Difficulty distribution
How the classified questions are distributed by difficulty.
1questions
Medium
1
100%
Multivariable Differentiation Theorems - Real Analysis - Mathematics Previous Year Questions
Practice Multivariable Differentiation Theorems - Real Analysis - Mathematics previous year questions organised from real papers, with year-wise coverage and clear topic navigation.
1Papers
1Years
1Questions
1Topics
Multivariable Differentiation Theorems question pattern
Every graph below is calculated only from this selection.
Questions by year
Compare question counts across years.
Question type distribution
MCQ, numerical, multiple-select and other formats found in these papers.
1questions
MCQ
1
100%
Subject weightage
Top subjects by unique question coverage.
Most asked topics
Top topics across the included previous year papers.
Subtopic coverage
Top subtopics inside this exact selection.
Paper coverage
Question coverage for the most populated papers. Every active PYP paper remains listed below.
Included previous year papers
Newest papers appear first. Search these papers or sort by year and name.
| Paper name | Year | Attempt | |
|---|---|---|---|
Mathematics (MA) 20142014 | 2014 |
|
|
All Multivariable Differentiation Theorems previous year questions
Practice every matching question in batches of 20, with every available option.
2014 · Mathematics · Real Analysis · Multivariable Differentiation Theorems
Mathematics (MA) 2014
Suppose that \(X_{1}, X_{2}, \ldots, X_{n}\) is a random sample of size \(n\) drawn from a population with probability density function
\(f(x ; \theta)=\begin{cases}\frac{x}{\theta^{2}} e^{-\frac{x}{\theta}} & \text { if } x>0 \\ 0 & \text { otherwise, }\end{cases}\)
where \(\theta\) is a parameter such that \(\theta>0\). The maximum likelihood estimator of \(\theta\) is
\(f(x ; \theta)=\begin{cases}\frac{x}{\theta^{2}} e^{-\frac{x}{\theta}} & \text { if } x>0 \\ 0 & \text { otherwise, }\end{cases}\)
where \(\theta\) is a parameter such that \(\theta>0\). The maximum likelihood estimator of \(\theta\) is