How do I approach Statistics & Math interview questions?

Statistics & Math questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master statistics & math interviews.

What difficulty level is this interview question?

This is a medium difficulty Statistics & Math question, commonly asked during Technical Screen rounds at Google.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Google during technical interviews.

Assess Fundamental Statistics Knowledge in Data-Science Interviews

Quick Overview

This question evaluates fundamental statistics competencies including parameter estimation (MLEs for normal models), manipulation of joint and conditional densities, familiarity with the standard normal pdf and cdf, and estimator properties such as unbiasedness, and falls under the Statistics & Math domain.

Fundamental Statistics (Technical Phone Screen)

Context

You are given standard statistics tasks commonly used in a data-science interview. Assume all random variables and densities are well-defined and integrable where needed. For Question 1, let X₁, …, Xₙ be an i.i.d. sample from Normal(μ, σ²).

Questions

Maximum Likelihood Estimation (Normal):

Given an i.i.d. sample from a Normal distribution N(μ, σ²), derive the maximum-likelihood estimators (MLEs) for μ and σ².

Conditional Density from a Joint Density:

For continuous random variables X and Y with a known joint pdf f_{X,Y}(x, y), derive the conditional pdf f_{X|Y}(x | y).

Standard Normal Distribution:

Write the pdf φ(z) and cdf Φ(z) of the standard Normal distribution.

Unbiasedness Assumptions (Concrete Task):

Pick a concrete estimation task (e.g., estimating a population mean). List and justify the assumptions required for the estimator to be unbiased, and show a brief verification under those assumptions.

Quick Overview

Questions

Maximum Likelihood Estimation (Normal):

Given an i.i.d. sample from a Normal distribution N(μ, σ²), derive the maximum-likelihood estimators (MLEs) for μ and σ².

Conditional Density from a Joint Density:

For continuous random variables X and Y with a known joint pdf f_{X,Y}(x, y), derive the conditional pdf f_{X|Y}(x | y).

Standard Normal Distribution:

Write the pdf φ(z) and cdf Φ(z) of the standard Normal distribution.

Unbiasedness Assumptions (Concrete Task):

Pick a concrete estimation task (e.g., estimating a population mean). List and justify the assumptions required for the estimator to be unbiased, and show a brief verification under those assumptions.

Assess Fundamental Statistics Knowledge in Data-Science Interviews

Quick Overview

Fundamental Statistics (Technical Phone Screen)

Context

Questions

Solution

Comments (0)

Assess Fundamental Statistics Knowledge in Data-Science Interviews

Quick Overview

Fundamental Statistics (Technical Phone Screen)

Context

Questions

Solution

Comments (0)