1. AGEC 730 Applied Agribusiness Logistics
Relation of Dark Cutting Incidence in five
Commercial feedlots with different stressful conditions
Pablo Garcia
Kansas University
October, 2015

2. Executive Summary
Stress induced meat quality problems such as dark cutters cause large monetary losses
to the livestock industry, pre-slaughter stress is one of the most often associated
causes. One of the main goals of this research is to provide useful tools and information
that allows the beef industry to be more efficient, by reducing losses in all the supply
chain that is affected by dark cutter carcasses: Producer, manufacturer, retailer and
finally to satisfy the final customer who is the one who buys the final product from the
supply chain. Dark Cutter Carcasses (DCC) is the cause of a chemical reaction that
happens when animals are exposed to stressful conditions and there is a depletion of
muscle glycogen (blood sugar). The severity of the decline in muscle glycogen depends
on the duration and severity of the stress.
In this research the variables that were dismantled are: Feedlot origin, sex or gender,
days of feed at the feedlot, number of cattle shipped to the processing plant, Live and
carcass weight, overweight cattle or heavies, distance to plant from feedlot, road or
route conditions. All of these variables were analyzed with a multiple regression model
that eliminated any trend or relation with dark cutter carcasses percentages, leaving just
three considerations to address against the dark cutter carcasses.
From the three independent variables that had a significant relation with DCC %, Sex or
Type of cattle was the one with the highest relation which it can be related as why
Holsteins have a greater percentage of DCC, a breed that is more excitable and more
difficult to handle than the common breeds. Steers showed a higher percentage of DCC
than heifers and this confirms researchers that found the antagonistic behavior of steers
(mounting, fighting and chin resting) disturbed and stressed more than heifers in estrus

3. just showing mounting behavior. The independent variable “Number of cattle”
essentially shows the relation with DCC as for every increment on cattle shipped and
processed at the plant there will be an increase of dark cutter percentage which is
inversely proportional. The third independent variable was days on feed (DOF) cattle
spent at the feedlot where chart 5 shows the peaks of the days on feed where DCC are
more likely to occur in cattle. This statistical relation can be backed up by age of
animals where if they are too young, they also tend to be wilder as they have had less
human interaction and have to spend more time at the feedlot to get more familiarized
with handling. Conversely, older cattle that spend fewer days on feed at the feedlot are
less wild and have being handled by people, so they tend to be less excitable or have a
low (stirry) temperament.
The average of the five feedlots for DCC is 1.57% where Hartley and XIT feedlots have
a greater value than the average, feedlots that are closer than Cimarron and Grant
County meaning that distance is not a crucial factor when implying that it could be of
great impact because of the stress the animal suffers while in the trailer. Grant County
feedlot is the facility with the longer travel distance to the plant and it even shows the
lowest DDC percentage. Equivalently, road or route conditions have absolutely no
statistical significance when trying to relate it to DCC and so this variable is totally
discarded from the hypothesis.
The results obtained in this research not only unveil several hypotheses about
transportation and its relation to stress in cattle that might cause dark cutter carcasses
but also gives the opportunity to research further than these variables which can be
factors such as weather conditions and year seasons which can also be related to DCC.

4. It is recommended that other variables should be studied further are shipping time and
kill time where the shipping time is recorded at the feedlot as what time it left the feedlot
and at what time was it killed by the slaughter house or packing plant and so this can be
another hypothesis that can have great impact on dark cutter carcass percentage
because this is time where cattle suffer of physical stress that can be dehydration,
hunger and even fatigue.

5. Abstract
The purpose of this study is to identify the possible causes of dark cutter carcasses
found in the packing plants of cattle from similar commercial feedlots in the beef
industry as well as discard the variables that might not have any relation for causing this
unwanted characteristic frequently found in slaughter houses that has a significant
economic impact mainly on producers such as feedlots and ranchers. Pre-slaughter
stress is the most often associated causes of dark cutters but other factors need to be
eliminated in order to reduce the hypothesis of pre-slaughter activities. The independent
variables evaluated include Feedlot origin, Sex of animal, Days on Feed, Number of
Cattle, Live Weight, Carcass Weight, Heavy Carcasses, Distance to Plant and Road
Conditions. With the collected data from 5 different commercial feedlots, several
multiple regression models discarded 6 of the 9 variables evaluated, reducing it to 3
variables: Sex, days of feed and Number of Cattle Shipped. The final regression model
gave a low R-square showing a low accuracy on the model, meaning the independent
variables sex, days of feed and number of cattle may show a trend in relation to dark
cutter percentage but with a low reliability value. In order to have a more accurate
model, more variables need to be added to the model, variables such as time spent at
pens at the slaughter house, weather conditions, year seasons, shipping time vs. killing
time, animal welfare when shipping and receiving cattle at facilities, etc.

