Table 11
Combined Early Elimination and Early Qualification Predictions

Early Early
Subset Elimination Qualification Elimination Qualification © Cumulative
Size Cutoff (<) Cutoff (>) % % %
2 114 166 14.7 10.5 25.2
3 172 248 3.5 6.4 35.0
4 234 326 2.3 2.3 39.6
5 299 401 6 2.3 42.5
6 363 477 12 2.9 46.6
7 435 545 1.2 12 49.9
8 Su 609 18 76 58.4
9 587° 673 12 5.8 65.4

* Mathematically eliminated with a score < 600.
Implementing the Strategy

Figure 1 shows how the proposed target engagement reduction strategy would be
implemented in an armor battalion using the cutoff scores shown in the second and third
columns of Table 11. In general, once the unit commander has randomly selected the ten
TTVII engagements (from the set of 12 possible) to be fired, crew gunnery proficiency
would be evaluated after the firing of each engagement, irrespective of firing order.

All crews would begin firing with the first two engagements. Those not scoring at
least 114 would be pulled from the range and given TADSS-based remedial training,
perhaps on the COFT or Abrams Full-Crew Interactive Simulation Trainer (AFIST).
Once their TADSS-based performance suggests a reasonable probability of successful
live-fire TTVIII qualification (See Hagman & Smith, 1996, for the description of a
COFT-based tool for predicting such), they would be given one rerun attempt, starting
from the beginning with the first two engagements.

First-run crews scoring 166 or higher after firing the first two engagements would be
awarded early qualification (i.e., Q1e); those scoring from 114 to 165 would go on to the
third engagement. Crews not scoring at least 172 after three engagements would undergo
remediation before beginning their rerun from the beginning. Rerun crews would be
evaluated as if they were firing their first run, except that predictions would now apply to
Q2 rather than Q1. Those predicted to need remediation as a result of low scores on their
rerun would receive an unqualified rating. First-run crews scoring 248 or higher
after three engagements would be awarded early qualification; those scoring from 172 to
247 would go on to the fourth engagement, and so on.

12



them, multiply each score by its respective regression coefficient shown in Table 4 (e.g.,
1.01 for Engagement A1, 1.18 for Engagement A2S), and then add the prediction
equation constant (i.e., 162.74) to arrive at the predicted total score for each crew. This
procedure would be time-consuming and subject to error when performed on the range.

Table 4
Prediction Equations for Subset Sizes I to 9
Subset .
Size Prediction Equation
1 Y' = 653.21 + 1.03 (BI)
2 Y' = 573.75 + 1.11(B1) + 1.28(A2S)
3 Y'= 500.32 + 1.12(B1) + 1.27(A2S) + 1.07(A3)
4 Y'=423.87+ 1.07(B1) + 1.29(A2S) + 1.01(A3) + 1.11(AS/ASA)
5 Y' = 372.19 + 1.00(B1) + 1.18(A2S) + 1.04(A3) + 1.10(AS/ASA) + .97(B2/B2A)
6 —-Y'=298.06 + 1.05(B1) + 1.27(A2S) + 1.08(A3) + 1.07(A5/A5A) + .97(B2/B2A) + .87(Al)
7 ¥'=206.02 + .95(B1) + 1.22(A2S) + 1.05(A3) + 1.16(A5/A5A) + 1.05(B2/B2A) + .98(Al) +
.96(A4)
8 Y= 162.74 + 1.00(B1) + 1.18(A2S) + 1.06(A3) + 1.13(AS/ASA) + 1.03(B2/B2A) + 1.01(A1)

+ 1.07(A4) + .87(B4)
9 Y'= 79.10 + 1.01(B1) + 1.09(A2S) + 1.04(A3) + 1.02(A5/ASA) + 1.02(B2/B2A) + .97(A1) +
1.0(A4) + .95(B4) + .93(B3S)

Shortcut Approach

Both of these potential negative outcomes raise the question of whether an easier-
to-implement (i.e., shortcut) prediction procedure could be used on the range with
minimal, if any, sacrifice of predictive accuracy. Visual inspection of the regression
coefficients shown in Table 4 revealed that most values were close to 1.00, thereby
suggesting the possibility of eliminating them altogether and substituting a procedure that
weighs each engagement equally and eliminates the constant. If so, a shortcut prediction
procedure could be reduced to three simple steps:

1: Add the engagement scores of the desired engagement subset size.
2: Divide the sum by Nau», the number of engagements in the subset.
3: Multiply the quotient by 10.

Accordingly, each engagement is weighted equally by dividing by the number of
engagements in the subset (Ns), and the mean of all engagements in the subset is
extrapolated to a ten-engagement total score (by multiplying by 10), thereby lumping the
variance from all available engagements into a single predictor.

The efficacy of this procedure was tested by constructing a series of shortcut
predictor variables; one for each subset size, based on the best predictive set of
engagements identified in the stepwise regression procedures. For the two-engagement
subset, for example, the first shortcut predictor variable was calculated by the procedure:
{1 + A2S)/2] x 10. Thus, if a crew fired a score of 55 on Engagement B1 and a score



References

Department of the Army (1993). Tank gunnery training (Abrams) (FM 17-12-1-2).
Washington, DC: Author.

Department of the Army (1998). Tank gunnery training (Abrams) (FM 17-12-1-2).
Washington, DC: Author.

Hagman, J. D., & Morrison, J. E. (1996, November-December). A strategy for efficient
device-based tank gunnery training in the Army National Guard. Armor, 48-50

Hagman, J. D., & Smith, M. D. (1996). Device-Based prediction of tank gunnery
performance. Military Psychology, 8, 59-68.

Hagman, J. D., & Smith, M. D. (1999, March-April). How the guard could cut costs on
Table VII without really trying. Armor, CVIII(2), 47-49.

Hays, W. L. (1963). Statistics. New York: Holt, Rinehart, and Winston

Krug, R. E., & Pickell, G. A. (1996, February). SIMITAR sharpens the guard. Army, 57-
59.

Shaler, M. D. (1995). Compressed gunnery program for Abrams Tank battalions:
Trainer’s handbook. Arlington, VA: Houston Associates.

Smith, M. D., & Hagman, J. D. (1998). Enhancing the resource efficiency of live-fire tank
gunnery evaluation (Tech. Rep. 1088). Alexandria, VA: U.S. Army Research
Institute for the Behavioral and Social Sciences. (AD A368 641)

SPSS, Inc. (1994). SPSS 6.1 for Windows update. Chicago, IL: SPSS, Inc.

