression, residuals are always binary, while in Logistic Regression, residuals are continuous.
- D. Both Linear and Logistic Regression assume errors are homoscedastic and normally distributed.
Part 2: Expected Tosses for 3 Heads in a Row
What is the expected number of fair coin tosses needed to achieve the first occurrence of three consecutive heads?
- A. 6
- B. 8
- C. 14
- D. 20