Cardiologists use the short-range scaling exponent α1, which measures the randomness of heart rate patterns, as a tool to assess risk of heart attack. The article “Applying Fractal Analysis to Short Sets of Heart Rate Variability Data” compared values of α1 computed from long series of measurements (approximately 40,000 heartbeats) with those estimated from the first 300 beats to determine how well the long-term measurement (y) could be predicted the short-term one (x). Following are the data (obtained by digitizing a graph). Short Long 0.54 0.55 1.02 0.79 1.4 0.81 0.88 0.9 1.68 1.05 1.16 1.05 0.82 1.05 0.93 1.07 1.26 1.1 1.18 1.19 0.81 1.19 0.81 1.2 1.28 1.23 1.18 1.23 0.71 1.24 Note: This problem has a reduced data set for ease of performing the calculations required. This differs from the data set given for this problem in the text. Find a 95% prediction interval for the long-term measurement for a particular individual whose short term measurement is 1.2. Round the answers to three decimal places. The 95% prediction interval is ( , ).
Cardiologists use the short-
Short | Long |
0.54 | 0.55 |
1.02 | 0.79 |
1.4 | 0.81 |
0.88 | 0.9 |
1.68 | 1.05 |
1.16 | 1.05 |
0.82 | 1.05 |
0.93 | 1.07 |
1.26 | 1.1 |
1.18 | 1.19 |
0.81 | 1.19 |
0.81 | 1.2 |
1.28 | 1.23 |
1.18 | 1.23 |
0.71 | 1.24 |
Note: This problem has a reduced data set for ease of performing the calculations required. This differs from the data set given for this problem in the text.
Find a 95% prediction interval for the long-term measurement for a particular individual whose short term measurement is 1.2. Round the answers to three decimal places.
The 95% prediction interval is ( , ).
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