Tatsuoka, M.M. (1969). Selected topics in advanced statistics: Validation studies.
Champaign, IL: The Institute for Personality and Ability Testing.

U.S. Army Armor School (1995). Enhanced mounted brigade training strategy. Fort
Knox, KY: Author.

17



FOREWORD

For the Army’s armor units to attain and maintain readiness levels in the face of shrinking
resources, more efficient ways to conduct tank gunnery training and evaluation must be
identified. Although increased reliance on the use of training aids, devices, simulators, and
simulations (TADSS) for tank gunnery appears to be paying off, additional efficiencies are also
likely to result from efforts to streamline the structure and content of live-fire-based gunnery
evaluation exercises, or tables. The rising cost of main gun ammunition, growing restrictions on
access to live-fire range/maneuver areas, and the difficulty/cost associated with transporting
soldiers/crews to and from these areas suggest that the benefits of more efficient live-fire tank
gunnery evaluation could be substantial.

This report describes the results of research showing that the efficiency of live-fire tank
gunnery evaluation on Tank Table VIII (the crew-level certification exercise) can be enhanced by
changing its content, to include fewer engagements, and its structure, to include performance
"gates" to support early qualification and remediation decisions. By making these changes, the
Amny can save roughly 20% of the ammunition, operational tempo (OPTEMPO), and range time

Tesources normally spent on Tank Table VIII evaluation without jeopardizing its purpose or
intent.

This research was conducted by the U.S. Army Research Institute for the Behavioral and
Social Sciences Reserve Component Training Research Unit (ARI-RCTRU), whose mission is to
improve the effectiveness and efficiency of RC training through use of the latest in training and
evaluation technology. This research is supported under Work Package "RC MAX: Maximizing

the Resource Efficiency of RC Weapons Qualification” of ARI's Science and Technology
Program for Fiscal Year 2000.

This research was conducted to update and extend that initially sponsored by the National
Guard Bureau (NGB), under Project SIMITAR (Simulation in Training for Advance Readiness)
under a continuing Memorandum of Understanding initially signed 12 June 1985. Findings have
been presented to Chief, Combined Arms Training Strategy, U.S. Army Armor School.

‘A M. SIMUTIS
Technical Director

Preceding Page's Blank



Fire Two Engagements

Award Early Qualification (Qle)
(or Q2 if rerun)

Remedial Training

Fire Third Engagement

> 248

Fire Fourth Engagement, etc.

Figure 1. Flowchart of TTVII engagement sequence.

Resource Savings

Generally speaking, the earlier in the target engagement firing sequence that early
elimination or qualification predictions can be made, the greater the resource savings will
be. Assuming that each engagement accounts for roughly 10% of the total resources
spent on TTVIIL crews predicted to Q1 after only two engagements would save about
80% of the resources needed to fire all ten. Those predicted to Q] after three
engagements would save 70%, and so on.

Resources are likely to be saved by crews predicted to early Q1 as well as by those
predicted to need remediation. Using the results obtained with pooled group data, the
number of crews in a 44-crew armor battalion that would be predicted to Q1 after each
engagement, as well as the predicted number of engagements they would save, was
calculated. As shown in Table 12, five crews would be predicted to Q1 after two
engagements (i.e., 10.5% [Table 5] x 44 crews in the battalion) and save a total of 40
engagements (i.e., 5 crews x 8 engagements), three crews would be predicted to Q1 after
three engagements and save 21 engagements, and so on, with 88 engagements saved in
all by the entire battalion. Thus, on crews predicted to early Q1 alone, 20 % (88/440) of
an armor battalion’s first-run engagements could be saved merely by applying the above
strategy.

13



Table A-4
Five-Engagement Random Subsets vs The Five Best Predictors

Multiple Adjusted

Engagements R R? F(i,169) SE
A3, A4, AS/ASA, B1, B3S 73 53 195.73* 61.51
Al, A2S, A5/ASA, B1, BS 77 60 253.46* 57.16
A3, A4, A5/ASA, B1, B4 -73 53 194.74* 61.60
A2S,A5/A5A,B2/B2A,B4,B5 67 45 139.40* 66.90
Al, A2S, A3, A4, B2/B2A. -75 55 211.84* 60.20
Mean Random 73 53 199.03 61.47
Best 5 83 -68 366.86* 50.75

Table A-5

Six-Engagement Random Subsets vs The Six Best Predictors

Excluded Multiple Adjusted

Engagements R R2 F(i,169) SE
Al, A3, B3S, BS 82 67 351.36* 51.50
A3, AS/ASA, B3S, BS 81 66 327.48* 52.72
A3, A4, B1, B3S 75 56 213.90* 60.04
A2S, B1, B2/B2A, B5 wal 49 167.52* 64.04
A2S, A4, B2S, BS 78 60 257.71* 56.87
Mean Random 77 60 263.59 57.03
Best Six 87 -76 534.77* 44.28

Seven-engagement random subsets. The first five rows of Table A-6 present the
shortcut-method-based multiple regression results for the five randomly drawn subsets
containing seven engagements. Means in the sixth line of the table are based on these
subsets. The last line in the table provides analogous multiple regression results based on
the four best predictors (Engagements B1, A2S, A3, A5/A5A, B2/B2A, Al, and A4).
Adjusted R’s for the random subsets ranged from .66 to .81 and accounted for an average
of 72% of criterion variance and produced an average SE of 47.70 along with significant
F-values in excess of 327. By comparison, the two best predictors accounted for 84% of
adjusted criterion variance and had an F-value of 892.13.

Eight-Engagement random subsets. The first five rows of Table A-7 present the
shortcut-method-based multiple regression results for the five randomly drawn subsets
containing eight engagements. Means in the sixth line of the table are based on these
subsets. The last line in the table provides analogous multiple regression results based on
the four best predictors (Engagements B1, A2S, A3, A5/A5A, B2/B2A, Al, A4, and B4).
Adjusted R’s for the random subsets ranged from .71 to .90 and accounted for an average
of 82% of criterion variance and produced an average SE of 37.25 along with significant
F-values in excess of 424. By comparison, the two best predictors accounted for 90% of
adjusted criterion variance and had an F-value of 1471.52.

A-3



validation procedures, those interested in performing further tests of predictive validity
should consider doing so with TTVIII data gathered from several different tank crew
samples, just to be on the safe side.

