LAISHA
Open Laisha

ASRB NET Data Analysis with Statistical Software & Reproducible Research: practice questions

ASRB NET - Agricultural Statistics (and Computer Application / Bioinformatics) · India · Agricultural Scientists Recruitment Board (ICAR)

Original practice questions written to the published syllabus · not past papers
7worked here, free
16written to this exam in this subject
10syllabus units

What this subject covers

Running 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)

Where the 16 questions fall

How our questions for ASRB NET spread across the units of Data Analysis with Statistical Software & Reproducible Research. Difficulty: 3 easy, 12 medium, 1 hard.

Spreadsheets: statistical functions and data-analysis tools2
SPSS and point-and-click tools: uses and limits2
Genstat and specialist software for agricultural research2
Survey analysis software: design-based estimation with weights2
Data management: cleaning, validation and metadata2
Web-based statistical computing portals for agricultural researchers2
R for designed experiments: aov, lm, emmeans and agricolae1
SAS: PROC GLM, MIXED, REG and SURVEYMEANS1
Literate programming: R Markdown, Quarto and notebooks1
Version control with Git1

7 practice questions, worked

Choose an answer, then open the working. Each explanation says why the right answer is right and why the others are not.

1 · Spreadsheets: statistical functions and data-analysis tools · medium

When two independent groups may have different variances, the safer spreadsheet t-test is:

  1. The paired test (on the differences)
  2. The equal-variance test
  3. The F-test for variances alone
  4. Welch's unequal-variance test
Answer and working

D. Welch's unequal-variance test

Welch's test does not pool variances and adjusts degrees of freedom.

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)

2 · R for designed experiments: aov, lm, emmeans and agricolae · hard

In aov(yield ~ A * B + Error(block/A)), the main-plot factor A is tested against:

  1. The residual subplot error
  2. The block stratum only
  3. The block-by-A stratum
  4. The total variance
Answer and working

C. The block-by-A stratum

The Error term defines the strata of a split plot.

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)

3 · SAS: PROC GLM, MIXED, REG and SURVEYMEANS · medium

Which PROC REG option diagnoses collinearity?

  1. VIF
  2. PDIFF
  3. DOMAIN
  4. RANDOM
Answer and working

A. VIF

Variance inflation factors measure how much collinearity inflates variances.

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)

4 · SPSS and point-and-click tools: uses and limits · medium

A risk of point-and-click analysis is that:

  1. Menus cannot run any form of t-test or ANOVA
  2. Defaults need not suit the design
  3. Output is always incorrect
  4. Data cannot be labelled
Answer and working

B. Defaults need not suit the design

Sums-of-squares type, contrasts and missing-data handling are easy to leave at defaults.

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)

5 · Genstat and specialist software for agricultural research · easy

REML for unbalanced mixed models was derived by Patterson and Thompson in:

  1. 1925
  2. 1990
  3. 2005
  4. 1971
Answer and working

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)

6 · Genstat and specialist software for agricultural research · easy

Reports of statistical analyses should name:

  1. Only the p-values
  2. The analyst's computer brand
  3. Nothing about software
  4. The software and its version
Answer and working

D. The software and its version

Versions affect defaults and 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)

7 · Web-based statistical computing portals for agricultural researchers · medium

A quick plausibility check of a portal's ANOVA output is to verify:

  1. The colour scheme
  2. The server location
  3. The page title
  4. The error degrees of freedom
Answer and working

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)

Ten here, 16 in the app. The Laisha app has every practice question we have written to ASRB NET 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.

Practise the full set in Laisha →

Read the subject

Data Analysis with Statistical Software & Reproducible ResearchThe full study manual, free ASRB NETFormat, sections and how it has changed

More ASRB NET practice

Small Area Estimation & Model-Based Survey InferenceBioinformatics & Computational BiologySampling Theory II: Unequal Probability, Cluster, Multistage & Repeated SamplingFactorial Experiments, Confounding, Split- & Strip-Plot DesignsResponse Surface, Mixture & Optimal DesignsStatistical Genetics: Population & Quantitative GeneticsBiometrical Techniques in Plant & Animal BreedingSpecialised Agricultural Experiments: Cropping Systems, Animal & On-Farm TrialsStatistical Genomics: QTL Mapping, GWAS & Genomic SelectionIncomplete Block Designs & Design ConstructionGroups of Experiments, Multi-Location Trials & Genotype x Environment AnalysisLinear Mixed Models, Variance Components & Spatial Analysis of Field TrialsAdvanced Design of Experiments (PhD)Bioassay, Survival & Epidemiological StatisticsCrop Area & Yield Estimation: Crop-Cutting ExperimentsSurvey Design, Questionnaires, Field Operations & Non-Sampling ErrorsLinear Models & Regression AnalysisCategorical Data Analysis & Generalized Linear ModelsPrinciples of Experimental Design & Basic DesignsOptimization Techniques for StatisticiansMultivariate AnalysisCrop Forecasting, Weather-Based Models & Non-linear Growth ModelsSampling Theory I: Simple, Stratified, Systematic, Ratio & Regression EstimationNonparametric & Rank-Based Methods

Data Analysis with Statistical Software & Reproducible Research for other exams

ICAR-IASRI MSc and PhD · IndiaICAR AICE-JRF/SRF · India

Ask Laisha about this

Opens Laisha's chat with your question ready to send. Sign in or start free.