Question 1188763
Here's how to analyze Neeve's data:

**a. Table of Ranks:**

First, we need to rank the ages and the number of TV hours separately.  Rank 1 goes to the smallest value, and the highest rank goes to the largest value. If there are ties, we take the average of the ranks that would have been assigned.

| Age | Rank (Age) | TV Hours | Rank (TV) |
|---|---|---|---|
| 8 | 1 | 20 | 6.5 |
| 42 | 8.5 | 15 | 4 |
| 17 | 2 | 30 | 10 |
| 81 | 12 | 2 | 1 |
| 45 | 10 | 25 | 8 |
| 14 | 3 | 28 | 9 |
| 39 | 6 | 19 | 5 |
| 42 | 8.5 | 14 | 3 |
| 31 | 5 | 16 | 4.5 |
| 40 | 7 | 21 | 7 |
| 28 | 4 | 26 | 8.5 |
| 24 | 3 | 20 | 6.5 |

**b. Spearman's Rank Correlation:**

Spearman's rank correlation coefficient (rs) is calculated using the formula:

rs = 1 - (6 * Σd²) / (n * (n² - 1))

Where 'd' is the difference between the ranks for each pair, and 'n' is the number of pairs (12 in this case).

1.  Calculate 'd' for each pair:  (Rank Age - Rank TV)
2.  Calculate d² for each pair.
3.  Sum the d² values (Σd²).  You should get approximately 111.5
4.  Apply the formula:

rs = 1 - (6 * 111.5) / (12 * (12² - 1))
rs = 1 - (669) / (12 * 143)
rs = 1 - (669) / (1716)
rs ≈ 1 - 0.39
rs ≈ 0.61

**c. Neeve's Conclusion:**

Neeve concludes that age *affects* how much TV a person watches. This implies a causal relationship.  However, correlation does *not* equal causation.  While there's a moderately positive correlation (rs ≈ 0.61), this only suggests that as age *tends* to increase, the number of TV hours *tends* to decrease. It does *not* prove that age *causes* a decrease in TV watching.

**Better Conclusions:**

*   There is a moderate positive correlation between age and the number of hours spent watching television.
*   Older individuals in this sample tend to watch less television than younger individuals.
*   Other factors besides age could be influencing television viewing habits (e.g., lifestyle, work schedules, access to streaming services, etc.).  Further investigation is needed to explore these potential influences.  A larger sample size would also be beneficial.

Therefore, Neeve's conclusion is too strong.  The data only supports a correlation, not a causal link.