**APPENDIX B**

**METHODOLOGY FOR ESTIMATING OUTSIDE PLANT COSTS**

**I. Introduction**

Section II in this appendix explains in specific detail the regression
equations and the adjustments to these equations for estimating the
input values adopted in this Order for structure and cable costs. These
regression equations and these adjustments are set forth in this
appendix on the following tables: Table I., labeled \"Regression
Equations Derived From RUS Data For Estimating Cable And Structure
Costs;\" Table II., labeled \"Adjustments To Regression Equations
Derived From RUS Data For Estimating Cable And Structure Costs;\" and
Table III., labeled \"Regression Equations Derived From Non-Rural LEC
Data For Estimating Cable Costs.\"

Section III illustrates use of the Huber methodology to derive
reasonable estimates for 24-gauge aerial copper cable costs.[^1] This
illustration uses the diagram in this appendix labeled \"Scatter Diagram
Of 24-Gauge Aerial Copper Cable Cost And Size With The Huber Regression
Line.\" This diagram shows RUS cable cost observations for 24-gauge
aerial copper cable and the regression line fit to these observations by
using the Huber methodology. It also uses the frequency distribution in
this appendix set forth on Table IV., labeled \"Frequency Distribution
Of Huber Weights For 24-gauge Aerial Copper Cable Cost.\" This frequency
distribution shows the number of aerial copper cable observations to
which the Huber methodology assigns particular weights.

Section IV demonstrates that the Huber methodology generally does not
have a statistically significant impact on the level of the material
costs reflected in the cable cost estimates adopted in this Order. This
finding provides support for the large LEC buying power adjustment
reflected in these estimates. This finding is supported by the
statistical information set forth in this appendix on Table V., labeled
\"Analysis Of Coefficient For Cable Size Variable In The Huber
Regression Equations.\"

**II. Regression Equations For Estimating Outside Plant Structure
Costs**

**A. Regression Equations Derived From RUS Data For Estimating Cable And
Structure Costs**

Table I, labeled \"Regression Equations Derived From RUS Data For
Estimating Cable And Structure Costs,\" sets forth the regression
equations adopted in this Order for estimating the cost of: (1) 24-gauge
aerial copper cable; (2) 24-gauge underground copper cable; (3) 24-gauge
buried copper cable and structure; (4) aerial fiber cable; (5)
underground fiber cable; (6) buried fiber cable and structure; (7)
poles; and (8) underground structure. These regression equations, other
than the equations for poles and underground structure, are developed by
revising the regression equations for cable and structure costs
developed by Gabel and Kennedy in the NRRI Study.[^2] The regression
equations adopted in this Order, other than the equation for poles, are
estimated by using the Huber methodology with RUS data. The regression
equations in the NRRI Study[^3] are developed by using ordinary least
squares (OLS) with RUS data.[^4] The regression equation for poles
adopted in this Order is the regression equation for poles in the NRRI
Study. The regression equation adopted in this Order for poles is not
estimated by using the Huber methodology because the Huber regression
for poles is not statistically significant at the five percent level.

Column A identifies, by type of cost, the regression equations adopted
in this Order. Set forth in columns B, D, F, H, J, L, and N are the
intercepts and the slope coefficients reflected in these regression
equations. The coefficients set forth in these columns for these
regression equations are for the variables that indicate the size of a
cable,[^5] density zone,[^6] soil surface texture,[^7] bedrock type,[^8]
combined bedrock and soil type,[^9] and the presence of a high water
table.[^10] Columns C, E, G, I, K, M, and O display the t-statistics
used to measure the statistical significance of these intercepts and
coefficients. Column P displays the F-statistics used to measure the
statistical significance of these regression equations. Column O
displays the number of observations in the data used to estimate these
equations.

The coefficients for the variable that indicates the size of the cable
in the regression equations for 24-gauge copper cable cost and fiber
cable cost do not reflect the adjustments adopted in this Order for
large LEC buying power. The intercepts and the coefficients in these
equations do not reflect splicing and LEC engineering costs because
these costs are not reflected in the RUS data from which these equations
are derived. The intercepts and the coefficients for the water, soil,
and bedrock indicator variables in the regression equations for
structure costs do not reflect LEC engineering costs because these costs
are not reflected in the RUS data from which these equations are
derived. The intercept and the coefficients for the water, soil, and
bedrock indicator variables in the regression equation for pole costs do
not reflect costs for anchors, guys, and other pole-related items
because these costs are not reflected in the RUS data from which this
equation is derived.

