What topics are addressed in Page 2 questions?

MA(1) process beta estimation, trend fitting by least squares, stratified sampling variance, and finite distributed lag models with OLS considerations.

What is the significance of ridge estimation?

Ridge estimation is used to tackle multicollinearity in regression by introducing a bias to reduce variance of estimates.

What is the interpretation of unbiased estimators in this paper?

An unbiased estimator has its expected value equal to the parameter it estimates, e.g., p̂ unbiased for P.

What is the focus of the MA(1) problem on Page 2?

Estimating β from autocovariances and discussing implications when γ̂1 takes certain values.

What is a finitely distributed lag model about?

A model that distributes the effect of a predictor across several lags; OLS estimation may require restrictions on lag weights for stability.

How many alternative titles are provided?

Three alternative titles are provided for SEO variations.

Statistics Paper III (2017) IES/ISS – Two Sections of Questions

Name: Statistics Paper III (2017) IES/ISS – Two Sections of Questions
Creator: IES/ISS
Keywords: SRSWOR, unbiased estimator, sampling variance, population proportion, multicollinearity, ridge estimation, MA(1), autocovariance, beta estimation, trend analysis, least squares, stratified sampling, population mean, variance estimation, finitely distributed lag

Central Government Jobs Other Jobs 2017

Year 2017
Conducted By IES/ISS
Questions 8
Maximum Marks 200
Duration Three Hours Question Paper Specific Instructions Please Read Each Of The Following Instructions Carefully Before Attempting Questions: There Are
Languages English

Exam Details

Detail	Information
Examination	IES/ISS EXAM
Year	2017
Conducting Body	IES/ISS
Paper	STATISTICS Paper – III
Subject	STATISTICS
Duration	Three Hours Question Paper Specific Instructions Please Read Each Of The Following Instructions Carefully Before Attempting Questions: There Are
Maximum Marks	200
Number of Questions	8
Question Type	Descriptive / Subjective

This SEO block covers IES/ISS Statistics Paper III (2017). The exam comprises eight questions across two sections: Section A has two compulsory questions on sampling (SRSWOR) and multicollinearity in regression; Section B contains six questions from which any three are to be answered. Page 2 presents repaired/descriptive problems focusing on MA(1) time-series estimation, linear trend fitting with production data, stratified sampling variance, and finite distributed lag models with OLS considerations and lag weight restrictions. The material emphasizes statistical estimation, variance, and model diagnostics within a government-exam context.

Major Topics Covered

Sampling theory
SRSWOR
Unbiased estimator
Population proportion
Sampling variance
Multicollinearity
Linear regression
Ridge estimation
Time series
MA(1) process
Autocovariance
Parameter estimation
Trend analysis
Least squares
Stratified sampling
Population mean
Variance estimation
Finitely distributed lag model
OLS estimation
Lag weights

Why This Paper is Important

Useful for IES/ISS EXAM preparation
Helps understand the latest exam pattern
Useful for practice and self-assessment
Covers frequently asked General Studies topics
Helpful for analysing question trends

Related Resources

Instructions

There are EIGHT questions divided under TWO sections.
Candidate has to attempt FIVE questions in all.

Questions (page 2)

Q1. Q1. Consider the MA
(1) process X_n = e_n + β e_n-1, where e_n ∼ N(0, 1).
(c) For a data set it is noted that autocovariances are \hatγ_0 = 1 and \hatγ_1 = -0.25. Estimate β. Which value of the estimate do you think we should
(i) choose and why? What problem do we have if \hatγ_1 = -0.5? How would the variance of
(ii) the error have affected the change?

Q2. Q2. (a) cooperative sugar factory : Year: 2010 2011 2012 2013 2014 2016 2015 Production: 77 88 84 85 91 98
90. Fit a linear trend by least squares method. Tabulate the trend
(i) values. Compute the monthly estimated increase in production during the
(ii) period.

Q3. Q3. If, in every stratum, the simple estimator \bary_h is unbiased, then show (b) that \overline{\mathbfy}_{\rm st} = ∑limits_{\rm t}^{\rm L} W_h \overline{\mathbfy}_{\rm h} is unbiased for population mean \bary, where W_h is the proportion of population units in the strata and L denotes the total number of strata in the population. Derive the sampling variance of \bary_{\rm st} and state how you would unbiasedly estimate the same.

Q4. Q4. In the context of a finitely distributed lag model, discuss the problem of
(c) OLS estimation and suggest how to obtain good (consistent) estimates of the parameters in such a model by bringing in some restrictions on lag weights.

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Frequently asked questions

What is the exam and year?

IES/ISS Exam, 2017, Statistics Paper – III.

How many sections are in the paper and what are they?

Two sections: Section A with two compulsory questions, Section B with six questions from which any three must be attempted.

What is the maximum marks and duration?

Maximum marks are 200 and the time allowed is Three Hours.

How many questions must be answered?

Eight questions are given; candidates must attempt five in total.

Are Section A questions compulsory?

Yes, both Section A questions are compulsory.

What topics are covered in Section A QI and QII?

QI covers sampling theory (SRSWOR) and unbiased estimators; QII covers multicollinearity in regression and ridge estimation.

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