Statistical Appraisal of Timber with an Application to the Chequamegon National Forest

Abstract A statistical method of appraising timber is presented. It consists of predicting the high bid of a particular timber offering under competitive conditions and adjusting this value to reflect uncertainty and the goals of the selling agency. Using data from the Chequamegon National Forest in northern Wisconsin, it was found that a simple linear model using 14 variables explained 93% of the variance in high bid for competitive sales from 1976 to 1980. This model predicted well post-sample high bids for 1981 and 1982. Based on this model, three possible definitions of appraised value were investigated: (1) predicted high bid, (2) predicted high bid minus one standard error, and (3) predicted high bid minus two standard errors. The consequences of each definition on timber sales, had it been applied in 1981 and 1982, were examined. Definitions (1) and (2) would have increased receipts by 28 and 5%, while decreasing the volume sold by only 5 and 3.6 %, respectively. Definition (3) would have led to the sale of approximately the same volume, but a decrease in receipts of 26%. North. J. Appl. For. 1:72-76, Dec. 1984.

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Multi-Center Calibration of the Second Reference Material for Thromboplastin, Rabbit, Plain, Coded CRM 149R

Thrombosis and Haemostasis ◽

10.1055/s-0038-1648132 ◽

1991 ◽

Vol 65 (03) ◽

pp. 263-267 ◽

Cited By ~ 3

Author(s):

A M H P van den Besselaar ◽

R M Bertina

Keyword(s):

Reference Material ◽

Standard Error ◽

Standard Errors ◽

Sensitivity Index ◽

Long Term Stability ◽

Significant Bias ◽

The Mean ◽

The Relationship ◽

International Sensitivity Index

SummaryIn a collaborative trial of eleven laboratories which was performed mainly within the framework of the European Community Bureau of Reference (BCR), a second reference material for thromboplastin, rabbit, plain, was calibrated against its predecessor RBT/79. This second reference material (coded CRM 149R) has a mean International Sensitivity Index (ISI) of 1.343 with a standard error of the mean of 0.035. The standard error of the ISI was determined by combination of the standard errors of the ISI of RBT/79 and the slope of the calibration line in this trial.The BCR reference material for thromboplastin, human, plain (coded BCT/099) was also included in this trial for assessment of the long-term stability of the relationship with RBT/79. The results indicated that this relationship has not changed over a period of 8 years. The interlaboratory variation of the slope of the relationship between CRM 149R and RBT/79 was significantly lower than the variation of the slope of the relationship between BCT/099 and RBT/79. In addition to the manual technique, a semi-automatic coagulometer according to Schnitger & Gross was used to determine prothrombin times with CRM 149R. The mean ISI of CRM 149R was not affected by replacement of the manual technique by this particular coagulometer.Two lyophilized plasmas were included in this trial. The mean slope of relationship between RBT/79 and CRM 149R based on the two lyophilized plasmas was the same as the corresponding slope based on fresh plasmas. Tlowever, the mean slope of relationship between RBT/79 and BCT/099 based on the two lyophilized plasmas was 4.9% higher than the mean slope based on fresh plasmas. Thus, the use of these lyophilized plasmas induced a small but significant bias in the slope of relationship between these thromboplastins of different species.

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Investor Returns on National Forest Timber Sales

Financial Analysts Journal ◽

10.2469/faj.v41.n3.69 ◽

1985 ◽

Vol 41 (3) ◽

pp. 69-75

Author(s):

Robert A. Olsen

Keyword(s):

National Forest ◽

Timber Sales ◽

Investor Returns

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Statistical method of steepest improvement of response

Industrial laboratory Diagnostics of materials ◽

10.26896/1028-6861-2018-84-11-74-87 ◽

2018 ◽

Vol 84 (11) ◽

pp. 74-87

Author(s):

V. B. Bokov

Keyword(s):

Statistical Method ◽

Linear Model ◽

Response Prediction ◽

Initial Experiment ◽

First Order ◽

Prediction Response ◽

Conditional Extremum ◽

Order Polynomial ◽

Limiting Values

A new statistical method for response steepest improvement is proposed. This method is based on an initial experiment performed on two-level factorial design and first-order statistical linear model with coded numerical factors and response variables. The factors for the runs of response steepest improvement are estimated from the data of initial experiment and determination of the conditional extremum. Confidence intervals are determined for those factors. The first-order polynomial response function fitted to the data of the initial experiment makes it possible to predict the response of the runs for response steepest improvement. The linear model of the response prediction, as well as the results of the estimation of the parameters of the linear model for the initial experiment and factors for the experiments of the steepest improvement of the response, are used when finding prediction response intervals in these experiments. Kknowledge of the prediction response intervals in the runs of steepest improvement of the response makes it possible to detect the results beyond their limits and to find the limiting values of the factors for which further runs of response steepest improvement become ineffective and a new initial experiment must be carried out.

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A note on growth curves of rabbit lines selected on growth rate or litter size

Animal Science ◽

10.1017/s000335610000698x ◽

1993 ◽

Vol 57 (2) ◽

pp. 332-334 ◽

Cited By ~ 13

Author(s):

A. Blasco ◽

E. Gómez

Keyword(s):

Growth Rate ◽

Litter Size ◽

Growth Curves ◽

Live Weight ◽

Maximum Growth ◽

Maximum Growth Rate ◽

Standard Errors ◽

Adult Weight ◽

Weight Growth ◽

Using Data

Two synthetic lines of rabbits were used in the experiment. Line V, selected on litter size, and line R, selected on growth rate. Ninety-six animals were randomly collected from 48 litters, taking a male and a female each time. Richards and Gompertz growth curves were fitted. Sexual dimorphism appeared in the line V but not in the R. Values for b and k were similar in all curves. Maximum growth rate took place in weeks 7 to 8. A break due to weaning could be observed in weeks 4 to 5. Although there is a remarkable similarity of the values of all the parameters using data from the first 20 weeks only, the higher standard errors on adult weight would make 30 weeks the preferable time to take data for live-weight growth curves.