**B. Adjustments To Regression Equations Derived From RUS Data** **For
Estimating Cable And Structure Costs**

Table II, labeled \"Adjustments To Regression Equations Derived From RUS
Data For Estimating Cable And Structure Costs,\" sets forth adjustments
to the regression equations adopted in this Order for estimating costs
for 24-gauge copper cable, fiber cable, and structure. The equations
that reflect these adjustments, *i.e*., the adjusted equations, are used
for estimating the cost of: (1) 24-gauge aerial copper cable; (2)
24-gauge underground copper cable; (3) 24-gauge buried copper cable; (4)
aerial fiber cable; (5) underground fiber cable; (6) buried fiber cable;
(7) aerial structure; (8) underground structure; and (9) buried
structure.

Column A identifies, by type of cost, the adjusted equations used to
derive the cable and structure costs adopted as input values in this
Order.

Column B displays the intercepts in the adjusted equations. In the
adjusted equations for the cost of aerial and underground 24-gauge
copper cable, fiber cable, and structure, the intercepts are those in
the regression equations for these costs. The intercepts in the adjusted
equations for 24-gauge buried copper cable and buried fiber cable
represent the fixed cost of buried copper cable and the fixed cost of
buried fiber cable, respectively. The intercepts in the regression
equations for 24-gauge buried copper cable and structure and buried
fiber cable and structure represent the fixed cost of buried copper
cable and structure and the fixed cost of buried fiber cable and
structure, respectively, in density zone 1. The fixed cost of 24-gauge
buried copper cable used as the intercept in the adjusted equation for
24-gauge buried copper cable, approximately \$.46 per foot, is derived
by subtracting from the intercept in the regression equation for
24-gauge buried copper cable and structure, approximately \$1.16 per
foot, the value of the fixed cost for buried structure in density zone 1
adopted in this Order, \$.70 per foot. The fixed cost of fiber cable
used as the intercept in the adjusted equation for fiber cable,
approximately \$.47 per foot, is derived by subtracting from the
intercept in the regression equation for buried fiber cable and
structure, approximately \$1.17 per foot, the \$.70 per foot fixed cost
adopted for buried structure in density zone 1. The intercept in the
adjusted equation for buried structure represents the fixed cost of
buried structure in density zone 1. The fixed cost of buried structure
in density zone 1 used as the intercept in the adjusted equation for
buried structure is the \$.70 per foot fixed cost adopted for buried
structure in density zone 1.

Column C displays the coefficients for the cable size variable in the
adjusted 24-gauge copper and fiber cable equations. In the adjusted
equations for the cost of aerial and underground 24-gauge copper cable
and fiber cable, the coefficients for the cable size variable are those
for this variable in the regression equations for these costs. In the
adjusted 24-gauge copper cable equation, the coefficient for the cable
size variable is the coefficient for this variable in the 24-gauge
buried cable and structure regression equation. In the adjusted 24-gauge
fiber cable equation, the coefficient for the cable size variable is the
coefficient for this variable in the buried fiber cable and structure
regression equation.

Column D displays the large LEC buying power adjustment factors. These
factors are applied to the coefficients for the cable size variable in
the adjusted copper and fiber cable equations. Column E displays the
values of the coefficients for these cable size variables in these
equations, as adjusted to reflect large LEC buying power.

Columns F, G, and H display the coefficients for the density zone,
bedrock indicator, and combined soil and bedrock indicator variables in
the adjusted structure equations. In the adjusted equations for the cost
of aerial and underground structure, these coefficients are those for
these variables in the regression equations for these costs. In the
adjusted buried structure equation, these coefficients are those for
these variables in the 24-gauge buried copper cable and structure
regression equation. The coefficients for the water and soil indicator
variables in the structure regression equations are not reflected in the
adjusted equations because the value for these variables is set equal to
zero to estimate structure costs used as input values.

Column I displays the loading factors used to reflect splicing costs in
the cable cost estimates for 24-gauge copper cable and fiber cable.

