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Daily Quiz- 211

GK bank

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Q1. The Mahalanobis Growth Model under the Second Five-Year Plan emphasised:
a) Agricultural development through land reforms
b) Export-led growth
c) Heavy industry-led industrialisation
d) Import substitution through light industries
Q2. Which of the following was a limitation of the Mahalanobis Model?
1. Overemphasis on heavy industry
2. Neglect of agriculture
3. Rapid employment generation
4. Foreign exchange shortages
Options:
a) 1, 2 and 4 only
b) 2 and 3 only
c) 1, 3 and 4 only
d) 1, 2, 3 and 4
Q3. The Mahalanobis Model can best be described as a:
a) Three-sector model (agriculture, industry, services)
b) Two-sector model (investment goods, consumer goods)
c) One-sector model (industrial planning only)
d) Five-sector model
Q4. Who among the following economists criticized the Mahalanobis heavy-industry strategy for being capital-intensive and neglecting employment?
a) Amartya Sen
b) Vakil and Brahmananda
c) Kautilya and DR Gadgil
d) Manmohan Singh
Q5. Which of the following institutions was founded by PC Mahalanobis in 1931?
a) National Council of Applied Economic Research (NCAER)
b) Indian Statistical Institute (ISI)
c) Planning Commission
d) National Development Council

Answers Quiz-210 (September 4)

1. b) It is the birth anniversary of PC Mahalanobis
Explanation: June 29 marks Mahalanobis’ birth anniversary (1893), celebrated as National Statistics Day since 2007.
2. b) 75 Years of National Sample Survey
Explanation: In 2025, the focus was on 75 years of NSS, started in 1950 to strengthen household surveys and evidence-based policymaking.
3. b) Collect household-level socio-economic data
Explanation: NSS pioneered large-scale household surveys for socio-economic indicators, later feeding into Five-Year Plans.
4. b) Sankhyā
Explanation: Founded in 1933, Sankhyā was India’s first statistical research journal of global repute.
5. b) Multivariate statistical analysis
Explanation: Mahalanobis Distance helps in detecting outliers and correlations in multivariate datasets, widely used today in AI and data science.
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