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On the Smoothing of Observed Data

Mathematical Proceedings of the Cambridge Philosophical Society ◽

10.1017/s0305004100077586 ◽

1937 ◽

Vol 33 (4) ◽

pp. 444-450 ◽

Cited By ~ 4

Author(s):

Harold Jeffreys

Keyword(s):

Standard Error ◽

Random Error ◽

Simple Function ◽

Standard Errors ◽

Random Errors ◽

Previous Theory ◽

Know How ◽

True Value ◽

Actual Error ◽

Range Of Variation

1. It often happens that we have a series of observed data for different values of the argument and with known standard errors, and wish to remove the random errors as far as possible before interpolation. In many cases previous considerations suggest a form for the true value of the function; then the best method is to determine the adjustable parameters in this function by least squares. If the number required is not initially known, as for a polynomial where we do not know how many terms to retain, the number can be determined by finding out at what stage the introduction of a new parameter is not supported by the observations*. In many other cases, again, existing theory does not suggest a form for the solution, but the observations themselves suggest one when the departures from some simple function are found to be much less than the whole range of variation and to be consistent with the standard errors. The same method can then be used. There are, however, further cases where no simple function is suggested either by previous theory or by the data themselves. Even in these the presence of errors in the data is expected. If ε is the actual error of any observed value and σ the standard error, the expectation of Σε2/σ2 is equal to the number of observed values. Part, at least, of any irregularity in the data, such as is revealed by the divided differences, can therefore be attributed to random error, and we are entitled to try to reduce it.

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Area-Wide Analysis of Hardwood Defoliator Effects on Tree Conditions in the Allegheny Plateau

Northern Journal of Applied Forestry ◽

10.1093/njaf/21.1.31 ◽

2004 ◽

Vol 21 (1) ◽

pp. 31-39 ◽

Cited By ~ 10

Author(s):

Randall S. Morin ◽

Andrew M. Liebhold ◽

Kurt W. Gottschalk

Keyword(s):

Tree Mortality ◽

Forest Health ◽

National Forest ◽

Forest Land ◽

Land Area ◽

Usda Forest Service ◽

Forest Inventory And Analysis ◽

Allegheny Plateau ◽

Using Data ◽

Crown Dieback

Abstract The effects of defoliation caused by three foliage feeding insects, the gypsy moth (Lymantria dispar), the cherry scallopshell moth (Hydria prunivorata), and the elm spanworm (Ennomos subsignarius), on tree mortality and crown conditions were evaluated using data collected from 1984 to 1999 in the Allegheny National Forest located in northwestern Pennsylvania. While previous studies have focused on the effects of defoliation on trees in individual stands, this study differed in that it used exhaustive maps of defoliation and an areawide network of plots to assess these effects. A geographic information system was used to map the coincidence of USDA Forest Service Forest Inventory and Analysis and Forest Health Monitoring plot locations with defoliation polygons derived from aerial surveys to calculate cumulative years of defoliation for each pest. Over 85% of the Allegheny National Forest land area was defoliated at least once during the 16-year period from 1984 to 1999. Frequency of defoliation by specific defoliator species was closely associated with the dominance of their primary hosts in stands. Frequency of defoliation was often associated with crown dieback and mortality, but these relationships were not detectable in all species. These results suggest that when impacts are averaged over large areas (such as in this study) effects of defoliation are likely to be considerably less severe than when measured in selected stands (as is the approach taken in most previous impact studies).

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On Describing an Incidence Sample

Journal of Educational Statistics ◽

10.3102/10769986007004311 ◽

1982 ◽

Vol 7 (4) ◽

pp. 311-331 ◽

Cited By ~ 1

Author(s):

Gwyneth M. Boodoo

Keyword(s):

Variance Components ◽

Generalizability Theory ◽

Standard Errors ◽

Eighth Grade ◽

Eighth Grade Students ◽

Competency Based ◽

Mean And Variance ◽

The Mean ◽

Using Data ◽

Variance Estimates

Parameters used to describe an incidence sample are estimated using the theory of generalized symmetric means and generalizability theory. The former is used to compute estimates of the mean and variance components in an ANOVA framework, while the latter is used in obtaining generalizability coefficients. Standard errors of the variance estimates are obtained. The procedure is illustrated using data from two competency-based tests given to eighth grade students in mathematics and reading.

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Variation of Quiet Sun Radiation during Solar Cycles 23 and 24

Proceedings of the International Astronomical Union ◽

10.1017/s1743921317007992 ◽

2017 ◽

Vol 13 (S335) ◽

pp. 11-13

Author(s):

Mahender Aroori ◽

G. Yellaiah ◽

K. Chenna Reddy

Keyword(s):

Radio Emission ◽

Brightness Temperature ◽

Statistical Method ◽

Solar Radio ◽

Solar Radio Emission ◽

Solar Cycles ◽

Quiet Sun ◽

Data Centre ◽

National Geophysical Data ◽

Using Data

AbstractRadio observations play a very important role in understanding the structure of the solar atmosphere. In this paper the quiet sun component of the solar radio emission has been investigated using data obtained from the Solar Indices Bulletin, National Geophysical Data Centre. By statistical method, the quiet sun component is estimated for 84 successive basic periods containing three solar rotations each using data obtained at different frequencies. From the quiet sun component we estimate the brightness temperature in each observing frequency.

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