Column J displays the loading factor used to reflect LEC engineering
costs in the structure cost estimates.

Column K displays the flat dollar loading used to reflect LEC
engineering costs in the cable cost estimates for 24-gauge copper cable
and fiber cable.

Column L displays the adjusted equations used to estimate costs for
aerial, underground, and buried 24-gauge copper and fiber cable, buried
and underground structure, and poles.

Columns M-O display adjustments to the adjusted pole equation. These
adjustments add to the cost of poles the costs for anchors, guys, and
other pole-related items, including LEC engineering costs associated
with these additional items, and convert per pole costs, inclusive of
costs for anchors, guys, and other pole-related items, *i.e.*, aerial
structure costs, to per foot costs. Column M displays the costs for
anchors, guys, and other pole-related items for density zones 1 and 2
(\$32.98 per pole), density zones 3-7 (\$49.96 per pole), and density
zones 8 and 9 (\$60.47 per pole).[^11] Column N displays the loading
factor used to reflect LEC engineering costs in the costs for anchors,
guys, and other pole-related items. Column O displays the distance
between poles used to calculate aerial structure cost per foot for
density zones 1 and 2 (250 feet per pole), density zones 3 and 4 (200
feet per pole), density zones 5 and 6 (175 feet per pole), and density
zones 7-9 (150 feet per pole).

Column P displays the adjusted equation used to estimate aerial
structure cost per foot, including poles, anchors, guys, and other
pole-related items.

We illustrate how the adjusted equations are used to develop the input
values adopted in this Order by calculating the cost for a 100-pair
24-gauge aerial copper cable. Column L sets forth the adjusted equation
used to develop the input values adopted in this Order for 24-gauge
aerial copper cable. The adjusted equation set forth in column L for
24-gauge aerial copper cable is as follows:[^12]

A1 = (B1 + (E1)(# of Prs.))(1 + I1) + K1

where:

A1 = 24-gauge aerial copper cable cost per foot;

B1 = the intercept for 24-gauge aerial copper cable in dollars per foot;

E1 = the coefficient, adjusted for buying power, in dollars per pair per
foot, for the variable that represents the number of 24-gauge aerial
copper cable pairs;

I1 = the splicing loading for 24-gauge aerial copper cable expressed as
a percentage;

K1 = the LEC engineering loading for 24-gauge aerial copper cable in
dollars per foot.

By substituting into the above equation for 24-gauge aerial copper cable
the values from Table II for the intercept, adjusted coefficient for the
cable size variable, splicing loading, and LEC engineering loading, and
the number of cable pairs in this example, 100, we obtain the following
estimate for the cost of a 100-pair 24-gauge aerial copper cable:

A1 = (1.014907 + (.008329)(100))(1 + .094) + .19

= (1.014907 + .8329)(1.094) + .19

= (1.847807)(1.094) + .19

= 2.021501 + .19

= \$2.21 per foot.

We adopt this estimate as the input in the model for the cost of a
100-pair 24-gauge aerial copper cable.

**C. Regression Equations Derived From Non-Rural LEC Data** **For
Estimating Cable Costs**

We adopt in this Order a methodology to derive estimates of 26-gauge
copper cable costs from 24-gauge copper cable costs. We first estimate
by using the Huber methodology with RUS data the cost for 24-gauge
copper cable for each cable size.[^13] We then obtain by using the Huber
methodology with certain non-rural LEC data estimates of the cost for
24-gauge copper cable and 26-gauge copper cable for each cable
size.[^14] We next divide the 24-gauge copper cable cost estimate
derived from the non-rural LEC data into the estimate for 26-gauge
copper cable cost derived from these data for each cable size. The
result is a ratio of 26-gauge copper cable cost to 24-gauge copper cable
cost for each cable size.[^15] Finally, we multiply this ratio by the
estimate of the cost for 24-gauge copper cable derived from the RUS data
to obtain the cost for 26-gauge copper cable for each cable size.[^16]
We adopt these estimates as inputs for 26-gauge copper cable costs in
the SM.

