Court Opinion

ID: 5138970
Source: CourtListenerOpinion
Date Created: 2021-12-21 15:22:22.036329+00
Date Added: 2024-06-11T08:24:13.260429
License: Public Domain

2019 UT App 39

               THE UTAH COURT OF APPEALS

 CALIFORNIA COLLEGE INC., STEVENS-HENAGER COLLEGE INC.,
COLLEGEAMERICA SERVICES INC., COLLEGEAMERICA DENVER INC.,
           AND COLLEGEAMERICA ARIZONA INC.,
                       Appellees,
                            v.
             UCN INC. AND INCONTACT INC.,
                      Appellants.

                            Opinion
                       No. 20160120-CA
                      Filed March 21, 2019

           Third District Court, Salt Lake Department
                The Honorable Heather Brereton
                          No. 090907053

            David M. Bennion, Zack L. Winzeler, and
           Alan S. Mouritsen, Attorneys for Appellants
        Robert E. Mansfield and Steven J. Joffee, Attorneys
                          for Appellees

JUDGE MICHELE M. CHRISTIANSEN FORSTER authored this Opinion,
   in which JUDGES KATE APPLEBY and DAVID N. MORTENSEN
                         concurred.

CHRISTIANSEN FORSTER, Judge:

¶1      This is an interlocutory appeal asking us to determine
whether the district court abused its discretion when it denied
inContact’s pretrial motion to exclude certain expert witness
testimony. InContact contends that the data set underlying the
experts’ opinions at issue failed to meet the standards of
reliability set forth by rule 702 of the Utah Rules of Evidence.
Because both expert opinions admitted by the court relied on
data the parties agree was, to an extent, unreliable, we determine
that a threshold showing of reliability was not shown.
                     California College v. UCN

Accordingly, we vacate the district court’s ruling denying
inContact’s motion to exclude and remand for further
proceedings.

                         BACKGROUND

¶2      The appellant in this case is inContact Inc., formerly
known as UCN Inc. We use its current name for clarity. The
appellees are a consortium of educational entities including
California College Inc., Stevens-Henager College Inc.,
CollegeAmerica Services Inc., CollegeAmerica Denver Inc., and
CollegeAmerica Arizona Inc. 1 For convenience, we refer to them
collectively as “the College,” mindful that the appellees are
separate entities and without ascribing any legal status to the
term.

¶3      InContact provided enhanced telephone services to assist
the College in recruiting students. From February 2006 to April
2007 (Damage Period), the College’s offices “were frequently
unable to receive inbound calls,” and “inbound calls were often
incorrectly routed, delayed, or simply lost.” These problems
allegedly left the College without full telephone capabilities for a
total of approximately 108 hours.

¶4     In December 2009, the College filed a complaint against
inContact, alleging multiple causes of action. The College sought
damages equal to its lost profits, asserting that the telephone
problems led to lower recruitment, which in turn led to reduced
student enrollment and therefore lost profits. The College
retained two experts with experience in calculating damages:

1. California College Inc. does business as California College for
Health Sciences and Independence University.

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                    California College v. UCN

Ted Tatos, a statistician, and Richard Hoffman, a certified public
accountant.

¶5     Tatos estimated the number of student enrollments that
the College lost during the Damage Period. Tatos used a method
known as regression analysis to develop a statistical model for
estimating the number of enrollments the College should have
had but for the malfunctioning telephone services. 2 To conduct
the regression analysis and develop the model, Tatos relied upon
data collected by the College. The data reflected, among other
things, how many potential students had contacted the College,
how many interviews were conducted, and how many students
had enrolled. Tatos compared the figures from January 2003 to
March 2006 (before the Damage Period) and June 2007 to
December 2010 (after the Damage Period) with the figures from
the Damage Period. These figures ostensibly were drawn from
the College’s internal operating reports (OPS Reports). Using his
regression model, Tatos estimated that the College suffered 1,254
lost student enrollments as a result of the faulty telephone
services.

¶6     Hoffman used Tatos’s estimate of lost enrollments to
calculate the College’s lost profits. He first determined the
average net tuition dollars the College received for each student
who enrolled during 2006 (roughly $19,000) and subtracted the
costs the College would have incurred to generate that tuition
(roughly $3,300). Hoffman then multiplied this result by the

2. Regression analysis is a statistical tool concerned with
estimating the relationship between different variables and the
influence of one variable on other variables. See Reed Constr. Data
Inc. v. McGraw-Hill Cos., 49 F. Supp. 3d 385, 396–401 (S.D.N.Y.
2014) (providing a detailed explanation of regression analysis).
For example, regression analysis may be used to test the
predictive strength of snowfall on ski tourism.

