PREVIOUS YEAR SOLVED PAPERS - June 2019
57. Match List-I with List-II:
| List-I (Objectives of business firms) | List-II (Hypothesis) |
| (a) Maximization of firms’ growth rate | (i) Baumol’s hypothesis |
| (b) Managerial utility function | (ii) Marris hypothesis |
| (c) Satisfying behaviour | (iii) Williamson hypothesis |
| (d) Sales Maximization | (iv) Cyert March hypothesis |
Codes
| (a) | (b) | (c) | (d) | |
| 1 | (ii) | (iii) | (iv) | (i) |
| 2 | (iii) | (iv) | (i) | (ii) |
| 3 | (iv) | (i) | (ii) | (iii) |
| 4 | (i) | (ii) | (iii) | (iv) |
59. Match List-I with List-II:
| List-I | List-II |
| (a) Ind AS-16 | (i) Income tax |
| (b) Ind AS-38 | (ii) Leasing |
| (c) Ind AS-17 | (iii) Intangible assets |
| (d) Ind AS-12 | (iv) Property, plant and equipment |
Codes
| (a) | (b) | (c) | (d) | |
| 1 | (iv) | (iii) | (i) | (ii) |
| 2 | (iv) | (iii) | (ii) | (i) |
| 3 | (iii) | (ii) | (iv) | (i) |
| 4 | (iv) | (ii) | (i) | (iii) |
- Option : A
- Explanation : Non-parametric tests vs. Parametric tests: Tests appropriate for analyzing ordinal and nominal data are called non-parametric tests. In contrast, tests for analyzing interval or ratio scale are called parametric tests. Parametric tests (z, t or F) require that certain assumption s be valid concerning the population from where the samples were drawn while non-parametric require few assumptions.Tests involving ranks of data are non-parametric. Non-parametric tests are not as powerful as parametric statistics and tend to err on the conservative side.
Examples of Non-parametric tests:
∎ Chi-square test: Test of hypothesis to determine if categorical data shows dependency or if two classifications are independent.
∎ One sample sign test: Test of hypothesis related single value for given data.
∎ Two sample sign test, Fisher-Irwin test, Rank sum test (Wilcoxon-Mann-Whitney test i.e., U test, Kruskal-Wallis test i.e., H test), Wilcoxon Matched pairs test/Signed Rank test: Test of hypothesis related no difference among two or more sets of data.
∎ Charles Spearman’s rank correlation, Kendall’s Coefficient of concordance: Test of hypotheses related to relationship between variables.
∎ Kruskal-Wallis test: Test of hypothesis between more than two sets of data are analogous to ANOVA in parametric test.