Table III, labeled \"Regression Equations Derived From Non-Rural LEC
Data For Estimating Cable Costs,\" sets forth regression equations
derived from the non-rural LEC data for: (1) 24-gauge aerial copper
cable; (2) 24-gauge underground copper cable; (3) 24-gauge buried copper
cable; (4) 26-gauge aerial copper cable; (5) 26-gauge underground copper
cable; and (6) 26-gauge buried copper cable. We use these regression
equations to develop the ratios of 26-gauge copper cable costs to
24-gauge copper cable costs used to derive the cost for 26-gauge copper
cable. Column A identifies these regression equations by type of copper
cable cost. Set forth in columns B and D are the intercepts and the
slope coefficients reflected in these regression equations. Columns C
and E display the t-statistics used to measure the statistical
significance of these intercepts and coefficients. Column F displays the
F-statistics used to measure the statistical significance of these
regression equations. Column G displays the number of observations in
the data used to estimate these equations. Column H shows the regression
equations derived from the non-rural LEC data for estimating costs for
24-gauge and 26-gauge copper cable.

These regression equations are derived from *ex parte* data on 24-gauge
and 26-gauge copper cable costs submitted by Sprint and Aliant, data on
these cable costs submitted by BellSouth with its comments,[^17] and the
BCPM default values for these cable costs. These regression equations
are developed by using the Huber methodology. Using the Huber
methodology with non-rural LEC data to estimate cable costs for 24- and
26-gauge copper cable costs is consistent with use of this methodology
to estimate 24-gauge copper cable costs from the RUS data. The
regression equations derived from non-rural LEC data use the number of
copper cable pairs as the sole independent variable. Using the number of
copper cable pairs as the sole independent variable in these regression
equations is consistent with using this variable as the sole independent
variable in the regression equations for 24-gauge copper cable costs
estimated from the RUS data.

In this Order, we find it reasonable to rely on the non-rural LEC data
for calculating the ratio of the cost for 24-gauge copper cable to that
for 26-gauge copper cable but not for calculating the absolute cost for
24-gauge copper cable and 26-gauge copper cable.[^18] As discussed in
this Order, we find that the non-rural LEC data is not a reliable
measure of absolute costs. Notwithstanding this finding, we conclude
that it is reasonable to use the non-rural LEC data to determine the
relative value of the cost for 24-gauge copper cable to that for
26-gauge copper cable. We find that it is reasonable to conclude that
each LEC used the same methodology to develop both 24-gauge and 26-gauge
copper cable costs. Accordingly, any bias in the costs for 24-gauge and
26-gauge copper cable that results from using a given methodology is
likely to be in the same direction and of a similar magnitude. As a
consequence, cost estimates for 24-gauge and 26-gauge copper cable for
each cable size developed from non-rural LEC data by using the Huber
methodology are likely to be biased by approximately the same factor.
The ratios of these estimates are not likely to be affected
significantly because the bias in one estimate approximately cancels the
bias in the other estimate when the ratio is calculated.

We illustrate how we calculate the costs that we adopt in this Order for
26-gauge copper cable by calculating the cost for a 100-pair 26-gauge
aerial copper cable. As explained above, we derive a ratio of 26-gauge
copper cable cost to 24-gauge copper cable cost from non-rural LEC data
to obtain costs for 26-gauge copper cable. To calculate this ratio for a
100-pair aerial copper cable, we estimate separately from non-rural LEC
data the cost for a 100-pair 24-gauge aerial copper cable and a 100-pair
26-gauge aerial copper cable. We first estimate the numerator of this
ratio, *i.e*., the cost for a 100-pair 24-gauge aerial copper cable.
Column H shows the regression equation derived from non-rural LEC data
for estimating the cost for 24-gauge aerial copper cable. The regression
equation set forth in column H for 24-gauge aerial copper cable is as
follows:

A1 = B1 + (D1)(# of Pairs)

where:

A1 = 24-gauge aerial copper cable cost per foot;

B1 = the intercept for 24-gauge aerial copper cable in dollars per foot;

D1 = the coefficient in dollars per pair per foot for the variable that
represents the number of 24-gauge aerial copper cable pairs.

By substituting into the above equation for 24-gauge aerial copper cable
the values from Table III for the intercept and the coefficient for the
cable size variable, and the number of cable pairs in this example, 100,
we obtain the following result for the cost of a 100-pair 24-gauge
aerial copper cable:

A1 = 2.1548 + (.012393)(100)

= 2.1548 + 1.2393

= \$3.39 per foot.