Although the strategy presented here is specific to the AC, it should also apply to the
RC with the exception of the tabled early elimination and qualification cutoff score
values. Additional data still need to be collected from RC tank crews in order to
determine exactly what these values would be.

15



Table 8
A Test of Early Elimination Predictions

__ Identified Crews __

Predictive

Subset Cutoff # of Cum Cum Accuracy
Crews Size Score(<) Crews # % Hits %
171 2 114 25 25 14.7 25 100
146 3 172 6 31 18.1 6 100
140 4 234 4 35 20.5 4 100
136 5 299 1 36 21.1 1 100
135 6 363 2 38 22.2 2 100
133 7 435 2 40 23.4 2 100
131 8 Sil 3 43 25.1 3 100
128 9 587° 1 44 25.7 1 100

Note. Cum = Cumulative
* Mathematically eliminated with a score < 600.

Once these 25 crews were removed from the analysis, the three-engagement subset
size prediction was tested on the remaining 146 crews. Based on this test, six additional
crews (yielding a cumulative 18.1% of the total sample) were identified for early
elimination. All six of these crews indeed failed to Q1. Finally, by the time subset size
reached nine (See bottom row.), 25.7% of the total sample (44 crews) had been correctly
predicted as unlikely to Q1.

Early qualification predictions. An adaptation of Equation 5 can be used to identify
crews likely to fire Q1:

Equa = ( [700 + (1.65 * SEina)] / 10 )* Noub (6)

Where Eouai is the minimum cutoff score necessary for early qualification, and all other
terms are as defined for Equation 5. Crews scoring at or above the specified subset cutoff
scores could be awarded early Q1 status with 95% confidence that, had they been

allowed to fire all ten engagements, they would have received a score of 700 or more.

Table 9 shows the required Equa cutoff score for each subset size. After completing
the number of engagements listed in the Subset Size column, crews achieving a
cumulative score equal to or greater than the corresponding cutoff value in the Minimum
Equa column would be eligible for early Q1.

Testing early qualification predictions. The same steps used for testing early
elimination were also used for testing early qualification. The results of the latter are
shown in Table 10. For the two-engagement subset, 18 crews (10.5% of the total sample)
were predicted to Q1. This prediction turned out to be correct for all 18 crews, for a hit
rate accuracy of 100%. Once these 18 crews were removed from the analysis, the
prediction for the three-engagement subset size was tested on the remaining 153 crews.
Here, 11 additional crews (yielding a cumulative 17% of the total sample) were identified

10


sequence that these predictions can be made, the greater will be the resource (e.g., range
time, ammunition, operational tempo [OPTEMPO)]) savings.

Recently, the TTVIII engagements upon which this strategy is predicated have
been changed (Department of the Army, 1998). Consequently, the strategy needs to be
reexamined to determine its applicability to this new set of engagements. The present
research examines this applicability in regard to AC tank crew performance, describes the
process followed in doing so, and concludes with an estimate of resource savings to be
expected from strategy usage.

Method
Data

The first-run, individual engagement and total gunnery scores of 171 M1A2 tank
crews undergoing semiannual TT VIII qualification firing at Fort Hood, TX, were
analyzed. Total scores could vary from 0-1000 (i.e., up to 100 points for each of ten
individual engagements) with a qualification score of 700 or more needed for crew
proficiency certification.

Analyses

Following procedures described by Smith and Hagman (1998), stepwise multiple
regression routines (SPSS, 1994) were used to determine if fewer than ten TTVIII
engagements (i.e., subsets) could be used to predict total scores (Le., those based on the
firing of all ten engagements). The best subsets of from one to nine engagements were
identified and the predictive validity specified for each. Subset identification was based
on part-whole Pearson Product-Moment coefficients of correlation (r) between individual
engagement and total scores. The best individual predictor (i.e., engagement score) was
then used to construct a prediction equation of the form:

Y= Bot BX) qd)

Where Y' was the predicted total score, By was the intercept/constant (or theoretical total
score when the predictor variable equals zero), B, was the empirically derived regression
coefficient linking changes in the criterion variable (i.e., total score) with changes in the
predictor variable (i.e., engagement score), and X; was the engagement score most highly
correlated with the criterion variable.

The remaining nine engagements were then examined to identify the one that best
enhanced the predictive power of the first engagement. After all pairwise combinations
of the original predictor with each of the remaining potential predictors were tested and
the best second predictor identified, a new multiple regression prediction equation was
developed using the combined predictive power of the two best predictors. The new
prediction equation took the following form:



ENHANCING THE EFFICIENCY OF TANK GUNNERY EVALUATION: A STRATEGY
REVISITED

CONTENTS

INTRODUCTION

Descriptive Data.

Implementing the Strategy
Resource Savings

a)

Cross Validation. 4
Pooled-Group Prediction Equations 5
Shortcut Approach............... 6
Random Engagement Subset 7
Predicting Qualification ....... 8
12

13

SUMMARY AND CONCLUSIONS...

REFERENCES

APPENDIX A. RANDOM ENGAGEMENT SUBSETS.

LIST OF TABLES
Table 1. TTVIII Descriptive Data .......scsesscssessseseessceseseeneessensscsnssessesseesessnessesssecnssassecasenennennegee 4

2. TTVIII Correlation Matrix ...

3. Stepwise Multiple Regression Results ..........:s-sessscssssessessessesessessessssesstenssnseeneeseseneeneenes 5

4, Prediction Equations for Subset Sizes 1 to 9 .

ix



ENHANCING THE EFFICIENCY OF TANK GUNNERY EVALUATION: A
STRATEGY REVISITED

EXECUTIVE SUMMARY.

Research Requirement:

Develop a target engagement reduction strategy for enhancing the efficiency of
live-fire gunnery evaluation on Tank Table VIII (TT VII).

Procedure:

The first-run individual engagement and total gunnery scores of 171 M1A2 tank
crews undergoing TT VIII qualification firing at Fort Hood, TX, were analyzed via linear
regression routines to determine if fewer than the typically required ten engagements can
be used to predict successful qualification.

Findings:

From these analyses, an easy-to-use strategy was developed for predicting which
armor crews will, and will not, first-run qualify on TT VIII before all ten engagements
have been fired. Scores are added as each engagement is fired and the resulting sum is
compared to tabular formatted cutoff scores established to support accurate qualification
predictions.