6. Introduction
Meat quality is a prime mover of the economic value of beef carcasses in the beef
market; this is determined by two main characteristics of value that the consumers
mainly focus on: tenderness, marbling and color (Voon, 1992). One of the biggest
discounts in packing plants for feedlots and cattle producers is dark cattle carcasses
(DCC) which results from a low concentration of muscle glycogen at the time of
slaughter. The lack of glycogen reduces lactic acid formation which results in muscle
with a higher pH than normal (Brett Littler, 2001). When dark cutting carcasses are
exposed to oxygen they fail to go through the chemical reaction of glycolysis and
therefore pH tends to stay at neutral value (7.2) and not turning into the desired cherry
red color. Customers associate dark beef with meat from old animals, toughness and
poor flavor. Additionally, dark cutting beef has poor storage properties and shortened
shelf life. There are too many factors related to the incidence of dark cutter carcasses in
cattle and so the independent variables should be reduced to the minimum so
resources are not spent on elements that have been attributed to be responsible to
DCC and not having the desirable results of reducing the big discounts packing plants
due to feedlots and cattle producers. In this research the variables that were dismantled
are: Feedlot origin, sex or gender, days of feed at the feedlot, number of cattle shipped
to the processing plant, Live and carcass weight, overweight cattle or heavies, distance
to plant from feedlot, road or route conditions.
All of these variables were analyzed with a multiple regression model that eliminated
any trend or relation with dark cutter carcasses percentages, leaving just three
considerations to address against the dark cutter carcasses. This research leaves the

7. responsibility to commercial feedlots or individual cattle producers of using this
information to their convenience, these results were obtained from a one-year period of
a commercial packing plant.
One of the main goals of this research is to provide useful tools and information that
allows the beef industry to be more efficient, by reducing losses in all the supply chain
that is affected by dark cutter carcasses: Producer, manufacturer, retailer and finally to
satisfy the final customer who is the one who buys the final product from the supply
chain.
Methodology
Data was collected from the same slaughter house where the five feedlots were sending
their cattle to have them killed and processed, data base included closeouts for each
and every lot shipped by all of the different feedlots including: lot number, feedlot origin,
sex, quantity per lot, average of live weight, average of carcass weight, dressing
percentage, marbling scores, yield grades and dark cutter percentages. After
consolidating data from September 2014 to September 2015, a one-year worth of data,
the different variables were chose according to its possible relation and/or effect on dark
cutter carcasses, being these the following: Feedlot origin (Cimarron, Coronado, Grant
County, Hartley and XIT), Sex of animal (Steer, Heifer and Holstein which the industry
has separated the Holstein breed cattle away from beef cattle), Days on Feed (Since
when it got to the feedlot until it is shipped out to the packing plant), Number of Cattle
(Number of cattle per lot), Live Weight (Weight taken at feedlot when shipping cattle
out), Carcass Weight (Weight of carcass at the packing plant), Heavy Carcasses (Over

8. weight cattle), Distance to Plant (Distance from feedlot to plant) and Route Conditions
(Number of sharp turns). Where the distance from feedlot to plant was measured
through global position system and the road conditions were rated based on number of
sharp turns and highways taken, being “1” a route in fairly good conditions with three or
less sharp turns, “2” an average route that includes farm market roads with four to six
sharp turns and category “3” being a bad route with more than six sharp turns with farm
to market roads with poor or low maintenance. The Monthly averages were taken from
each of the commercial feedlots from which percentages were calculated and analyzed,
total dark cutters per feedlot, percentage of dark cutters per sex and total dark cutters at
different days of feed, this was made with the only reason to compare and contrast
between the different variables evaluated. With this data on hand, multiple regression, a
mathematical model was used to learn more about the relationship between several
independent variables and a dependent variable (dark cutter carcasses %).
After running the model with all the variables included, the p-values were obtained, a p-
value for each term tests the null hypothesis that the coefficient is equal to zero (no
effect). A low p-value (<0.05) indicates that you can reject the null hypothesis, in other
words, is likely to be a meaningful addition to the model because changes in the
predictor (independent variable) are related to changes in the response variable
(dependent variable) (Frost, 2014). After running two regression models, 6 out of 9
variables were discarded because they did not meet a lower p-value than 0.05. Another
important statistical measure outstanding in the model is the “Adjusted R square” which
basically is the measure that shows how close the data are to the fitted regression line.
R squared value ranges from 0% to 100%, being 0% a low reliable model indicating that