We next estimate the denominator for the ratio of 26-gauge aerial copper
cable cost to 24-gauge aerial copper cable cost for a 100-pair aerial
copper cable, *i.e*., the 26-gauge aerial copper cable cost for a
100-pair cable. Column H shows the regression equation derived from
non-rural LEC data for estimating the cost for 26-gauge aerial copper
cable. The regression equation set forth in column H for 26-gauge aerial
copper cable is as follows:

A4 = B4 + (D4)(# of Pairs)

where:

A4 = 26-gauge aerial copper cable cost per foot;

B4 = the intercept for 26-gauge aerial copper cable in dollars per foot;

D4 = the coefficient in dollars per pair per foot for the variable that
represents the number of 26-gauge aerial copper cable pairs.

By substituting into the above equation for 26-gauge aerial copper cable
the values from Table III for the intercept and the coefficient for the
cable size variable, and the number of cable pairs in this example, 100,
we obtain the following result for the cost of a 100-pair 26-gauge
aerial copper cable:

A4 = 2.385108 + (.008721)(100)

= 2.385108 + .8721

= \$3.26 per foot.

We next calculate the ratio of 26-gauge copper cable cost to 24-gauge
copper cable cost for a 100-pair cable. The ratio of 26-gauge copper
cable cost to 24-gauge copper cable cost for a 100-pair cable is .96
(\$3.26 per foot divided by \$3.39 per foot).

Finally, we multiply this ratio by the estimate of the 24-gauge copper
cable cost for a 100-pair cable derived from the RUS data, \$2.21 per
foot, to obtain the cost for a 100-pair 26-gauge copper cable, \$2.12
per foot. We adopt this estimate as the input in the SM for the cost of
a 100-pair 26-gauge aerial copper cable.

**III. Huber Methodology**

We find in this Order that it is reasonable to use the Huber methodology
to develop input values for cable and structure costs. The structure and
cable cost inputs used in the SM should reflect those that are typical
for cable and structure for a number of different density and terrain
conditions. Otherwise, the model may substantially overestimate or
underestimate the cost of building a network. The Huber methodology
produces estimates of costs that are typical for cable and structure by
assigning zero or less than full weight to cable and structure cost
observations that have extremely high or extremely low values. At the
same time, it assigns full or nearly full weight to closely clustered
cable and structure cost observations.

Use of the Huber methodology to derive reasonable estimates from RUS
data is illustrated for aerial copper cable cost on the diagram labeled
\"Scatter Diagram Of 24-Gauge Aerial Copper Cable Cost And Size With The
Huber Regression Line\" and on the frequency distribution set forth on
Table IV, labeled \"Frequency Distribution Of Huber Weights For 24-Gauge
Aerial Copper Cable Cost.\" The scatter diagram shows RUS cable cost
data points representing combinations of aerial copper cable costs
(measured on the vertical axis in dollars per foot) and cable size
(measured on the horizontal axis by number of pairs). It also shows the
regression line that the Huber methodology fits to these data points.
The algebraic expression of this line explains or predicts the effects
on aerial copper cable costs of changes in cable size.[^19] The
observations to which Huber assigns a weight that is less than .47 are
identified with an "o"; those to which it assigns a weight that is
greater than .47 are identified with an "\*". The frequency distribution
shows the number of aerial copper cable observations to which the Huber
methodology assigns particular weights.

The scatter diagram and the frequency distribution demonstrate that the
aerial copper cable estimates derived by using the Huber methodology
with RUS data reflect most of the information contained in nearly all of
the observations. As depicted on the scatter diagram, the majority of
the aerial copper cable observations are clustered closely around the
regression line. These are the observations to which Huber assigns the
greatest weight when fitting the regression line to the data. As the
frequency distribution shows, approximately 82 percent of the aerial
copper cable observations is assigned a weight of at least .8. This
large majority of closely clustered observations clearly represents
typical cable costs. The minority of the aerial copper cable
observations lies a considerable distance from the regression line.
These are the observations to which Huber assigns the least weight when
fitting the regression line to the data. As the frequency distribution
shows, approximately 18 percent of the observations is assigned a weight
of at less than .8. This small minority of observations comprises
extremely high and extremely low values that do not represent typical
cable costs. The scatter diagram also shows that some of the
observations that receive a relatively small weight lie a substantial
distance above the regression line while others that receive such weight
lie a substantial distance below this line. This demonstrates that the
Huber methodology excludes or assigns less than full weight to data
outliers without regard to whether these are high or low cost
observations.