Use of Findings:

Adherence to this strategy will help Active Army armor unit commanders to
maximize the efficiency of tank gunnery evaluation by reducing the number of first-run
engagements fired, as well as the range time and operational tempo (OPTEMPO)
resources spent in doing so, by roughly 20% without sacrificing the purpose and intent of
the crew gunnery certification process.

vii



for early Q1, again with 100% predictive accuracy. Subsequent rows may be interpreted
in a similar manner with each row containing the data for its respective subset size.

Table 9
Minimum Equa Values for Early Qualification
Subset Minimum
Size Prediction Equation Equat
2 Eguat = (700 + (1.65 * 79.41)] / 10 * 2 166
3 Equa = [700 + (1.65 * 75.82)] / 10 * 3 248
4 Equa = [700 + (1.65 * 69.56)] / 10 * 4 326
5 Equar = [700 + (1.65 * 61.78)] / 10 *5 401
6 Equa = [700 + (1.65 * 57.32)] / 10 * 6 477
7 Equa = [700 + (1.65 * 47.94)] / 10 *7 545
8 Eguai = [700 + (1.65 * 37.44)] / 10 * 8 609
9 Equa = [700 + (1.65 * 29.27)] / 10 * 9 673
Table 10
A Test of Early Qualification Predictions
__Identified Cases __
Predictive
Subset Cutoff #of Cum Cum Accuracy
Crews Size Score(>) Crews # % Hits %
171 2 166 18 18 10.5 18 100
153 3 248 i 29 17.0 i 100
142 4 326 4 33 19.3 4 100
138 5 401 4 37 21.6 4 100
134 6 477 5 42 24.6 5 100
129 7 545 2 44 25.7 2 100
127 8 609 13 57 33.3 13 100
114 9 673 10 67 39.2 10 100

Note. Cum = Cumulative

Combined effects of early elimination and early qualification predictions. Based on
the combined early elimination and qualification predictions shown below in Table 11,
about a quarter of the crews (See the last column.) in the present sample could have been
removed from the range after firing only two engagements. Over one third could have
been removed after firing only three engagements, about one half after seven
engagements, and so on. Finally, the bottom row shows that less than 35% of the crews
would have needed to fire all ten engagements to determine their Q1 status, because their
scores continued to fall between the cutoff values established for early elimination and
qualification.



Enhancing the Efficiency of Tank Gunnery Evaluation: A Strategy Revisited
Introduction

For the Army’s combat units to attain and maintain required readiness levels in the
face of shrinking resources, more efficient ways to conduct crew-served weapons training
and evaluation must be identified. Although it is not always clear how to train and
evaluate more efficiently, various approaches based on the use of training aids, devices,
simulators, and simulations (TADSS) appear to be paying off. The Army National
Guard’s (ARNG’s) decision to increase TADSS usage in an effort to improve the
efficiency of tank gunnery training and evaluation (e.g., Krug & Pickell, 1996, February),
for example, has resulted in the development of a number of useful TADSS-based
products. These include a tool for predicting crew-level, live-fire tank gunnery
performance from that fired on the Conduct-of-Fire Trainer (COFT) (Hagman & Smith,
1996), a strategy for using this tool in support of home station, TADSS-based gunnery
training and evaluation (Hagman & Morrison, 1996), and a host of other TADSS-based
strategies designed to maximize the payoff from resources spent (e.g., Shaler, 1994; U.S.
Army Armor School, 1995).

Although increased reliance on the use of TADSS is likely to produce continued
improvements in the efficiency of tank gunnery training and evaluation in the Active, as
well as the Reserve, Component (AC/RC), additional efficiencies are also likely to result
from efforts to streamline the structure and content of live-fire-based gunnery evaluation
exercises (.e., tables). The rising cost of main gun ammunition, growing restrictions on
access to live-fire range/maneuver areas, and the difficulty/cost associated with
transporting soldiers/crews to and from these areas suggest that the benefits of more
efficient live-fire tank gunnery evaluation could be substantial.

To this end, the U.S. Army Research Institute (ARI) (ie., Smith & Hagman, 1998;
Hagman & Smith, 1999, March-April) has developed a strategy for enhancing efficiency
by reducing the number of target engagements fired on Tank Table VIII (TTVIID (i.e.,
the crew-level proficiency certification exercise) without compromising evaluative
validity. TT VIII consists of ten engagements, selected from a set of twelve, that
encompass a variety of offensive and defensive combat scenarios involving both single
and multiple stationary and moving targets (Department of the Army, 1993). Although
performance demonstrated on a ten-engagement scenario has historically been used to
certify crew-level gunnery proficiency on TTVIIL, the tightening of resources over the
last decade prompted ARI’s development of a strategy that employs fewer engagements
to do the same job.

In general, this target engagement reduction strategy uses the crew performance
scores obtained as each engagement is fired to predict what the final ten-engagement
score would be. These predictions are then used to either qualify a crew or send it back
for remedial training (e.g., using TADSS) -- two actions that heretofore have had to await
the firing of all ten engagements. The earlier in the TT VIII target engagement firing



Appendix A
Random Engagement Subsets

Two-Engagement random subset. The first five rows of Table A-1 present the
shortcut-method-based multiple regression results for the five randomly drawn subsets
containing two engagements. Means in the sixth line of the table are based on these
subsets. The last line in the table provides analogous multiple regression results based on
the two best predictors (Engagements B1 and A2S). Adjusted R’s for the random subsets
ranged from .16 to .37 and accounted for an average of 23% of criterion (total TTVIII
score) variance and produced an average SE of 79.01 along with significant F-values in
excess of 33. By comparison, the two best predictors accounted for 37% of adjusted
criterion variance and had an F-value of 101.30.

Table A-1
Two-Engagement Random Subsets vs The Two Best Predictors

Multiple Adjusted

Engagements R R2 F (1,169) SE
Al, Bl A9 23 52.58* 78.92
A3, B3S AT .22 48.13* 79.73
B2/B2A, B4 40 16 33.11* 82.64
B1, A2S 61 37 101.30* 71.46
A4, B3S Al 17 34.82* 82.29
Mean Random 48 23 53.99 79.01
Best Two 61 37 101.30* 71.46

* p<.05 for this and all following tables

Three-Engagement random subsets. The first five rows of Table A-2 present the
shortcut-method-based multiple regression results for the five randomly drawn subsets
containing three engagements. Means in the sixth line of the table are based on these
subsets. The last line in the table provides analogous multiple regression results based on
the three best predictors (Engagements B1, A2S, and A3). Adjusted R?s for the random
subsets ranged from .22 to .40 and accounted for an average of 30% of criterion variance
and produced an average SE of 75.44 along with significant F-values in excess of 48. By
comparison, the two best predictors accounted for 49% of adjusted criterion variance and
had an F-value of 167.57.

