Q.63 Which of the following assertions are CORRECT? P: Adding 7 to each entry in a list adds 7 to the mean of the list Q: Adding 7 to each entry in a list adds 7 to the standard deviation of the list R: Doubling each entry in a list doubles the mean of the list S: Doubling each entry in a list leaves the standard deviation of the list unchanged (A) P, Q (B) Q, R (C) P, R (D) R, S

Q.63 Which of the following assertions are CORRECT?
P: Adding 7 to each entry in a list adds 7 to the mean of the list
Q: Adding 7 to each entry in a list adds 7 to the standard deviation of the list
R: Doubling each entry in a list doubles the mean of the list
S: Doubling each entry in a list leaves the standard deviation of the list unchanged
(A) P, Q (B) Q, R (C) P, R (D) R, S

Adding a constant to every data point shifts the mean but not the standard deviation,
while scaling doubles the mean and also scales the standard deviation.
The correct assertions are P and R.

✅ Correct Answer: (C) P, R

These two assertions hold true based on fundamental statistical properties of mean and standard deviation transformations.

🔍 Detailed Assertion Analysis

P: Adding 7 Adds 7 to Mean ✅ CORRECT

Original list: x₁, x₂, ..., xₙ with mean x̄ = (1/n)Σxᵢ
New list: xᵢ + 7, new mean x̄' = (1/n)Σ(xᵢ + 7) = x̄ + 7

The mean shifts exactly by the added constant.

Q: Adding 7 Adds 7 to SD ❌ INCORRECT

SD: σ = √[(1/n)Σ(xᵢ - x̄)²]
New deviations: (xᵢ + 7) - (x̄ + 7) = xᵢ - x̄, so σ' = σ (unchanged)

Standard deviation measures spread around the mean, which remains identical.

R: Doubling Doubles the Mean ✅ CORRECT

New list: 2xᵢ, new mean x̄' = (1/n)Σ(2xᵢ) = 2x̄

The mean scales linearly with the scaling factor.

S: Doubling Leaves SD Unchanged ❌ INCORRECT

New SD: σ' = √[(1/n)Σ(2xᵢ - 2x̄)²] = √[4 × (1/n)Σ(xᵢ - x̄)²] = 2σ

Standard deviation also doubles because deviations are scaled by 2.

🎯 Why P and R Matter in Practice

These properties underpin data standardization in biotechnology and statistics. For instance, in microbial growth kinetics, shifting measurements (like adding a baseline OD value) adjusts means predictably without altering variability, aiding precise model fitting in fermentation processes and enzyme kinetics studies.

 

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