**IV. Analysis Of Coefficient For Cable Size Variable In The Huber
Regression Equations**

In this Order, we derive equations to estimate the non-rural LECs\'
labor and material cost for cable. We derive these equations by: (1)
deriving regression equations by using the Huber methodology with RUS
cable cost data that reflect labor and material costs; and (2) adjusting
downward the coefficient for the variable that represents cable size in
these regression equations to reflect the buying power of large LECs in
comparison to RUS companies. The coefficient for the variable that
represents cable size represents the additional cost for an additional
pair of cable and therefore represents cable material costs. The
adjustment to this coefficient is based on the difference between the
average cable material prices that Bell Atlantic and the RUS companies
pay for different cable sizes. The RUS companies\' average cable
material prices are calculated by using unweighted RUS data. Conversely,
the Huber methodology used to estimate the regression equations assigns
zero or less than full weight to data points that have extremely high or
extremely low values. Below we demonstrate that the Huber methodology
generally does not have a statistically significant impact on the level
of material costs reflected in the cable cost estimates. That is, in
general, there is not a statistically significant difference between the
value of the coefficient for the cable size variable in the regression
equations estimated by using the Huber methodology and the value of this
coefficient in the regression equations developed in the NRRI Study by
using OLS. Accordingly, the buying power adjustment for material is
based on averages of RUS companies\' cable material prices calculated by
using unweighted RUS data.

Table V, labeled \"Analysis Of Coefficient For Cable Size Variable In
The Huber Regression Equations,\" displays the values of the coefficient
for the cable size variable in the regression equations estimated from
RUS data by using the Huber methodology in this Order and the 95 percent
confidence interval surrounding the value of this coefficient in these
equations in the NRRI Study estimated from these data by using OLS.
Except for 24-gauge buried copper cable, the value of the this
coefficient estimated by using the Huber methodology lies inside the 95
percent confidence interval surrounding the value of this coefficient in
these equations in the NRRI Study estimated from these data by using
OLS. That is, except for 24-gauge buried copper cable, the value of the
cable size coefficient estimated by using the Huber methodology lies
within an interval that contains with 95 percent certainty the true
value of the OLS cable size coefficient.[^20] This statistical evidence
supports a finding that the Huber methodology does not have a
statistically significant impact on the level of the material costs
reflected in the cable cost estimates derived by using this
methodology.[^21] The cable size coefficient obtained by using the Huber
methodology for buried copper cable lies outside the 95 percent
confidence interval associated with the cable size coefficient obtained
by using OLS for buried copper cable. This supports a finding that the
Huber methodology does have a statistically significant impact on the
level of the material costs reflected in the buried copper cable cost
estimates.[^22]

[^1]:     ^?^ We used Stata Statistical Software: Release 5 (Stata) to
    perform the calculations needed to estimate the regression equations
    adopted in this Order for cable and structure costs. Stata has a
    robust regression methodology that uses formulas developed by P. J.
    Huber, R. D. Cook, A. E. Beaton and J. W. Tukey. We used this
    methodology to estimate the regression equations for cable and
    structure costs. We refer to this regression methodology as the
    Huber methodology. *See* StataCorp., *Stata Reference Manual,
    Release 5*, vol. 3, P-Z, 168-173 (College Station, TX: Stata Press,
    1997).

[^2]:     ^?^ There is no regression equation for underground structure
    in the NRRI Study. The regression equation for underground structure
    adopted in this Order was developed after the NRRI Study was
    published.

[^3]:     ^?^ These regression equations are set forth in the NRRI Study
    at 58, Table 2-16 (24-gauge aerial copper cable cost); 60, Table
    2-19 (24-gauge underground copper cable cost); 41, Table 2-7
    (24-gauge buried copper cable and structure cost); 59, Table 2-18
    (aerial fiber cable cost); 61, Table 2-20 (underground fiber cable
    cost); 49, Table 2-10 (buried fiber cable and structure cost); 52,
    Table 2-12 (pole cost).

