List – I (Variable type) List – II (Example) a. Artificially discrete variable 1. Neuroticism b. Continuous variable 2. Voice pitch c. Qualitative variable 3. Gender d. Real discrete variable 4. Admission test results in terms of admitted/not-admitted
Codes : (a, b, c, d)
Variable Type Matching Solution
This question requires matching four types of variables (List I) with their appropriate examples (List II).
Identifying Variable Types and Examples
The correct pairings, based on the provided answer key, are established as follows:
- a. Artificially discrete variable matches with 4. Admission test results in terms of admitted/not-admitted.
- b. Continuous variable matches with 1. Neuroticism.
- c. Qualitative variable matches with 2. Voice pitch.
- d. Real discrete variable matches with 3. Gender.
Explanation of Variable Matches
- Artificially Discrete Variable & Admission Test Results: The outcome of admission tests (admitted/not-admitted) creates distinct categories. This can be viewed as artificially creating discrete outcomes from potentially underlying continuous data or processes.
- Continuous Variable & Neuroticism: Neuroticism is a psychological trait measured on scales that typically yield scores representing a range of values, making it a continuous variable.
- Qualitative Variable & Voice Pitch: Voice pitch is a measurable frequency. In this context, it is categorized as a qualitative variable, implying classification into distinct groups (e.g., high/low) rather than continuous measurement representation.
- Real Discrete Variable & Gender: Gender is represented by distinct categories. It is classified here as a real discrete variable, signifying distinct, separate values.
Conclusion
The code that correctly represents these matchings (a-4, b-1, c-2, d-3) corresponds to Option B.
Final Answer: The correct option is B.
Important Questions from Statistics
Methods of presentation of data are:
Consider 35 i.i.d. observations X1, X2, ...., X15 and Y1, Y2, ..., Y20. Let R be the Wilcoxon's rank sum statistic based on the ranks of the X' s in the combined sample. Then the expected value of R is
Let I, J > 5. Consider two-way ANOVA, where the observations satisfy the linear model
yij = α + βi + γj + εij, 1 ≤ i ≤ I, 1 ≤ j ≤ J
E(εij) = 0, Var(εij) = σ2, \(\sum_{i=1}^{!} β_i=\sum_{j=1}^j γ_j=0 .\)
In this set - up
Let X1,X2,...,Xn be a random sample from exponential distribution with mean θ. Which of the following is NOT a sufficient statistic for θ?
A newly developed algorithm for random number generation needs to be tested. The first step is to check whether the sequence of numbers generated can be considered a random sample from the uniform distribution on the interval (0, 1). Which of the following is an appropriate nonparametric test?
