ICAR AICE-JRF/SRF - Agricultural Statistics, Computer Application, Bioinformatics (PhD admission and fellowship) · India · National Testing Agency for ICAR
Original practice questions written to the published syllabus · not past papersRunning real analyses in R, SAS, SPSS, Python and Genstat: importing and managing agricultural data, running designed-experiment, survey and genetics analyses, checking outputs, and making the work reproducible with scripts, literate documents and version control. Includes spreadsheet analysis tools that AIEEA-PG names.
In the exams: AIEEA-PG Group 07 'Excel data analysis tools, correlation and regression, t-test and one-way ANOVA' (heavy); AICE-JRF Ag Stat (applied); SAS certifications (heavy)
How our questions for ICAR AICE-JRF/SRF spread across the units of Data Analysis with Statistical Software & Reproducible Research. Difficulty: 3 easy, 15 medium, 6 hard.
| R for designed experiments: aov, lm, emmeans and agricolae | 4 | |
| Genstat and specialist software for agricultural research | 4 | |
| Survey analysis software: design-based estimation with weights | 4 | |
| SAS: PROC GLM, MIXED, REG and SURVEYMEANS | 3 | |
| Web-based statistical computing portals for agricultural researchers | 3 | |
| Literate programming: R Markdown, Quarto and notebooks | 2 | |
| Version control with Git | 2 | |
| SPSS and point-and-click tools: uses and limits | 1 | |
| Data management: cleaning, validation and metadata | 1 |
Choose an answer, then open the working. Each explanation says why the right answer is right and why the others are not.
For a split plot in blocks analysed with PROC MIXED, the random effects are specified as:
A. RANDOM block block*A;
Block and the main-plot error (block × A) are random.
Further reading: Welham et al., Statistical Methods in Biology (2015); Littell et al., SAS for Mixed Models (2006); Lumley, Complex Surveys (2010); Chacon and Straub, Pro Git (2014)
PROC GLIMMIX extends PROC MIXED to:
D. Non-normal mixed models
Counts and proportions are modelled with link functions.
Further reading: Welham et al., Statistical Methods in Biology (2015); Littell et al., SAS for Mixed Models (2006); Lumley, Complex Surveys (2010); Chacon and Straub, Pro Git (2014)
REML for unbalanced mixed models was derived by Patterson and Thompson in:
D. 1971
It allows recovery of inter-block information with unequal block sizes.
Further reading: Welham et al., Statistical Methods in Biology (2015); Littell et al., SAS for Mixed Models (2006); Lumley, Complex Surveys (2010); Chacon and Straub, Pro Git (2014)
Separable AR(1) × AR(1) models in field-trial analysis describe:
D. Row and column spatial correlation
Neighbouring plots are correlated in both directions.
Further reading: Welham et al., Statistical Methods in Biology (2015); Littell et al., SAS for Mixed Models (2006); Lumley, Complex Surveys (2010); Chacon and Straub, Pro Git (2014)
A stratum containing a single PSU causes a problem because:
C. Its variance is not estimable
Strata are collapsed or special options used.
Further reading: Welham et al., Statistical Methods in Biology (2015); Littell et al., SAS for Mixed Models (2006); Lumley, Complex Surveys (2010); Chacon and Straub, Pro Git (2014)
A parameterised report is useful for a multi-location trial because it:
B. Renders one template for each site
Routine reporting becomes efficient and consistent.
Further reading: Welham et al., Statistical Methods in Biology (2015); Littell et al., SAS for Mixed Models (2006); Lumley, Complex Surveys (2010); Chacon and Straub, Pro Git (2014)
Caching long computations in a dynamic document requires care that:
B. Caches are refreshed when inputs change
Stale caches give outdated results.
Further reading: Welham et al., Statistical Methods in Biology (2015); Littell et al., SAS for Mixed Models (2006); Lumley, Complex Surveys (2010); Chacon and Straub, Pro Git (2014)
Committing a password to a repository is serious mainly because:
C. It persists in the history
Even deleted files remain in earlier commits.
Further reading: Welham et al., Statistical Methods in Biology (2015); Littell et al., SAS for Mixed Models (2006); Lumley, Complex Surveys (2010); Chacon and Straub, Pro Git (2014)
A quick plausibility check of a portal's ANOVA output is to verify:
D. The error degrees of freedom
Wrong df reveal a mis-specified design.
Further reading: Welham et al., Statistical Methods in Biology (2015); Littell et al., SAS for Mixed Models (2006); Lumley, Complex Surveys (2010); Chacon and Straub, Pro Git (2014)
An experiment with repeated measurements over time on each plot is analysed in a portal offering only one-time RCBD analysis. The likely error is:
B. Repeated measures treated as independent
Correlation within plots over time must be modelled.
Further reading: Welham et al., Statistical Methods in Biology (2015); Littell et al., SAS for Mixed Models (2006); Lumley, Complex Surveys (2010); Chacon and Straub, Pro Git (2014)
Ten here, 24 in the app. The Laisha app has every practice question we have written to ICAR AICE-JRF/SRF in this subject, marked as you go, with timed papers, a jump-to-unanswered list and a certificate when you pass at 70 per cent.
Free for ten days, then one subscription of US$10 a month. We would rather say so here than have you find it later.
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