UGC NET Management Unit - VIII Syllabus

Unit - VIII syllabus — full chapter and topic list

Expand any chapter to see its topics and sub-topics. This is the whole examinable outline for Unit - VIII in UGC NET Management, not a summary of it.

1. 1

   Concept

   1 topic

   • Measures Of Central Tendency and Dispersion
2. 2

   Probability Distribution

   4 topics

   • Binomial
   • Poison
   • Normal
   • Exponential
3. 3

   Data Collection & Questionnaire Design

   1 topic

   • Sampling
     • Concept
     • Process
     • Techniques
4. 4

   Hypothesis Testing

   2 topics

   • Procedure
   • T, Z, F, Chi-square tests
5. 5

   Correlation and Regression Analysis

   overview

   Examined as a single unit within Unit - VIII — no further topic split in the official outline.

Unit - VIII flashcards for UGC NET Management

23 of 50 cards from the Unit - VIII deck — real questions with worked answers.

1. What are the three main measures of central tendency?

   Mean (arithmetic average), Median (middle value when data is ordered), and Mode (most frequently occurring value).

2. State the formula for the arithmetic mean of ungrouped data.

   Mean (x̄) = Σx / n, the sum of all observations divided by the number of observations.

3. How is the median located in a dataset with an even number of observations?

   It is the average of the two middle values: the mean of the (n/2)th and (n/2 + 1)th ordered observations.

4. What empirical relationship links Mean, Median, and Mode in a moderately skewed distribution?

   Mode = 3 Median − 2 Mean.

5. What are the four main measures of dispersion?

   Range, Quartile Deviation (semi-interquartile range), Mean Deviation, and Standard Deviation (and its square, Variance).

6. Define range as a measure of dispersion.

   Range = Largest value (L) − Smallest value (S); the simplest measure of spread.

7. State the formula for population variance (σ²) of ungrouped data.

   σ² = Σ(x − μ)² / N, the mean of the squared deviations from the population mean.

8. What is the standard deviation and how does it relate to variance?

   Standard deviation (σ) is the positive square root of the variance; it measures spread in the original units of the data.

9. Define the coefficient of variation (CV) and its use.

   CV = (σ / x̄) × 100. It is a unit-free relative measure used to compare the variability of two or more datasets with different units or means.

10. What is quartile deviation (semi-interquartile range)?

    QD = (Q3 − Q1) / 2, half the difference between the third and first quartiles.

11. Which measure of central tendency is most affected by extreme values (outliers)?

    The arithmetic mean, because it uses every value in its calculation.

12. Which measure of central tendency is best for open-ended class intervals or qualitative data?

    The median (for open-ended classes) and the mode (for qualitative/nominal data).

13. What does skewness measure?

    The degree and direction of asymmetry of a distribution about its mean (positive/right skew, negative/left skew, or symmetric).

14. What does kurtosis measure?

    The degree of peakedness or flatness of a distribution relative to a normal curve (leptokurtic, mesokurtic, or platykurtic).

15. List the conditions required for a Binomial distribution.

    Fixed number of trials (n); each trial has only two outcomes (success/failure); constant probability of success (p) across trials; trials are independent.

16. State the probability mass function of the Binomial distribution.

    P(X = x) = nCx · p^x · q^(n−x), where q = 1 − p, for x = 0, 1, 2, …, n.

17. What are the mean and variance of a Binomial distribution?

    Mean = np; Variance = npq (where q = 1 − p). Note variance is always less than the mean.

18. When does the Binomial distribution become symmetric?

    When p = 0.5 (q = 0.5); it is positively skewed when p < 0.5 and negatively skewed when p > 0.5.

19. State the probability mass function of the Poisson distribution.

    P(X = x) = (e^(−λ) · λ^x) / x!, for x = 0, 1, 2, …, where λ is the mean number of occurrences.

20. What are the mean and variance of a Poisson distribution?

    Both the mean and the variance equal λ (mean = variance = λ).

21. Under what conditions does the Binomial distribution approximate a Poisson distribution?

    When n is large, p is very small, and np = λ remains finite (rare events with many trials).

22. Give two typical examples of phenomena modelled by the Poisson distribution.

    Number of calls arriving at a switchboard per hour; number of defects per unit; number of accidents per day (rare, independent events over a fixed interval).

23. What type of variable does the Poisson distribution describe — discrete or continuous?

    Discrete; it gives the probability of a count of events (0, 1, 2, …) in a fixed interval of time or space.

See more Unit - VIII flashcards →

Planning Unit - VIII for UGC NET Management

Unit - VIII (UGC NET Management) FAQ