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                    California College v. UCN

number of lost enrollments calculated by Tatos and arrived at
approximately $19.7 million in lost profits. 3 Hoffman’s original
report proffered these analyses and his damages estimate.

¶7     InContact moved to exclude Tatos and Hoffman as expert
witnesses. It submitted two rebuttal expert reports—one from
Patrick Kilbourne, a forensic accountant, and one from Steven
Waters, an economist. Kilbourne’s report cast doubt on the
accuracy of the data set Tatos used, while Waters’s report
challenged the reliability of Tatos’s statistical model based upon
the data.

¶8     Kilbourne first noted that the values assigned to the
variables Tatos used were incorrect. Instead of relying on the
actual OPS Reports, Tatos used a “summary” prepared by Carl
Barney, one of the owners of one of the colleges. Barney drew
some values from the OPS Reports but also supplemented those
with his own estimates. It was Barney’s “summary,” prepared
specifically for the purpose of the lawsuit, that had been
provided to Tatos and upon which Tatos based his regression
analysis and the resulting statistical model. Kilbourne also
identified some of the flaws in the OPS Reports. Because the OPS
Reports each included twelve to eighteen months of rolling data,
a particular month appeared in multiple reports and the
numbers reflected for that month could vary for a variety of
reasons. For example, the OPS Reports that included January
2007 recorded the number of interviews conducted during that
month variously as 582, 594, 622, 631, and 654. Tatos used the
lowest value in his analysis, without explanation, and Kilbourne

3. InContact notes that this amount of lost profits exceeds the
amount of the College’s actual annual profits before and during
the Damage Period. In 2004, before the Damage Period, the
College’s profits were $4,263,322. In 2005, the profits were
$1,223,191 and in 2006, the profits were $2,453,204.

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                     California College v. UCN

criticized this approach because it resulted in a model that
maximized the estimated shortfall. Moreover, Kilbourne noted
that many of the numbers provided by Barney and used by
Tatos did not match any of the OPS Reports. 4

¶9     Waters’s report first rebutted the concept of lost
enrollments, suggesting that the changing unemployment rate
produced lower enrollments—a variable not accounted for in
Tatos’s model. Had Tatos incorporated the unemployment rate
as a variable, Waters suggested, Tatos would have found that the
data predicted no lost enrollment. Waters then opined that
Tatos’s model was overly sensitive to changes in the input
values. To demonstrate this shortcoming, Waters plugged
different sets of values into Tatos’s statistical model. Waters first
used values from the most recent OPS Reports, noting that the
College’s lawyers had described those figures as “the ‘most
accurate and up-to-date’ data.” Waters concluded that, “[b]y
simply using [these values] instead of the [values] used by Mr.
Tatos, I find that Mr. Tatos’s model estimates [lost enrollments]
of 588—a decrease of over 53% from 1,254—and this is without
accounting for the unemployment rate.” Waters also applied two
other sets of numbers to Tatos’s model: Tatos’s data through
2007, which resulted in a calculation of 358 lost starts, and the
OPS report data through 2007, which resulted in a calculation of
275 lost starts. In other words, Waters opined that (1) Tatos’s
model was flawed because it did not account for any change in
the unemployment rate; and (2) even if not flawed in this

4. On appeal, the College concedes that “the initial data
provided to [Tatos and Hoffman] was mistakenly not derived
entirely from OPS Reports and included certain inaccurate
information.” For example, the numbers recorded in Barney’s
summary only matched the OPS Reports for two of the forty-six
months between February 2005 and December 2008.

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                    California College v. UCN

manner, Tatos’s model was too sensitive because it generated
drastically different results when the input values were
changed. 5

¶10 In response, the College submitted an updated expert
report from Hoffman. Hoffman’s updated report acknowledged
that the values used in his original report were inaccurate. In
his updated report, Hoffman noted that the College had
“decided that the data used by Dr. Waters provided the most
reasonable estimate of” the inquiry, interview, and enrollment
figures. Hoffman therefore performed his calculations anew,
starting with 588 lost enrollments and multiplying that by
the same net per-student profit margin he had calculated
before. In essence, Hoffman utilized the same numbers
Waters had used to show that Tatos’s model was oversensitive,
but neither Waters nor Hoffman had independently verified
the data set by checking against the raw numbers. On this
basis, Hoffman re-estimated the College’s lost profits at roughly
$9.2 million. 6

5. InContact asserts, and we agree, that Waters was not opining
that the newest OPS Reports were the most accurate or that
Tatos’s model, based on a regression analysis of inaccurate
numbers, could generate accurate results so long as the right
inputs were used.

