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Question

The length of a cylinder is measured 10 times, yielding 10 distinct values. For this set of values, consider the following statements.
A. Five of these values will lie above the mean and five below it
B. Five of these values will lie above the median and five below it
C. At least one value will lie above the mean
D. At least one value will lie at the median
Which of the statements are necessarily correct?

The correct answer is
B and C

To determine which statements about the 10 distinct cylinder length measurements are necessarily correct, we need to analyze the properties of the mean and median.

Mean Analysis for Cylinder Measurements

Statement A claims five values lie above the mean and five below. The mean ($\mu$) is calculated as $\mu = \frac{\sum x_i}{n}$. For a set of 10 distinct values, it is not guaranteed that exactly half will be above and half below the mean. The mean's position depends on the distribution of all values.

Example: Consider the distinct values {1, 2, 3, 4, 5, 6, 7, 8, 9, 100}. The mean is $\mu = \frac{1+2+...+9+100}{10} = \frac{45+100}{10} = 14.5$. Only one value (100) is above the mean; nine are below it. Thus, Statement A is not necessarily correct.

Median Analysis for Cylinder Measurements

Statement B claims five values lie above the median and five below. For 10 distinct measurements sorted in ascending order as $x_1, x_2, ..., x_{10}$, the median ($M$) is the average of the 5th and 6th values: $M = \frac{x_5 + x_6}{2}$.

  • Since the values are sorted and distinct ($x_1 < x_2 < ... < x_5 < x_6 < ... < x_{10}$), we know $x_5 < M$ because $M = \frac{x_5 + x_6}{2}$ and $x_5 < x_6$.
  • Similarly, $M < x_6$ because $M = \frac{x_5 + x_6}{2}$ and $x_5 < x_6$.
  • The first five values ($x_1$ to $x_5$) are all less than $x_5$, and therefore, they are all below the median $M$.
  • The last five values ($x_6$ to $x_{10}$) are all greater than $x_6$, and therefore, they are all above the median $M$.

Thus, Statement B is necessarily correct.

Mean Existence for Cylinder Measurements

Statement C claims at least one value will lie above the mean. The definition of the mean is $\sum_{i=1}^{10} x_i = 10\mu$.

Assume, for contradiction, that all 10 values are less than or equal to the mean ($\forall i, x_i \le \mu$). Since the values are distinct, they cannot all be equal to the mean. Therefore, at least one value must be strictly less than the mean. This would imply that the sum $\sum x_i$ is strictly less than $10\mu$, contradicting the definition of the mean.

Hence, it must be true that at least one value is strictly greater than the mean ($\exists i$ such that $x_i > \mu$). Statement C is necessarily correct.

Median Existence for Cylinder Measurements

Statement D claims at least one value will lie at the median. As established for Statement B, the median $M = \frac{x_5 + x_6}{2}$ for 10 distinct sorted values.

Since $x_5$ and $x_6$ are distinct numbers, their average $M$ will typically lie strictly between them ($x_5 < M < x_6$). It is not guaranteed that the calculated median $M$ will be equal to any of the original data points.

Example: If $x_5 = 5$ and $x_6 = 6$, the median is $M = (5+6)/2 = 5.5$. No original data point is 5.5. Thus, Statement D is not necessarily correct.

Conclusion: Correct Statements for Cylinder Data

Based on the analysis, statements B and C are necessarily correct. This corresponds to Option 1.

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Important Questions from Elementary Statistics (Notes)

  1. Let $X_1, X_2, X_3$ be a random sample of size 3 from an absolutely continuous distribution that is symmetric about 0. For $i=1,2,3$, let $R_i$ denote the rank of $|X_i|$ among $|X_1|, |X_2|$ and $|X_3|$. 

    If $T^+ = \sum_{i=1, X_i>0}^3 R_i$

     is the Willcoxon signed-rank statistic, then which of the following statements are true?, 

  2. What is the geometric mean of 2, 4 and 8?
  3. In correlation analysis, the two variables

    1. Are treated with distinction.
    2. Are treated differently based on individual characteristics.
    3. Are treated symmetrically.
    4. Are regressed.
  4. In statistics, standard error measures the

    1. Specification error of the model.
    2. Autocorrelation in the regression model.
    3. Correlation between dependent and independent variables.
    4. Precision of an estimate.
  5. Linear regression model is

    1. linear in explanatory variables but may not be linear in parameters
    2. non-linear in parameters and must be linear in variables
    3. linear in parameters and must be linear in variables
    4. linear in parameters and may be linear in variables
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