Four-Engagement random subsets. The first five rows of Table A-3 present the
shortcut-method-based multiple regression results for the five randomly drawn subsets
containing four engagements. Means in the sixth line of the table are based on these
subsets. The last line in the table provides analogous multiple regression results based on
the four best predictors (Engagements B1, A2S, A3, and A5/A5A). Adjusted R’s for the
random subsets ranged from .36 to .46 and accounted for an average of 41% of criterion
variance and produced an average SE of 69.21 along with significant F-values in excess
of 94. By comparison, the two best predictors accounted for 59% of adjusted criterion
variance and had an F-value of 249.22.

A-l



Technical Report 1114

Enhancing the Efficiency of Tank Gunnery
Evaluation: A Strategy Revisited

Joseph D. Hagman
U.S. Army Research Institute

May 2001

20010618 073

United States Army Research Institute
for the Behavioral and Social Sciences

Approved for public release; distribution is unlimited.



Table 1

TIVII Descriptive Data

Engagement Mean SD
Total 719.84 90.10
Al 73.32 29.95
A2S 58.33 30.14
A3 68.46 29.59
A4 89.85 26.98
AS/ASA. 73.73 25.79
BI 64.60 38.54
B2B2A 65.26 27.85
B3S 68.62 21.94
B4 80.50 24.46
BS 80.16 20.38

Table 2
TTVIII Correlation Matrix

Al A2S  A3  A4 AS/ASA BI B2/B2A B3S_ BA BS
Total 20" 39 35% =.28* = 36* 44*# 30 34* 18* 2 8*

Al -.09 -.04 «14 01 -.08 -.03 03 -.02 12
A2S 01 .05 -.03 -.07 Al 12 07 12
A3 03 06 ~.02 -.03 04 -.01 .06

A4 -.08 14 -.06 09 -.14 02
AS/ASA 07 .03 13 .03 02
BI 10 -.01 <1 03
B2/B2A, 02 03 04
B3S, 09 -.05
B4 -.05

*p<.05

Cross Validation

Before proceeding with additional analyses, a split-half, cross-validation design
(Tatsuoka, 1969) was used to examine the potential generalizability of the present
performance scores to those of future AC tank crews. Eighty-six of the 171 tank crews in
the current sample were assigned at random to the normative group while the remaining
eighty-five crews were assigned to the cross-validation group. Stepwise, least-squares,
multiple regression routines were then used to identify the best predictive subsets of from
1 through 9 predictor variables for the normative group with a separate equation
developed for each subset size. All prediction equations were significant, producing
Multiple R’s ranging from .50 (based on 1 predictor) to .98 (based on 9 predictors) and F-
ratios ranging from 27.95 (1, 84) to 194.74 (9, 76).

The generalizability/validity of the normative group equations was then tested on the
cross-validation group and the accuracy of predictions for the two groups compared. The
resulting z tests for differences between Multiple R’s (Hays, 1963, p. 532) revealed that,
regardless of the number of predictors involved, the predictions developed from


Y= Bot BX) + BX) 2)

Where Y’, Bo, Bi, and X;, were as defined in Equation 1, B, was the empirically derived
regression coefficient linking changes in the criterion variable with changes in the second
predictor variable, and X2 was the second predictor variable--the one that best augmented
the predictive power of the original predictor.

The two-predictor multiple regression prediction equation was then fitted to the data,
yielding a new set of criterion residual scores which represented the criterion scores after
the linear effect of the first two predictors was removed. The remaining engagements
were then examined to identify the one that best enhanced predictive power when it was
added to the two-predictor model to form a three-predictor model. This step produced a
new prediction equation structurally similar to Equation 2 except that it contained the
term, B3(X3), which represented the third predictor and its empirically determined
regression coefficient:

Y= Bo + By(X) + Bo(X2) + Bs(%3) @)

This procedure was repeated as long as additional predictor variables significantly
enhanced the predictive power of the resulting equation, which took the general form:

Y= Bo + BX) + Bo(X2) + B3(X3) + Ba(Xn) (4)

Where Y', Bo, Bi, Xi, B2, X2, B3, and X3 were as defined in Equations 1-3 and the term
B,(X,) represented the nth predictor variable (X,) and its empirically determined
regression coefficient, By.

Results

Descriptive Data

Table 1 shows the means and standard deviations (SDs) for total and individual
engagement scores while Table 2 shows the part-whole correlations obtained between the
total score and each engagement score along with the intercorrelations obtained between
each engagement pair. The part-whole correlations ranged from .18 (Engagement B4) to
.44 (Engagement B1), with a mean of .32, and were all statistically significant with the
rejection region for this and all subsequent analyses set at .05. In contrast, the
intercorrelations among individual engagements ranged from -.14 (Engagements A1/A4
and A4/B4) to .14 (Engagements A4/B1), with a mean of .01, and were all
nonsignificant. The low intercorrelations among individual engagements revealed that
performance on one engagement cannot be predicted on the basis of performance on
another, whereas the higher part-whole correlations revealed that every engagement had
the potential of making a contribution to total score predictions.



CONTENTS (Continued)

Page

5. Adjusted R’ Values for Full Regression Models vs Shortcut Regression Models ............ 7

6. Adjusted R’ and SE Values for Random and Best Engagement Subset Sizes 2-9 ........00+ 8

7. Minimum Egim Values to Avoid Early Elimination ............csessssssessssssesssscsesssessensneeseenees 9

8. A Test of Early Elimination Predictions ..

9. Minimum Equa Values for Early Qualification...........ssssssessssssessessesessesncsesceseeneneeneesnes 11

10. A Test of Early Qualification Predictions............c.scssssssssssesssssessssesssesseesessseseeseseneseeneeee 11

11. Combined Early Elimination and Early Qualification Predictions .............c:sesssssseeseeneeee 12
12. Predicted # of Engagements Saved by an Armor Battalion on the First Run

Of TT VILL oe eeesseseesssscsessssesscscsssnsssssesscscsssnescsssesecsrsacsesscecssseucscseesssesassceeenenensenesuesaeen 14