[^4]:     ^?^ None of the regression equations adopted in this Order has
    a variable that indicates the presence of a second cable at the same
    location. The regression equations in the NRRI Study, other than the
    equation for poles, have a variable that indicates the presence of a
    second cable at the same location. The regression equations adopted
    in this Order for poles, underground structure, buried copper cable
    and structure, and buried fiber cable and structure have a variable
    that indicates the presence of a high water table. The regression
    equation in the NRRI Study for poles and buried fiber cable and
    structure have a variable that indicates the presence of a high
    water table. The regression equation in the NRRI Study for buried
    copper cable and structure does not have this variable.

[^5]:     ^?^ The cable size variable is used in the regression
    equations for estimating the cost of 24-gauge aerial copper cable,
    24-gauge underground copper cable, 24-gauge buried copper cable and
    structure, aerial fiber cable, underground fiber cable, and buried
    fiber cable and structure. It has values that equal the number of
    copper cable pairs in the 24-gauge copper cable regression equations
    and the number of fiber cable strands in the fiber cable regression
    equations.

[^6]:     ^?^ The density zone variable is used in the regression
    equations for 24-gauge buried copper cable and structure cost and
    buried fiber cable and structure cost. It has a value of 1 if a
    buried cable is installed in density zone 2; 0 if a buried cable is
    installed in density zone 1.

[^7]:     ^?^ The variable that indicates soil surface texture is used
    in the regression equation for pole cost. It has values that range
    from 0 for normal soil, to 1 for soft soil, to 3 for hard soil.
    *See* NRRI Study at 16 and 46, Table 2-8.

[^8]:     ^?^ The variable that indicates bedrock type is used in the
    regression equation for pole cost. It has values that range from 0
    for normal rock, to 1 for soft rock, to 2 for hard rock. These
    bedrock types are at a depth of 48 inches. *See* NRRI Study at 16
    and 44, Table 2-8.

[^9]:     ^?^ The combined bedrock and soil type variable is used in the
    regression equations for 24-gauge buried copper cable and structure
    cost, buried fiber cable and structure cost, and underground
    structure cost. It is the sum of separate variables for surface soil
    texture and bedrock type at a depth of 36 inches. *See* NRRI Study
    at 45, Table 2-8. The value of the variable that indicates surface
    soil texture ranges from 0 for normal soil, to 1 for soft soil, to 3
    for hard soil. *See* NRRI Study at 16 and 46, Table 2-8. The value
    of the variable that indicates bedrock type ranges from 0 for normal
    rock, to 1 for soft rock, to 2 for hard rock at a depth of 36
    inches. *See* NRRI Study at 16 and 44, Table 2-8. Accordingly, the
    value of the variable for the combined bedrock and soil type
    indicator ranges from 0 where there are normal surface soil texture
    and normal bedrock at a depth of 36 inches to 5 where there are hard
    surface soil texture and hard bedrock at a depth of 36 inches.

[^10]:     ^?^ The variable that indicates the presence of a high water
    table is used in the regression equations for 24-gauge buried copper
    cable and structure cost, buried fiber cable and structure cost,
    pole cost, and underground structure cost. It has values that range
    from 0 for the absence of a high water table, to 1 for the presence
    of a high water table. This variable assumes that a high water table
    has a depth of five feet or fewer. *See* NRRI Study at 12, 16 and
    46, Table 2-8.

[^11]:     ^?^ These costs for anchors, guys, and other pole-related
    items are based on the costs for these items in rural, suburban, and
    urban areas derived by Gabel and Kennedy in the NRRI Study. *See*
    NRRI Study at 51, Table 2-11.

[^12]:     ^?^ Set forth on Table II in specific columns and on specific
    rows are the values for the intercepts, coefficients (including the
    adjusted coefficients for the cable size variable), splicing
    loadings, and LEC engineering loadings reflected in the adjusted
    equations used to estimate structure and cable costs. The specific
    column is identified by a letter. The specific row is identified by
    a number. B1, for example, refers to the value set forth in column B
    on row 1.

[^13]:     ^?^ More technically, we obtain from these RUS data an
    estimate of the expected value of the cost for 24-gauge copper cable
    for each cable size.

[^14]:     ^?^ More technically, we obtain from these non-rural LEC data
    estimates of the expected value of the cost for 24-gauge copper
    cable and 26-gauge copper cable for each cable size.