6. Several months after Hoffman’s updated report, in response to
inContact’s Statement of Discovery Issues filed with the court
seeking the production of the OPS Reports, the College asserted,
“After reviewing its OPS reports, as well as the student data
identified by Dr. Waters as coming from the most recent OPS
reports, [the College] determined that the data pulled from the
OPS reports by Dr. Waters, which reflects monthly student data
from the most recent OPS reports, reflects the most accurate and
                                                  (continued…)

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                     California College v. UCN

¶11 After Hoffman submitted his updated report, both
Kilbourne and Waters updated their expert reports as well.
InContact’s experts continued to disagree with the analysis and
conclusions reached by Hoffman. Waters asserted, “[T]he
analysis I put forward in [my first] report was done for the sole
purpose of demonstrating the sensitivity of [the College’s]
findings. I did not, and still do not, consider [the College’s
expert’s] model to be statistically reliable.” Similarly, in his
updated report, Kilbourne asserted,

      [B]oth Dr. Waters and I identified data contained in
      [the OPS Reports] generated by [the College] in the
      normal course of their business that was
      substantially different from the data supplied to
      and relied upon by Mr. Hoffman. I did not opine,
      and I do not understand that Dr. Waters opined,
      that either data set was accurate or inaccurate, but
      rather that [the College] had produced multiple
      documents with conflicting data.

Kilbourne concluded that the integrity and accuracy of the data
set relied on by both Tatos and Hoffman was questionable and
that they had relied on irrelevant data in their analyses.

¶12 InContact moved to exclude Hoffman and Tatos as expert
witnesses, along with their respective opinions. The district court
held a hearing on the motion and noted inconsistencies in the
data provided to and relied on by Tatos, stating, “[T}he
regression analysis conducted by Mr. Tatos was not conducted
reliably in this case because it was based on flawed data and
insufficient data under Rule 702(b).” See Utah R. Evid. 702(b).

(…continued)
reasonable data upon which to analyze [the College’s] operating
metrics.”

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                    California College v. UCN

The district court therefore “exclude[d] any testimony based
upon the original data, including testimony from Mr. Tatos and
testimony from Mr. Hoffman to the extent he relied upon Mr.
Tatos’[s] work with the unreliable data.” 7

¶13 Following the district court’s order excluding the expert
testimony based upon Tatos’s original data, the case was
assigned to a different judge who revisited that decision.
Although no transcript of this hearing was included in the
record on appeal, the parties agree the court sua sponte
determined that it would reconsider the motion to exclude Tatos
and Hoffman from trial. Two days later, inContact filed an
objection to the court’s decision to reconsider. On December 11,
2015, the court again held a hearing regarding the motion to
exclude the College’s expert witnesses.

¶14 At that hearing, inContact argued that “the data on which
[the College] relied both in the first [report], but even in the
second [report], is unreliable data.” InContact highlighted
Tatos’s reliance on Barney’s summary of the OPS Reports rather
than the actual reports, but it also asserted that the underlying
OPS Reports were “inherently unreliable.” As an example,
inContact noted that the data for a given month varied from
report to report: “Then it happens again and again and again;
and the numbers vary wildly. So those numbers become really
problematic for making any kind of a reliable assessment. Which
number do you take? Which operations report do you take [the
number from]” to represent a particular month?

7. The court did not order Hoffman’s exclusion as an expert
witness. It determined that Hoffman’s updated report, which
relied in part on the values Waters used to discredit the initial
report, was sufficiently reliable to be admitted into evidence. We
address the apparent contradiction later in this opinion. Infra
¶¶ 27–29.

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                    California College v. UCN

¶15 InContact also explained that its expert—Waters—lacked
access to all of the College’s data, and therefore he relied on the
value reported in the most recent OPS Report for a given month.
InContact stressed that Waters had not vouched for the
reliability of the data and characterized his report as unreliable:
“‘Even if you do [the analysis] with the numbers that [the
College]” says are “the most reliable numbers, it’s either 588,
358, or 275 [lost starts.]’” And inContact pointed out that
Waters’s final opinion was that there were zero lost starts. Thus,
according to inContact, both Tatos’s and Hoffman’s reports were
based on inherently unreliable raw data.