LIST OF FIGURES
Figure 1. Flowchart of TTVIII engagement sequence ...........:sessssessssssesseseserseeeseeeseseeneneseseneenees 13
x



Table 12

Predicted # of Engagements Saved by an Armor Battalion

on the First Run of TIVIL
# of Engagements Predicted # of Predicted # of
Fired Early Q1 Crews Engagements Saved

2 5 40
3 3 2
4 1 6
5 1 5
6 1 4
7 1 3
8 3 6
9 3 3

Total: 18 Total: 88

Battalion resources should also be saved on crews predicted to need remedial
training simply because they can be identified before they have fired all ten TT VIII
engagements. How much savings, however, would depend on how many rerun
engagements are fired once these crews are returned to the range. Having these crews
start their reruns from the beginning, and then reapplying the engagement subset cutoff
scores, should help to maximize the savings on their rerun attempt. Thus, in general,
reducing the number of engagements fired through early prediction of which crews will,
and which will not, first-run qualify should translate into fewer main gun rounds, less
range time, and reduced OPTEMPO costs each year on TTVIII.

Summary and Conclusions

The findings of the present research reinforce and extend the findings of that
performed previously (Smith & Hagman, 1998; Hagman & Smith, 1999, March-April)
suggesting that more efficient live-fire gunnery evaluation is possible in AC armor units
simply by changing current TT VIII content, to include fewer engagements, as well as
structure, to include performance cutoff scores or “gates” to support early qualification
and remediation decisions. To this end, the target engagement reduction strategy
provided herein will promote TTVIII efficiency by reducing roughly 20% of the number
of engagements fired, as well as the OPTEMPO resources and range time spent in doing
so, all without sacrificing the purpose and intent of the evaluation process. These savings
can then be used to offset future resource cuts, pocketed, or used for other purposes such
as platoon-level gunnery (e.g., TTXII).

This strategy, provided in flowchart format, can be easily implemented at the
company or battalion level by first adding the scores of engagements as they are fired and
then determining the resulting summed score status in relation to early elimination and
qualification cutoff scores provided in accompanying tabular format. To the extent that
future tank crew performance is similar to that found in the present research, the resulting
qualification vs nonqualification predictions will, on the average, be accurate 95% of the
time. Although the validity of these predictions was tested herein using split-half cross-

14



Table A-6
Seven-Engagement Random Subsets vs The Seven Best Predictors

Excluded Multiple Adjusted .
Engagements R R? F169) SE

Al, A5/A5A, B2/B2A 84 71 414.50* 48.63

AS/ASA, B1, BS 81 66 327.69* 52.71 ‘
A2S, B2/B2A, B4 83 69 382.04* 50.05
A3, A4, B3S 85 71 432.37* 47.91
A4, B4, BS 90 81 730.06* 39.18
Mean Random 85 72 457.33 47.70
Best Seven 92 84 892.13* 36.06

Table A-7

Eight-Engagement Random Subsets vs The Eight Best Predictors

Excluded Multiple Adjusted

Engagements R R? F(1,169) SE
B4, BS 94 88 1249.43* 31.19
Al, A4 91 82 818.51* 37.38
B38, BS 95 90 1471.52* 29.00
A3, Bl 85 7A 424.78* 48.21
Al, B2/B2A 89 80 673.40* 40.48
Mean Random 91 82 927.53 37.25
Best Eight 95 90 1471.52* 29.00

Nine-Engagements random subsets. The first five rows of Table A-8 present the
shortcut-method-based multiple regression results for the five randomly drawn subsets
containing nine engagements. Means in the sixth line of the table are based on these
subsets. The last line in the table provides analogous multiple regression results based on
the four best predictors (Engagements B1, A2S, A3, A5/A5A, B2/B2A, Al, A4, B4, and
B38). Adjusted R’s for the random subsets ranged from .82 to .94 and accounted for an
average of 89% of criterion variance and produced an average SE of 29.12 along with
significant F-values in excess of 755. By comparison, the two best predictors accounted
for 95% of adjusted criterion variance and had an F-value of 3145.88.

A-4



Technical Report 1114

Enhancing the Efficiency of Tank Gunnery
Evaluation: A Strategy Revisited

Joseph D. Hagman
U.S. Army Research Institute

Reserve Component Training Research Unit
Ruth H. Phelps, Chief

U.S. Army Research Institute for the Behavioral and Social Sciences
5001 Eisenhower Avenue, Alexandria, Virginia 22333-5600

May 2001

ee
Army Project Number Personnel Performance
20363007A792 and Training

Approved for public release; distribution unlimited.

iii


of 97 on Engagement A2S, its predicted shortcut total score would be 760. This shortcut
score could then used to predict a crew’s total scores.

The results of the shortcut test are shown in Table 5. The column under “Regression
Models” shows Adjusted R? values for each subset size for the best engagement
predictors, as determined by stepwise multiple regression procedures. The column under
“Shortcut Models” shows Adjusted R? values using analogous shortcut regression
procedures based on the same best predictors for each subset size. The values in the two
columns turned out to be identical and indicate that the shortcut prediction method can
indeed be used successfully with reduced subsets of any size.

Table 5
Adjusted R’ Values for Full Regression Models vs Shortcut Regression Models
Regression Shortcut
Subset Models Models
Size Adjusted R? Adjusted R?
2 37 37
3 49 49
4 59 59
5 68 68
6 -76 76
7 84 84
8 90 90
9 95 95

Random Engagement Subsets

Even with the easier calculational procedures afforded by the shortcut method, using
the statistically identified best predictors requires that specific engagements be fired for
each subset (i.e., B1 and A2S for the two-engagement subset) and introduces the
possibility of “training to the test” in order to save time, especially if a commander were
to select a TTVIII certification subset with relatively few engagements. To encourage
training on the widest variety of engagements possible in preparation for TT VII firing,
engagements to be included in any particular subset could be selected at random. This
would necessitate training on all possible engagements because crews would not know
beforehand which ones would be included in the subset selected for certification.