9. the model explains none of the variability of the response data around its mean and a
100% a high accuracy model that explains all the variability of the response data around
its mean (Zaiontz, 2013).
Results
Data resulted showing different results compared to all the variables, when calculating
percentages of the several variables it was found that Holsteins was the “type or
gender” of cattle that had the highest percentage of dark cutter carcasses of 4.16%,
followed by 1.39% of dark cutters on steers and 1.23% of the total of heifers as chart 1
shows. From the total quantity of cattle processed 1.53% was from Cimarron, 1.45%
from Coronado, 1.17% from Grant County, 1.94% from Hartley and 1.77% from
September 2014 to September 2015, the weighted average of all five feedlots for this
year period is 1.57% represented in chart 2. All the data was obtained from the same
packing plant where all of these feedlots send their cattle and the distances and road
conditions from each feedlot and packing plant varies, where Cimarron is 40 mi. and
route conditions was rated as “1”, Coronado is 17mi. and route condition “1”, Grant
County is 131mi. and route condition “2”, Hartley is 41mi. and route condition “3”, and
XIT is 17mi. with a route condition of “2”.
The multiple regression had to be exercised three times (table 1, 2 and 3 respectively)
and for the first regression model the “Adjusted R square” value was 0.43, the standard
error 0.0125 and the independent variables “P-Value” were: Feedlot origin 0.2162, Sex
0.0001, DOF 0.0002, Number of Cattle 0.0428, Live Weight 0.5286, Carcass Weight

10. 0.4720, Heavies 0.0093, Distance to Plant 0.0686, Route Conditions 0.5035. For the
second regression model the results were 0.40 for the “Adjusted R square”, a standard
error of 0.0127 and the variables left had a “P-Value” of 0.0001 for Sex, 2.3269E-20 for
DOF, 0.0542 for Number of Cattle and 0.0831 for Heavies. After eliminating the
variables with a “P-value” higher than 0.05, the third regression model gave an
“Adjusted R square” of 0.40 and the three variables left had a “P-value” of 0.0009 for
Sex, 0.0163 for Number of Cattle and 2.4334E-20 for DOF. The coefficients of this final
regression model for the dependent variable “Dark Cutter Carcasses %” was -0.0187
and for the independent variables “Sex” was 0.0059, for “Number of Cattle” was
9.4711E-07 and for Days of Feed was 0.0001.
Discussion
The purpose of this research was to find a relation between several variables that are
part of the supply chain of the beef industry with an ongoing issue that has a big impact
into economic losses by all the beef industry, from the producer to the retailer: Dark
Cutter Carcasses. The beef industry averages a 1% of dark cutter carcasses, so it can
be difficult to make measurable change as an industry. However, producers can employ
strategies to lower the risk, especially if their cattle have greater than average issues.
Data from the Iowa Tri-County Steer Carcass Futurity indicate that calm cattle returned
$39.01 per head more than aggressive herd mates. On the other hand, discounts for
dark-cutting carcasses can amount to $30/cwt, mainly because consumers are unwilling
to purchase the dark color meat. Stores rarely even place in on the counter so it has to
be sold as pre-cooked or totally cooked meat (O'Diam, 2010). Producers can influence

11. handling, feeding and breeding, but there are many other variables that they cannot
control such as weather. What the industry should focus is on what it can be controlled
and this research presents some of the variables that can be measured and controlled
such as Feedlot origin, Sex or type of cattle, Days on Feed at the feedlot, Number of
Cattle shipped per month, Live weight of cattle, carcass weight, Cattle that are
overweight or “Heavies”, distance to plant and conditions of the route taken from the
feedlot to the plant. From all these variables a first multiple regression model discarded
five variables due to its high “P-value”, this value is used to determine statistical
significance in a hypothesis test where a high P-value likely represents a true null
hypothesis and a low “P-value” suggests that the sample provides enough evidence that
the null hypothesis can be rejected for the entire populations. In fact, “P-values” often
determine what studies get published and what projects get funding. In this statistical
model it is imperative to be aware of the “Adjusted R Square” value which basically
compares the explanatory power of regression models that contain different number of
predictors. In other words, the higher “Adjusted R-square” value the more relation the
independent variables have with the dependent variable (Dark Cutter Carcasses) (Frost,
2014).
The independent variables that had a lower “P-value” than 0.05 were Sex, DOF,
Number of Cattle and Heavies and this first model has a relative low “Adjusted R-
square” value of 0.4298 and a standard error of 0.0125 which represents the average
distance that the observed values fall from the regression line. Conveniently, it tells how
wrong the regression model is on average using the units of the response variable.
Smaller values are better because it indicates that the observations are closer to the

