CONFIDENCE LIMITS ON PREDICTED VALUES

Transcription

1 TN-I-3 l/81 CONFIDENCE LIMIT ON PREDICTED VALUE PURPOE: To determine the probable accuracy of a predicted value. GENERAL : Values used in engineering design are often predicted using data from laboratory experiments or field measurements. A common means used is to statistically fit a regression line to plotted data. This regres- sion line will give a predicted average value, i.e., the real value has an equal probability of being above or below the predicted value. It is desirable to have a confidence limit on the real value. This confidence limit around the predicted value will provide a range of values within -_ which there is a given probability of finding the real value. Taking the independent variable as x and the dependent variable (to be predicted) as y, a data point is given as (xi, Yi)' For a given value of the independent variable, xk, the predicted value of the depend- ent variable is 9, and the real value is yk. Regression lines giving a formula relating y to x, such as: for linear and mith, and Myers, which have y=a+bx (1) regression, can be obtained by established means (see Draper 1966; Mendenhall, 1966; Daniel and Wood, 1971; or Walpole 1978). Various manufacturers produce programable calculators standard programs for linear regression. Care must be exercised in applying regression analysis and confi- dence limits to particular sets of data. The data sets should be plotted to determine if the relationships appear to be linear or currilinear. Also, there must be a clear physical relationship between the dependent and the independent variables. ome of the references discuss the underlying assumptions and the precautions which should be observed in applying regression analysis. U.. Amy corps of Engi~~eer~, Coastal Engineering Research Center, Kingman Building, Fort Rehoir, VA 22060

2 Report Documentation Page Form Approved OMB No Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. end comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters ervices, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, uite 1204, Arlington VA Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. 1. REPORT DATE JAN REPORT TYPE 3. DATE COVERED to TITLE AND UBTITLE Confidence Limits On Predicted Values 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR() 5d. PROJECT NUMBER 5e. TAK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME() AND ADDRE(E) U.. Amy Corps of Engineer,Coastal Engineering Research Center,Kingman Bldg,Fort Belvoir,VA, PERFORMING ORGANIZATION REPORT NUMBER 9. PONORING/MONITORING AGENCY NAME() AND ADDRE(E) 10. PONOR/MONITOR ACRONYM() 12. DITRIBUTION/AVAILABILITY TATEMENT Approved for public release; distribution unlimited 13. UPPLEMENTARY NOTE 11. PONOR/MONITOR REPORT NUMBER() 14. ABTRACT Values used in engineering design are often predicted using data from laboratory experiments or field measurements. A common means used is to statistically fit a regression line to plotted data. This regression line will give a predicted average value, i.e., the real value has an equal probability of being above or below the predicted value. It is desirable to have a confidence limit on the real value. This confidence limit around the predicted value will provide a range of values within which there is a given probability of finding the real value. 15. UBJECT TERM 16. ECURITY CLAIFICATION OF: 17. LIMITATION OF ABTRACT a. REPORT unclassified b. ABTRACT unclassified c. THI PAGE unclassified ame as Report (AR) 18. NUMBER OF PAGE 6 19a. NAME OF REPONIBLE PERON tandard Form 298 (Rev. 8-98) Prescribed by ANI td Z39-18

3 Where linear regression is used for predictions, the confidence limits can be attained as follows: For a given probability, PI that y 5 or probability, P2r /_ \ /. \ that j? - est Y LyI. Y+et, t is obtained from the table of t values. ) L 8 ) Table 1 has been developed from the t-distribution (see Draper and mith, 1966) and may be used directly to obtain probabilities for the example problem to be shown. (Note that Table 1 is not a t-distribution table since one enters the table directly with the number of data points rather than with the number of data points less two. This assumes that a t-distribution with two degrees of freedom is appropriate for the solution). The value e s is the standard error and for a given predicted value y is given as: e (2) where 9, is the (xi YJ 9 n is predicted value of y corresponding to the data point -_ the number of data points, Z the mean value of xi, and xk is the value of x for which we are predicting yk. For a given probability, the confidence limits for the various values of yk will form curves above and below the regression line defining the values of 9,. GIVEN : At a hypothetical coastal location, there exists a 150-year record of tsunami flood levels. A total of seven tsunamis occurred having measured flood levels above mean sea level of 4 feet, 5 feet, 5.5 feet, 7 feet, 8 feet, 12 feet, and 16 feet. All flood levels are for a location 200 feet shoreward of the coastline. FIND: A flood level that has a 99 percent probability of not being ex- ceeded by the loo-year tsunami.