¶16 The court conceded that “[i]n a case like this with
business records kept the way they’re kept, you’re never going
to have a perfect number, it seems, because of the rolling nature
of the operation reports” but noted that “we don’t have to have
perfect numbers.” The court stated that it could not rule on
“whose regression analysis and methodology was better”
because that was “an issue for the fact finder.” Instead, the court
framed the issue as whether the expert opinions of Tatos and
Hoffman were reliable and, if not, the unreliability was “because
[the experts] didn’t use good data.”

¶17 The        court   eventually    ruled   that    inContact’s
arguments “go to the weight and credibility to be given to the
opinions of [the College’s] expert witnesses, not to the
admissibility of those opinions.” The court found that “the
facts and data upon which [the College’s] expert witnesses
have relied are sufficiently reliable to satisfy the minimal
threshold standard imposed by Rule 702 [of the Utah Rules of
Evidence]. Although the facts and data may not be perfect,
perfection is not required to satisfy Rule 702’s threshold
standard.” The court noted that it was the jury’s role, not the
court’s, to determine the weight and credibility of the experts’
opinions flowing from the data. The court therefore vacated its
earlier order excluding Tatos’s testimony, denied the motion to

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                     California College v. UCN

exclude Tatos’s and Hoffman’s testimony, and ruled that Tatos
and Hoffman would be permitted to offer expert testimony
during the trial. InContact sought interlocutory review, which
this court granted.

             ISSUE AND STANDARD OF REVIEW

¶18 InContact contends that, pursuant to rule 702 of the Utah
Rules of Evidence, the district court erred or abused its
discretion in not excluding Tatos’s and Hoffman’s opinions
because those opinions were not based upon sufficient facts and
data. We review a district court’s decision as to the admissibility
of expert witness testimony for an abuse of discretion and will
not reverse that decision unless it exceeds the limits of
reasonability. ConocoPhillips Co. v. Utah Dep’t of Transp., 2017 UT
App 68, ¶ 12, 397 P.3d 772.

                           ANALYSIS

¶19 The district court initially excluded the expert opinion
testimony of Tatos and Hoffman as “flawed” and “insufficient”
to the extent those opinions rested on the results of Tatos’s
model based on his original data. See Utah R. Evid. 702(b).
Following assignment of the case to a different judge, the district
court reconsidered this decision and reversed course in a second
ruling, determining instead that these expert opinions—though
imperfect—would be admitted.

¶20 As an initial matter, we note that the district court’s
reconsideration of the expert witness issue was not
inappropriate. “While a case remains pending before the district
court prior to any appeal, the parties are bound by the court’s
prior decision, but the court remains free to reconsider that
decision.” IHC Health Services, Inc. v. D & K Mgmt., Inc., 2008 UT

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                      California College v. UCN

73, ¶ 27, 196 P.3d 588. And, two district court judges presiding
over the same case, “while different persons, constitute a single
judicial office.” In re R.B.F.S., 2012 UT App 132, ¶ 12, 278 P.3d
143 (quotation simplified). Thus, a district court judge may
“revisit[] a previously decided issue during the course of a case,
regardless of whether the judge has changed or remained the
same throughout the proceedings” so long as the issue has not
been raised before and ruled on by an appellate court. Mid-
America Pipeline Co. v. Four-Four, Inc., 2009 UT 43, ¶ 11, 216 P.3d
352.

¶21 We begin with the district court’s second ruling: “The
Court finds that the facts and data upon which [the College’s]
expert witnesses have relied are sufficiently reliable to satisfy the
minimal threshold standard imposed by Rule 702.” The expert
witnesses referred to were Tatos and Hoffman. Because the only
numbers used by Tatos were drawn from Barney’s summary, we
understand the court’s statement to mean that non-expert, party-
representative Barney’s summary of the data, or a subset of that
summary, prepared for this lawsuit, was a sufficient basis for the
experts’ testimony.

¶22 Rule 702(b) describes when certain expert opinion is
admissible:

       Scientific,  technical,    or    other   specialized
       knowledge may serve as the basis for expert
       testimony only if there is a threshold showing that
       the principles or methods that are underlying in
       the testimony

       (1) are reliable,

       (2) are based on sufficient facts or data, and

       (3) have been reliably applied to the facts.

