**Core Concept**
The standard error of estimate (SEE) is a statistical measure used to quantify the variability of predictions made by a regression model. In the context of this question, it represents the standard deviation of the differences between observed and predicted values of hemoglobin levels in a population of women.

**Why the Correct Answer is Right**
To calculate the standard error of estimate, we use the formula: SEE = s / sqrt(n), where 's' is the standard deviation and 'n' is the sample size. In this case, the standard deviation is given as 1 gm%, and the sample size is 100 women. Plugging these values into the formula, we get SEE = 1 / sqrt(100) = 1 / 10 = 0.1 gm%. This means that if we were to repeat the measurement of hemoglobin levels in a new sample of 100 women, we would expect the average difference between the observed and predicted values to be approximately 0.1 gm%.

**Why Each Wrong Option is Incorrect**
**Option A:** This option is not provided.
**Option B:** This option is not provided.
**Option C:** This option is not provided.
**Option D:** This option is not provided.

**Clinical Pearl / High-Yield Fact**
A key concept to remember is that the standard error of estimate is inversely proportional to the square root of the sample size. This means that as the sample size increases, the standard error of estimate decreases, and vice versa.

**Correct Answer: D. 0.1 gm%**