To determine if engagements can indeed be selected at random without appreciably
degrading predictive accuracy, random subsets of engagements were constituted for
subset sizes ranging from two to nine. Randomization was accomplished by labeling ten
coins (i.e., one for each engagement). The coins were then placed in a hat and blindly
drawn without replacement to create a random subset of engagements of the desired size.
Once a subset was created, drawn coins were returned to the hat, all coins were again
shaken to redistribute them inside the hat, and the process was repeated until a total of
five random subsets were created for each subset size from two to nine. Subsets of size
six or greater were created by random exclusion. That is, to create a six-engagement



Early elimination predictions. For any given random engagement subset size (Nsuv),
the minimum cutoff score (Egjim) necessary to avoid early elimination (i.e., removal from
the range for remediation purposes) can be predicted from the general equation:

Extim = ( [700 - (1.65 * SEin)] / 10)* Nouv (5)

Where Exim is the minimum subset cutoff score needed to avoid early elimination, 700 is
the minimum ten-engagement-based score required for qualification, 1.65 is the normal
deviate (in a one-tailed directional test) for a 95% confidence interval (lower bound),
SEina is the standard error of estimate for the minimum cutoff score of 700 (assuming that
the actual probability of firing 700 will follow a normal distribution), and Ney» is the
engagement subset size on which the prediction is based. The SEing values used in this
case to test the prediction for each random subset size represent the mean SEing obtained
from the five randomly formed subsets identified earlier for each subset size (See
Appendix A for the engagement composition of each subset.).

Testing early elimination predictions. The early elimination predictive cutoff values,
shown in Table 7, were then tested against the gunnery scores fired by the pooled group
sample. For each subset size of engagements, tank crews’ scores were compared to the
cutoff scores in the far right column. Crews with scores that were less than the
corresponding tabled cutoff score were predicted to have less than a 5% chance of first-
run qualification (ie., Q1), if they were to proceed with the firing of all ten engagements.

Table 7
Minimum Egiim Values to Avoid Early Elimination
Subset Minimum
Size Prediction Equation Egtim
2 Egtim = [700 - (1.65 x 79.41)}/10 x2 114
3 Egtim = [700 - (1.65 x 75.82)]/10 x 3 172
4 Egtim = [700 - (1.65 x 69.56)]/10 x 4 234
5 Estim = [700 - (1.65 x 61.78)]/10 x 5 299
6 Exim = [700 - (1.65 x 57.32)]/10 x 6 363
7 Etim = [700 - (1.65 x 47.94)]/10 x7 435
8 Egtim = [700 - (1.65 x 37.44)]/10 x 8 S11
9 Egtim = [700 - (1.65 x 29.27)]/10 x 9 587

2 Mathematically eliminated with a score < 600

The early elimination test results are shown in Table 8. Based on the scores obtained
for a subset containing two engagements (See first data row.), for example, 25 crews
(14.7% of the total sample) were identified for early elimination with 100% accuracy, as
shown in the last column. Thus, all crews predicted to be nonqualifiers after firing two
engagements indeed did not qualify after firing ten (referred to as a Hit).



REPORT DOCUMENTATION PAGE

41, REPORT DATE (dd-mm-yy) 2. REPORT TYPE
May 2001 Final

3. DATES COVERED (from. . . to)
October 2000 - April 2001

4, TITLE AND SUBTITLE

Enhancing the Efficiency of Tank Gunnery Evaluation:
A Strategy Revisited

Sa CONTRACT OR GRANT NUMBER

5b. PROGRAM ELEMENT NUMBER
633007

6. AUTHOR(S)

Joseph D. Hagman (U.S. Army Research Institute)

Sc, PROJECT NUMBER
A792

5d. TASK NUMBER
207

Se. WORK UNIT NUMBER
HOL

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

U.S. Army Research Institute for the Behavioral and Social Sciences
5001 Eisenhower Avenue

Alexandria, VA 22333-5600

8. PERFORMING ORGANIZATION REPORT NUMBER

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10. MONITOR ACRONYM
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11. MONITOR REPORT NUMBER
Technical Report 1114

12. DISTRIBUTION/AVAILABILITY STATEMENT
Approved for public release; distribution is unlimited.

13. SUPPLEMENTARY NOTES

intent of the crew-level gunnery certification process.

14. ABSTRACT (Maximum 200 words): Based on the analysis of gunnery scores fired by 171 M1A2 tank crews, an easy-
to-use strategy was developed for predicting which crews will, and will not, first-run qualify on Tank Table VII
before all of the typically required ten engagements have been fired. Scores are added as each engagement is fired
and the resulting sum is compared to tabular formatted cutoff scores established to support accurate qualification
predictions. Adherence to this strategy will help Active Army armor unit commanders to maximize the efficiency
of tank gunnery evaluation by reducing the number of first-run engagements fired, as well as the range time and
operational tempo (OPTEMPO) resources spent in doing so, by roughly 20% without sacrificing the purpose and

15. SUBJECT TERMS
Active Army Armor Tank Gunnery Tank Table VIII

= os
19. LIMITATION OF
ABSTRACT

16. REPORT | 17. ABSTRACT | 18. THIS PAGE
Unclassified | Unclassified | Unclassified | Unlimited

Efficiency

20. NUMBER 21. RESPONSIBLE PERSON
OF PAGES (Name and Telephone Number)
29 Joseph D. Hagman

208-334-9390



Table A-2
Three-Engagement Random Subsets vs The Three Best Predictors

Multiple Adjusted

Engagements R R? F(1, 169) SE
B4, A5/ASA, BS AT .22 48.30* 79.69
B4, A4, B3S 48 .23 51.42* 79.13
A3, BS, A2S -56 31 78.17* 74.73
A2S, AS/ASA, B2/B2A 63 40 112.19* 70.06
Al, B1, B3S 58 33 85.78* 73.60
Mean Random 54 30 75.17* 75.44
Best Three 71 49 167.57* 64.03

Table A-3

Four-Engagement Random Subsets vs The Four Best Predictors

Multiple Adjusted
R

Engagements R? F(1,169) SE
Al, B5, B2/B2A, AS/ASA 60 36 94.23* = 72.33
B1, B38, BS, AS/ASA 68 46 145.08* 66.29
A4, A3, B4, B2/B2A 63 40 113.99* 69.83
A2S. B3S. BS, A3 63 40 112.64* 70.00
Bl, B2/B2A, A4, Al 66 A4 133.15* 67.58
Mean Random 64 Al 120.94 69.21
Best Four 77 59 249.22* 57.45

Five-Engagement random subsets. The first five rows of Table A-4 present the
shortcut-method-based multiple regression results for the five randomly drawn subsets
containing five engagements. Means in the sixth line of the table are based on these
subsets. The last line in the table provides analogous multiple regression results based on
the four best predictors (Engagements B1, A2S, A3, AS/ASA, and B2/B2A). Adjusted
R’s for the random subsets ranged from .45 to .60 and accounted for an average of 53%
of criterion variance and produced an average SE of 61.47 along with significant F-values
in excess of 139. By comparison, the two best predictors accounted for 68% of adjusted
criterion variance and an F-value of 366.86.