12. fitted line. In the second regression done, “Adjusted R-square” barely decreased to
0.4064, a 2% that reduced the relationship of the independent variables with the
dependent one. “Standard Error”, 0.0127” did not increase significantly (Zaiontz, 2013).
This time “P-values” discarded “Heavies”, giving the necessity of running a third
regression model where only Sex, DOF and Number of Cattle were used. The third and
final regression model had almost the same “Adjusted R Square” value of 0.4001 and
same “Standard error” of 0.0128. The “P-Values” on the third regression models
showed lower than the significance level of 0.05, meaning these variables reject the null
hypothesis of not having any relation with the dependent variable “Dark Cutter
Carcasses”.
The results allow us to be able to predict Dark Cutter Carcasses percentage if we use
the formula Y = a + b1X1 + b2X2 + b3X3. Where:
a = Y-Intercept
b1 = the coefficient “Sex”
b2 = the coefficient “Number of Cattle”
b3 = the coefficient “Days of Feed”
And so, Y = -0.0187 + 0.0059 + 9.4711E-07 + 0.0001
Y = -0.0126
95% of the time the forecast will be at this calculated value + 1.96 standard error, in
other words it will be off Y + 1.96*0.0128(standard error), or 0.0125.
Using multiple regression, it can be said that every 1% of dark cutter carcasses can be
associated with 0.59% of cattle’s sex, 0.0001% with the number of cattle shipped and
0.01% of the days of feed cattle spend at the feedlots.

13. Since the main objective of this research is to determine which predictors are
statistically significant and how changes in the predictors relate to changes in the
response variable, the value of “Adjusted R square” is almost totally irrelevant. Although
there are much more independent variables that can be added to this regression model
to find which one would have a greater relation with the percentage of dark cutter
carcasses, the purpose of this paper is to show the relationship of the variables
provided.
Conclusion
In conclusion, the results of this study provides some fascinating and interesting insight
into the causes of DCC and how related they are. It not only approves some of the
hypothesis found in some literature but also widens the study to other possible variables
that the industry might not be considering when improving their handling techniques
used with cattle to reduce this considerable economic loss that affect suppliers,
manufacturers and retailers in the supply chain of the beef market. Dark Cutter
Carcasses (DCC) is the cause of a chemical reaction that happens when animals are
exposed to stressful conditions and there is a depletion of muscle glycogen (blood
sugar). The severity of the decline in muscle glycogen depends on the duration and
severity of the stress. During normal handling, cattle experience two forms of stress:
- Psychological stress: restraint, handling and novelty.
- Physical stress: Thirst, hunger, dehydration, fatigue, injure or temperature
extremes.
(Brett Littler, 2001)

14. From the three independent variables that had a significant relation with DCC %, Sex or
Type of cattle was the one with the highest relation which it can be related as why
Holsteins have a greater percentage of DCC, a breed that is more excitable and more
difficult to handle than the common breeds. There are a few other studies concerning
the effect of breed on the incidence of dark cutter carcasses. Steers showed a higher
percentage of DCC than heifers and this confirms researchers (Brett Littler, 2001) that
found the antagonistic behavior of steers (mounting, fighting and chin resting) disturbed
and stressed more than heifers in oestrus just showing mounting behavior. Cattle with
excitable temperaments have a higher incidence of DCC, as well as lowered daily
weight gains in the feedlot compared with cattle of better or calm temperament. Animals
with excitable temperament also have the potential to cause physical and psychological
stress to other animals in the herd; such is the case of Holsteins. This can be
interpreted by Chart 3.
The independent variable “Number of cattle” essentially shows the relation with DCC as
for every increment on cattle shipped and processed at the plant there will be an
increase of dark cutter percentage which is inversely proportional. Chart 4 shows the
trend that exists between cattle sent to the slaughter house and the prediction of dark
cutter percentages. The third independent variable was days on feed (DOF) cattle spent
at the feedlot where chart 5 shows the peaks of the days on feed where DCC are more
likely to occur in cattle. This statistical relation can be backed up by age of animals
where if they are too young, they also tend to be wilder as they have had less human
interaction and have to spend more time at the feedlot to get more familiarized with
handling. Conversely, older cattle that spend fewer days on feed at the feedlot are less