[^15]:     ^?^ More technically, we obtain from these non-rural LEC data
    a ratio of an estimate of the expected value for 26-gauge copper
    cable cost to an estimate of the expected value for 24-gauge cable
    cost for each cable size.

[^16]:     ^?^ More technically, we obtain an estimate of the expected
    value for 26-gauge copper cable cost.

[^17]:     ^?^ *See* BellSouth *Inputs Further Notice* comments,
    Exhibit 1. BellSouth submitted separate copper cable costs for nine
    study areas. We calculate the weighted average of these copper cable
    costs for each cable size based on the number of access lines in
    each study area. We include this weighted average cable cost for
    BellSouth for each cable size in the non-rural LEC data from which
    we derive 24-gauge and 26-gauge copper cable costs. By using a
    weighted average, the regression equations derived from the
    non-rural LEC data do not reflect a disproportionate number of
    observations for BellSouth compared to the number of observations
    for the other non-rural LECs for which costs are reflected in these
    data. The cable costs reflected in the data for these other LECs are
    either company-wide costs or an average for multiple study areas. In
    either case, there is a single observation for each of these
    companies for a given cable size for 24-gauge and 26-gauge copper
    cable cost. By reflecting the weighted average cost for BellSouth in
    the data, there is only one observation for BellSouth for a given
    cable size for 24-gauge and 26-gauge copper cable cost.

[^18]:     ^?^ We discuss the rationale for using non-rural LEC data to
    calculate relative copper cable costs, but not absolute copper cable
    costs, in this Order, section V.C.4.b.

[^19]:     ^?^ The algebraic expression of the regression line for
    24-gauge aerial copper cable estimated from RUS data by using the
    Huber methodology is as follows:

    24-gauge aerial copper cable cost per foot = 1.014907 +
    (.009822)(number of pairs).

    In this regression equation, 24-gauge aerial copper cable cost is
    the dependent variable for which a value is measured along the
    vertical axis. The number of pairs is the independent variable for
    which a value is measured along the horizontal axis. The value
    1.014907 is the intercept of the regression line. It is the point at
    which the regression line hits the vertical axis. It measures the
    fixed cost for 24-gauge aerial copper cable. The value .009822 is
    the slope coefficient of the regression line. It is the slope of the
    regression line. It measures the additional cost for one additional
    pair of 24-gauge aerial copper cable.

[^20]:     ^?^ Strictly speaking, over a large number of different
    samples, 95 percent of the confidence intervals associated with
    different OLS estimates of the cable size coefficient are expected
    to contain the true value of the OLS cable size coefficient.

[^21]:     ^?^ In this Order, we affirm the tentative decision in the
    *Inputs Further Notice* to use conservatively the lower of the
    buying power adjustments for aerial and underground copper cable
    material costs as the adjustment for buried copper cable material
    costs because the Huber methodology does have a statistically
    significant impact on the buried copper cable material costs
    reflected in the buried copper cable cost estimates. *See* this
    Order, section V.C.4.b.

[^22]:     ^?^ The specifications for the copper and fiber cable
    regression equations in the NRRI Study differ slightly from the
    copper and fiber cable regression equations adopted in this Order.
    The difference in the specifications does not alter the statistical
    conclusions regarding the impact of the Huber methodology on the
    level of cable material costs reflected in the cable cost estimates.
    We estimated by using OLS copper and fiber cable regression
    equations for which the specifications matched identically those for
    the copper and fiber cable regression equations estimated by using
    the Huber methodology. Again, with one exception, the cable size
    coefficient in the regression equations estimated by using the Huber
    methodology lies inside the 95 percent confidence interval
    associated with the cable size coefficient in the regression
    equations with the identical specifications estimated by using OLS.
    The one exception is that the value of the cable size coefficient in
    the buried copper cable and structure regression equation estimated
    by using the Huber methodology lies outside the 95 percent
    confidence interval associated with the cable size coefficient in
    the buried copper cable and structure regression equation with the
    identical specification estimated by using OLS. Again, we conclude
    that the Huber methodology does not have a statistically significant
    impact on the level of the cable material costs reflected in the
    cable cost estimates other than the buried cable cost estimates.