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                     California College v. UCN

Utah R. Evid. 702(b). “Importantly, [rule 702] require[s] the
plaintiff to make only a threshold showing of reliability.”
Eskelson ex rel. Eskelson v. Davis Hosp. & Med. Center, 2010 UT 59,
¶ 12, 242 P.3d 762 (quotation simplified). This threshold “is not
so rigorous as to be satisfied only by [methodology or data] that
are free of controversy.” Id. (quotation simplified); cf. State v.
Woodard, 2014 UT App 162, ¶ 26, 330 P.3d 1283 (holding that one
method of fingerprint analysis was sufficiently reliable to be
admitted into evidence despite the existence of several
competing methodologies and the lack of a nationwide standard
method). While an expert may rely on his or her own
interpretation of data that have a foundation in the evidence,
even if the data is in dispute, the expert “cannot give opinion
testimony that flies in the face of uncontroverted” facts or data.
Eskelson, 2010 UT 59, ¶ 16 (quotation simplified). Thus, while a
range of values for a given variable may be admissible under
rule 702, there must be some evidence underpinning the values
used by the expert.

¶23 Applying rule 702, district courts “act as gatekeepers to
screen out unreliable expert testimony.” State v. Lopez, 2018 UT 5,
¶ 20, 417 P.3d 116 (quotation simplified). The court, “view[ing]
proposed expert testimony with rational skepticism,” id.
(quotation simplified), must determine whether the proponent
has met the threshold showing that the method used is
“‘generally accepted by the relevant expert community’” or that
the “principles underlying [the expert’s] testimony are ‘reliable,
based upon sufficient facts or data, and have been reliably
applied to the facts,’” id. ¶ 22 (ellipses omitted) (quoting Utah R.
Evid. 702).

¶24 As applied here, the data used by Tatos and Hoffman to
generate their opinions must have some foundation indicating
its reliability before the court may admit the data, the models
developed from that data, or the interpreted results of the
models. However, the College concedes that “the initial data

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                     California College v. UCN

provided to [Tatos and Hoffman] was mistakenly not derived
entirely from OPS Reports and included certain inaccurate
information.” And on appeal, the College does not direct us to
any evidence that the original data set used by Tatos to create his
regression model was somehow reliable despite including
inaccurate information.

¶25 The district court’s ruling states that the data set given to
Tatos was reliable. Now on appeal, however, even the proponent
of Tatos’s testimony admits that the data set “included certain
inaccurate information.” Because the College admits that the
data given to Tatos contained some inaccurate values and did
not identify any evidence rehabilitating the balance of those
data, we cannot endorse the district court’s conclusion that the
data met the threshold of reliability required by rule 702. And
Tatos does not claim that he in any way independently verified,
even minimally, the accuracy of the data set Barney originally
supplied. We therefore reject the district court’s conclusion that
simply assuming the reliability of Barney’s summary endows the
data with any degree of actual reliability. See, e.g., Maddox v.
Claytor, 764 F.2d 1539, 1552 (11th Cir. 1985) (“If the tested
disparity is based on erroneous assumptions or suffers from
flaws in the underlying data, then standard deviation analysis is
foredoomed to yield an equally faulty result.”).

¶26 Further, Tatos used unverified and summary
data provided by Barney to create his regression model. The
results of that analysis were also admitted by the district
court. But the nature of regression analysis is such that a
regression model based on flawed data will itself be flawed,
because a “best fit” model that uses erroneous data may
not reasonably approximate the actual values. See generally
Franklin M. Fisher, Multiple Regression in Legal Proceedings, 80
Colum. L. Rev. 702, 704, 707 (1980) (discussing the process of
regression analysis); Kevin Gilmartin & Elizabeth Hartka, Using
Regression Analysis to Compute Back Pay, 31 Jurimetrics J. 289, 295–

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                     California College v. UCN

96, 298 (1991) (noting, as a pitfall of regression analysis,
the design risk presented by “inclusion of tainted variables” and
the fact that “an ill-designed regression model could result in an
even more inequitable distribution of relief”); Michael J. Saks et
al., 179 Annotated Reference Manual on Scientific Evidence (2d
ed.), Reference Guide on Multiple Regression, at *15–16 (noting
that multiple regression analysis assumes that the variables
have been measured accurately and that inaccurate data will
decrease the reliability of the results). The College never
explained why Tatos’s model, developed from erroneous and
unreliable data, should nevertheless be considered reliable, even
by the low bar set by rule 702. Rule 702 requires an expert’s
opinion to rest on “sufficient facts and data.” Utah R. Evid.
702(b). Accordingly, we conclude that it was an abuse of
discretion for the district court to admit Tatos’s regression model
and its results, because the model was developed from “certain
inaccurate information.”