15. wild and have being handled by people, so they tend to be less excitable or have a low
(stirry) temperament (Brett Littler, 2001).
The average of the five feedlots for DCC is 1.57% where Hartley and XIT feedlots have
a greater value than the average, feedlots that are closer than Cimarron and Grant
County meaning that distance is not a crucial factor when implying that it could be of
great impact because of the stress the animal suffers while in the trailer. Grant County
feedlot is the feedlot with the longer distance to the plant and it even shows the lowest
DDC percentage. Equivalently, road or route conditions have absolutely no statistical
significance when trying to relate it to DCC and so this variable is totally discarded from
the hypothesis.
The statistical model also discarded “Feedlot Origin” and so we can conclude that DCC
has no significant relation to any specific feedlot which all of them have similar if not the
same type of handling facilities. Live weight, carcass weight and “overweight” or heavy
cattle also did not have any relation with the dependent variable DCC.
To sum up, the results obtained in this research not only unveil several hypotheses
about transportation and its relation to stress in cattle that might cause dark cutter
carcasses but also gives the opportunity to research further than these variables which
can be factors such as weather conditions and year seasons which can also be a
related to DCC. Other variable that can be studied further is shipping time and kill time
where the shipping time is recorded at the feedlot as what time it left the feedlot and at
what time was it killed by the slaughter house or packing plant and so this can be
another hypothesis that can have great impact on dark cutter carcass percentage

16. because this is time where cattle suffer of physical stress that can be dehydration,
hunger and even fatigue (Johnson, 2015).
References
- Brett Littler, J. H. (2001, June 13). Preventing glycogen loss. Retrieved October
13, 2015, from Primary Industries Agriculture:
http://www.dpi.nsw.gov.au/agriculture/livestock/beef/market/publications/dcb-
cattle-mgt
- Frost, J. (2014, April 17). How to Correctly Interpret P values. Retrieved October
4, 2015, from Blog.minitab.com: blog.minitabl.com/blog/adventures-in-
statistics/how-to-correctly-interpret-p-values
- Johnson, G. (2015, October 15). General Manager Coronado Feeders. (P.
Garcia, Interviewer)
- O'Diam, D. (2010, August 2010). Cattlemen can help prevent dark cutters.
Retrieved October 03, 2015, from Beefmagazine.com:
beefmagazine.com/cowcalfweekly/0920-cattlemen-help-prevent-cutters
- Onec, A. (2004, March 4). Dark Cutting Incidence in Holstein Friesian, Brown
Swiss and Eastern Anatolian Red Cattle Slaughtered under Turkish Commercial
slaughter conditions. Retrieved October 10, 2015, from citeseersx.ist.psu.edu:
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.430.2952&rep=rep1&ty
pe=pdf

17. - Voon, T. (1992). Proceedings of an Australian Workshop on Dark Cutting in
Cattle and Sheep. Australia: AMLRDC.
- Zaiontz, C. (2013, January 15). Real Statistics Using Excel. Retrieved October 4,
2015, from real-statistics.com: www.real-statistics.com/multiple-
regression/multiple-regression-analysis/multiple-regression-analysis-excel/
Figures and Charts
Chart 1
Chart 1. Percentages of Dark cutter in relation with sex or gender.
0.00%
2.00%
4.00%
6.00%
Heifer
Holstein
Steer
Total
1.23%
4.16%
1.39% 1.57%
Sex or Type
Percentage of Dark Cutter per Sex
Heifer
Holstein
Steer
Total

18. Chart 2
Chart 2. Percentage of DCC in relation with feedlot origin.
Chart 3
Chart 3. Fitted plot line for % of DDC in relation with sex or gender.
0.00%
0.50%
1.00%
1.50%
2.00% 1.53%
1.45%
1.17%
1.94%
1.77%
Percentage of Dark Cutters
Cimarron
Coronado
Grant County
Hartley
XIT
-2.00%
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
0 0.5 1 1.5 2 2.5 3 3.5
Dark%
Sex
Sex Line Fit Plot
Dark %
Predicted Dark %