Six-Engagement random subsets. The first five rows of Table A-5 present the
shortcut-method-based multiple regression results for random subsets containing six
engagements. Means in the sixth line of the table are based on these subsets. The last
line in the table provides analogous multiple regression results based on the four best
predictors (Engagements B1, A2S, A3, A5/A5A, B2/B2A, and Al). Adjusted R’s for the
random subsets ranged from .49 to .67 and accounted for an average of 60% of criterion
variance and produced an average SE of 57.03 along with significant F-values in excess
of 167. By comparison, the two best predictors accounted for 76% of adjusted criterion
variance and an F-value of 534.77.

A-2



U.S. Army Research Institute
for the Behavioral and Social Sciences

A Directorate of the U.S. Total Army Personnel Command

EDGAR M. JOHNSON
Director

ee

Technical Review by

Monte D. Smith, L-3 Communications
Bruce Sterling, US Army Research Institute

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Table A-8
Nine-Engagement Random Subset vs The Nine Best Predictors

Excluded Multiple Adjusted

Engagements R R? F,169) SE
A2S 94 89 1347.80* 30.16
B3S 97 94 2708.85* 21.90
B2/B2A 95 .90 1612.65* — 27.83
Bl 90 82 755.10* 38.65
A4 95 91 1716.22* 27.06
Mean Random 94 89 1628.12 29.12
Best Nine 97 95 3145.88* 20.40

A-S



normative group data accounted for a comparable amount of total score variance in the
cross-validation group. Thus, the predictions were found to be valid and, therefore, likely
to maintain similar accuracy levels when used to predict the TT VIII total scores of future
AC tank crew samples. Given the similar outcomes of the separate group analyses, along
with the desire to obtain the best possible predictions from the largest sample size
possible, subsequent analyses were performed on pooled-group data.

Pooled-Group Prediction Equations

Pooled-group prediction equations were developed for the best predictive subsets of
1 through 9 engagements. The order of engagement entry into the equations is shown in
the first column of Table 3. The equations themselves are shown in Table 4.

Table 3
Stepwise Multiple Regression Results

Order of Multiple Adjusted

Entry R R SE df E
1. BI Ad 19 81.10 1,169 40.85*
2. A2S 61 37 7154 2,168 50.84*
3. AB n 49 6426 3, 167 53.73*
4. ASIASA 78 59 5759 4,166 65.52*
5. B2/B2A 83 68 51.10 5, 165 72.12*
6. Al 88 76 44.02 6, 164 91,38*
7. Ad 92 84 35.85 7,163 130.12*
8. B4 95 90 28.89 8,162 —186.39*
9. B3S 98 95 20.60 9, 161. 343.47
*p<.05

The prediction equation for each subset size was significant, producing Multiple R’s
ranging from .44 (based on one predictor) to .98 (based on nine predictors) and F- ratios
ranging from 40.85 to 343.47. The first predictor to enter the equation (Engagement B1)
had the highest zero-order correlation (.44) with the criterion and accounted for almost
one fifth (Adjusted [for shrinkage] R? = 19%) of TTVIII total score variation. The
addition of the second predictor (Engagement A2S) boosted the proportion of explained
variance to 37%, with this proportion steadily increasing with the addition of each
subsequent predictor until a 95% total score predictive accuracy was achieved from the
firing of the nine-member subset which included all engagements except for B5. Thus,
after firing these nine engagements, total score predictions (i.e., the score obtained if all
ten engagements were fired) would have an accuracy rate of 95%. After firing the first
eight engagements, the predictive accuracy rate would be 90%, and so forth.

Although the firing order of these engagements would be limited only by practical
considerations (e.g., firing the day engagements first and then the night engagements, or
vice versa), implementation of the above prediction approach would saddle the user with
having to complete a cumbersome calculation procedure in order to arrive at the desired
TTVIII total score predictions. The commander who wants to trim two engagements
from the standard ten, for instance, must have his crews fire eight engagements, score



subset, four engagements were drawn randomly and excluded. The six engagements
remaining in the hat became the subset. For subsets of size seven, three engagements
were randomly excluded, and so on. This produced five 2-engagement random subsets,
five 3-engagement random subsets, and so on, up to and including five 9-engagement
random subsets. In all, 40 random subsets were constructed, five for each of the eight
possible subset sizes.

Multiple regression procedures based on the shortcut method were used to construct
a prediction equation for each of the 40 random subsets created (See Appendix A for the
specific engagements drawn for each random subset and associated statistical
information.) The predictive power of the random engagement subsets was then
compared to that of the best engagement subsets. As expected, the best predictors
accounted for a greater proportion of total score variance than that accounted for by the
mean random engagement predictors (See Table 6) across the different subset sizes.
Although predictive accuracy uniformly favored the former over the latter predictors, the
mean difference between the two was only 12% across the eight subset sizes. Arguably,
this difference is more than offset by the elimination of the possibility of training to the
test when random engagement subsets are used. In addition, use of these subsets affords
armor unit commanders maximum flexibility in the selection of engagements that can now
be best fitted to existing targeting sequence layouts on the range, thereby promoting the
most efficient use of OPTEMPO resources set aside for TTVIII firing. Consequently, the
remaining sections of this report assumes that engagements are selected on a random
basis.

Table 6.
Adjusted R’ and SE Values ‘for Random and Best Engagement Subset Sizes 2-9
Subset Random Best
Size Adjusted R? SE Adjusted R? SE
2 23 79.01 37 71.46
3 30 75.44 49 64.03
4 Al 69.21 59 57.45
5 53 61.47 68 50.75
6 60 57.03 76 44.28
7 72 47.70 84 36.06
8 82 37.25 90 29.00
9 89 29.12 95 20.40
Predicting Qualification

This section extends the analytical procedures outlined thus far to the prediction of
crew qualification status. The capability to predict which crews will, and will not, first-
run qualify, as each engagement is fired, would allow armor unit commanders to award
early qualification status to some crews (i.e., those predicted to fire 700 or above) and to
identify others in need of remediation (Le., those predicted to fire below 700). The
specific procedures for deriving such predictions are described below.