¶27 We next consider whether Hoffman’s opinions are
admissible. 8 Hoffman’s initial opinion had two operative parts.
First, Hoffman calculated the value of each lost start. Second, he
calculated the total amount lost by multiplying the number of
lost starts—estimated by Tatos to be 1,254—by the estimated
amount lost per-lost-start. Hoffman later modified his damages
opinion by substituting the number of lost starts from Waters’s
rebuttal opinion—588—for Tatos’s original number. Because

8. The district court did not directly rule upon whether
Hoffman’s updated expert report was admissible. Instead, it
determined that the data on which it relies—Waters’s
calculation—was sufficiently reliable. Because we conclude
otherwise, we address the admissibility of Hoffman’s updated
report in order to provide direction to the district court and
parties on remand.

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Hoffman relied on Waters’s results, we must also consider
Waters’s conclusion.

¶28 Waters ran moderately different, hypothetical data
through Tatos’s statistical model and arrived at a number of lost
starts that is less than half of Tatos’s result. Due to the widely
divergent results (1,254 and 588), Waters concluded that Tatos’s
statistical model itself was not reliable as it projected more
dramatic results with only moderate changes to the input data.
This also supported inContact’s broader assertion that the
original data did not support the proposition that the phone
system caused any lost starts. Neither Waters nor inContact
presented foundational evidence supporting the reliability of
Tatos’s statistical model. Indeed, the purpose for Waters’s
inputting new numbers through the model was to establish the
model’s complete unreliability. In modifying his expert opinion,
Hoffman simply adopted the number of lost starts from Waters’s
calculation without any explanation other than acknowledging
Waters had used them. No foundational evidence appears to
have been presented regarding the reliability of Hoffman’s
opinion—specifically the statistical model’s result that informs
his conclusion—other than Hoffman’s own statement that
Waters’s report provided “the most reasonable estimate” of the
relevant data.

¶29 Because nothing in the record establishes that Hoffman
has any expertise in regression analysis, there is no basis to
conclude that Hoffman could determine whether Waters’s report
was a reasonable estimate, let alone “the most reasonable
estimate.” Indeed, Hoffman fails to acknowledge that Waters in
no way endorsed the data he utilized as reliable. Instead, for
only rhetorical purposes, Waters used data the College’s
lawyers—not its experts—had suggested was better than the
previously inaccurate data. Accordingly, given the paucity of
foundational support, we must conclude that the district court
erred in determining that the College met the threshold showing

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                    California College v. UCN

of reliability required under rule 702(b) to admit Hoffman’s
expert opinion.

¶30 When the district court revisited its ruling, it determined
that the second set of data—the set of numbers used by Waters
in the rebuttal opinion showing the oversensitivity of Tatos’s
model—was sufficiently reliable to meet the threshold
requirement under rule 702. Accordingly, the court admitted,
without limitation, the expert opinion testimony of Tatos and
Hoffman. This was error. The court’s ruling implied that any
regression analyses, models, or results based upon Waters’s data
critical of Tatos’s model, satisfied rule 702. But, as we have
explained, the court’s determination that the data set was
admissible pursuant to rule 702 was an abuse of discretion. It
follows that it was improper to admit an expert opinion whose
factual basis relied on the flawed data. Although the district
court did not directly rule upon the admissibility of Hoffman’s
updated expert opinion, Hoffman’s updated opinion is based
upon flawed data and a correspondingly flawed statistical
model. As a result, that opinion should have been excluded.

                        CONCLUSION

¶31 The district court abused its discretion by admitting
Tatos’s expert opinion and Hoffman’s initial expert opinion,
because those opinions were developed from a data set both
parties acknowledge is unreliable. The district court also erred
when it determined that the second set of numbers—Waters’s
assumed data—was sufficiently reliable to meet the threshold
requirement of rule 702. We therefore vacate the district court’s
order and remand for further proceedings consistent with this
opinion.

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