19. Chart 4
Chart 4. Number of cattle sent to slaughter house in relation to DCC % and the fitted
plot line of predicted DCC%.
Chart 5
Chart 5. Dark Cutter quantity in relation with days of feed.
-2.00%
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
0 5000 10000 15000 20000
Dark%
Number of Cattle
Number of Cattle Line Fit Plot
Dark %
Predicted Dark %
0
100
200
300
400
500
600
700
800
900
1000
0
135
142
146
152
156
160
164
168
172
179
184
189
203
274
299
365
386
DarkCutters
Days of Feed
Dark Cutters Vs. Days of Feed
Total

20. Table 1
Regression Statistics
Multiple R 0.675526576
R Square 0.456336155
Adjusted R Square 0.429887644
Standard Error 0.012527749
Observations 195
ANOVA
df SS MS F Significance F
Regression 9 0.024370938 0.002708 17.25376 1.57547E-20
Residual 185 0.029034732 0.000157
Total 194 0.05340567
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept -0.042182351 0.010027286 -4.20676 4.03E-05 -0.061964882
-
0.022399821
-
0.061964882
-
0.022399821
Feedlot 0.001327526 0.001070002 1.240676 0.216297 -0.000783449 0.003438501
-
0.000783449 0.003438501
Sex 0.012508201 0.003151899 3.968465 0.000103 0.006289915 0.018726487 0.006289915 0.018726487
DOF 8.67108E-05 2.28747E-05 3.790693 0.000203 4.15821E-05 0.00013184 4.15821E-05 0.00013184
Number of Cattle 8.18392E-07 4.01365E-07 2.039024 0.04287 2.6552E-08 1.61023E-06 2.6552E-08 1.61023E-06
Live Weight -5.36733E-05 8.5029E-05 -0.63123 0.528666 -0.000221424 0.000114078
-
0.000221424 0.000114078
Carcass Weight 9.59653E-05 0.000133156 0.720699 0.472004 -0.000166734 0.000358664
-
0.000166734 0.000358664
Heavies 0.044282518 0.016858774 2.626675 0.009345 0.011022349 0.077542686 0.011022349 0.077542686
Distance to Plant -4.4794E-05 2.44618E-05 -1.83119 0.068681 -9.30539E-05 3.46585E-06 -9.30539E-05 3.46585E-06
Road conditions 0.001332419 0.001988224 0.670155 0.503594 -0.002590088 0.005254925
-
0.002590088 0.005254925
Table 1. First multiple regression model using 9 independent variables.

21. Table 2
Regression Statistics
Multiple R 0.64704335
R Square 0.418665097
Adjusted R Square 0.406426468
Standard Error 0.01278292
Observations 195
ANOVA
df SS MS F
Significance
F
Regression 4 0.02235909 0.005589773 34.20849503 1.7017E-21
Residual 190 0.03104658 0.000163403
Total 194 0.05340567
Coefficients
Standard
Error t Stat P-value Lower 95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept -0.023091955 0.005524121
-
4.180204251 4.44072E-05 -0.03398844 -0.0122 -0.03399 -0.0122
Sex 0.007593182 0.001997447 3.801443517 0.000193682 0.003653162 0.011533 0.003653 0.011533
DOF 0.000111445 1.07087E-05 10.40702533 2.32698E-20 9.03221E-05 0.000133 9.03E-05 0.000133
Number of Cattle 7.76855E-07 4.01081E-07 1.936899577 0.05424073 -1.429E-08 1.57E-06 -1.4E-08 1.57E-06
Heavies 0.027838095 0.01598169 1.741874222 0.083148762 -0.00368624 0.059362 -0.00369 0.059362
Table 2. 2nd
Multiple regression model after discarding 5 independent variables.

22. Table 3.
Regression Statistics
Multiple R 0.63982944
R Square 0.409381712
Adjusted R Square 0.400104985
Standard Error 0.012850808
Observations 195
ANOVA
df SS MS F
Significance
F
Regression 3 0.021863305 0.007287768 44.12997281 1.03095E-21
Residual 191 0.031542366 0.000165143
Total 194 0.05340567
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept -0.018776342 0.004963586
-
3.782817843 0.000207362
-
0.028566828
-
0.008985857
-
0.028566828
-
0.008985857
Sex 0.005995613 0.001783859 3.361035054 0.00093778 0.002477019 0.009514207 0.002477019 0.009514207
Number of Cattle 9.47113E-07 3.91055E-07 2.421942777 0.016372036 1.75772E-07 1.71845E-06 1.75772E-07 1.71845E-06
DOF 0.000111865 1.07628E-05 10.39366509 2.43342E-20 9.06357E-05 0.000133094 9.06357E-05 0.000133094
Table 3. Third multiple regression model using the last three independent variables.