4 TN-I-3 l/81 Table 1. t Values number Probability Pl that y eat of data points, Probability P2 that 7 - est - < y - < 9 + est.n !? 0.IL. " _ , , i ' Ok (IO

5 OLUTION: The probability of occurrence of each flood level, h, is given by P(h) = m/(n + 1) where m is the rank and n is the period of record in years. This gives: P (16) = l/( ) = P(12) = 2/( ) = P(8) = 3/( ) = P(7) = 4/( ) = P(5.5)= 5/( ) = P(5) = 6/( ) = P(4) = 7/( ) = Flood levels are plotted against probability in the Figure. Using the equation for tsunami flooding given by Houston, et a1.,(1977) and Camfield (1980) that h = -B - A logi P(h), (3). linear regression from standard methods (references 2,3,5) gives._ h= log,, P(h). (4) h, feet 0.01 P(h) Figure 1. Probability of occurrence of tsunami flood levels. Note: This figure is only applicable to the given set of data and should not be used for design purposes.

6 TN-I-3 l/81 For P(h) = 0.01 (the loo-year tsunami), from i;= loglo (0.01) = i;= = feet equation (4) (-2) which is the predicted flood level from linear regression. To calculate e note that the independent variable "x" is loglo P(h) where P(h) has been previously calculated, and the dependent variable "y" is h. r. C(hi - gi)' 92 ( loglo PO-d, - 1 log,, P(h))2 (5) e = n n- 2 c(log,, P(hji - loglo P(h))2 L n = 7 (the number of data points) The term "(hi - si);/(n - 2) ' is obtained directly from existing pro- II 1 grams for linear regression available on programable calculators (indi- vidual users should refer to manufacturers handbooks for the calculators that they have available). In this case, h, = 16, h, = 12, h, = 8, h 4 = 7, h, = 5.5, h, = 5, h, = 4 then. [ I Yhi _ n- 2 (Note: working this problem without a programable calculator is a long, tedious process which is beyond the scope of this technical note). Equation (5) for the standard error now becomes e = logr, log,, ( P(h>)2 C(log,, P(h)i - loglo P(h))2 Using a programable calculator, and inserting values of h and loglo P(h), the mean value of log,, P(h) is log, o P(h) =

7 and the summation is given as P(h) - logi, P(h) i = The standard error is now es = [+ + (~:~~~~)2]y e = To solve for h, for the loo-year tsunami from the table' for n = 7, and P, = 0.99, t = h (3.365) = h ft There is a 99 percent probability that the loo-year tsunami will not exceed. a flood level of 14.4 feet. To obtain a confidence limit curve (or the 99 percent confidence limit line in Figure l), one repeats the computation for a number of frequency intervals. *************************************** REFERENCE: 1. CAMFIELD, Frederick E., "Tsunami Engineering," pecial Report No. 6, U.. Army Coastal Engineering Research Center, Fort Belvoir, VA., February DANIEL, Cuthbert and WOOD, Fred, Fitting Equations to Data, John Wiley and ons, Inc., New York, New York, DRAPER, N.R., and MITH, H., AppZied Regression Analysis, John Wiley and ons, Inc., New York, New York, HOUTON, J.R., CARVER, R.D., and MARKLE, D.G., "Tsunami-Wave Elevation Frequency of Occurrence for the Hawaiian Islands," Technical Report H-77-16, U.. Army Engineer Waterways Experiment tation, Vicksburg, M, August MENDENHALL, William, Introduction to tatistics, Wadsworth Publishing Co., Inc., WALPOLE, R.E., and MYER, R.H., ProbabiZity and tatistics for Erz&neers and cientists, 2nd Ed., MacMillan Publishing CO., Inc., New York, New York,