Final Group Paper ECON 123 Group 10

Running head: “Next Time on….” AN ECONOMIC REGRESSION ON HOW TELEVISION
SHOWS ARE RENEWED FOR AN ADDITIONAL SEASON
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“Next Time on….” An Econometric Regression on How Television Shows are Renewed for
an Additional Season.
Econ 123 Econometric Project, Group 10
Ismael Reyes, Scott Fry, Pinder Singh
SPRING 2015

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Abstract
This research investigates the impact on the renewal of a second season for television
programing using a seasonal level panel data set. Previous studies regarding the renewal of
television programing have approaches that focus on either capital investment or demographic
data of viewers. This study uses the focal point of the composition of the actual program to
deduce the renewal of the second season of programing. The research finds that among all of the
television ratings, TVY7, TVPG and TVMA are the most significant. The most significant genre
categories include comedy, drama and reality TV. And the most significant broadcast format
was cable subscriptions. These findings are relevant in that they are most likely to contribute to
the renewal of a television programs second season.
Introduction
Using econometrics, statistical methods can be applied to collected data to estimate a
relationship between a dependent variable – which is the second season renewal of a television
series – with the independent variables – which are attributed to various characteristics which
originate from within the television series themselves (Halcoussis, 2005). The research question
is: Do qualitative factors such as lead characters, parental ratings, genres, number of episodes,
means of distribution, and runtime have a positive correlation with the renewal of a second
season for television series? In addition, with an econometric study the unit of measurement
must be defined to determine whether or not a program will be renewed - which in this case is
commonly referred to as a season. According to Landon Palmer, who writes critical review

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articles pertaining to television series, a television season is defined as a separation of episode
groups by discrete gaps in the historical progression of time (Palmer, 2013). Normally,
television seasons are divided between two calendar seasons – summer and winter – and are
ordinal in their appearance. Additionally, the number of episodes that comprise a season can
vary depending on the projected television series. The study introduces a literature review of
previous economic studies on the topic relating to the relationship of marketing levels and
television series. Also included are the conclusions which other scholars have written stating
their analytical findings on the same subject. For the study, the regression model used is
ordinary least squares, also the binary choice model is used in order to calculate estimates that
can be interpreted as probabilities. Finally, this study will be concluded by covering the findings
of the research methods and provide insight into the effects of what determines whether or not a
television program is renewed for a second season.
Review of the Literature
The literature review begins by introducing prior analytical research that is associated
with the renewal of a television program for an additional season associative to factors or
characteristics that might convey a common discourse. For example, in a related study,
conducted by Gong, Van der Stede, and Young, the relative economic factor was capital
investment. Their approach was to use a cost benefit analysis for film marketing and sequels
within the motion picture industry. It focused on the renewal of television programing in which
the television studio, faced with analysis decisions, adopts what is referred to as the real options

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framework in which companies initiate risk management (Gong et al., 2011). A real option is
defined as the appropriate, but not obligatory, pursuit of business decisions; normally in the form
of an investment. Movie studio executives also use a dual option method with which they
choose to either continue, abandon, or increase their commitment to certain shows. The first of
these options, referred to as a growth option, allows studio executives to produce additional
feature films and gives them the ability to develop franchises. The second option, referred to as
an abandonment option, in which a film is abandoned after the initial release if revenues fall
below desired expectations, then the marketing dollars for the film are reassigned to other
projects (Brealey, Myers, and Allen 2008). The study concluded two things: (1) that marketing
costs diversified with the initial success of a film’s release, and (2) real options were more
favorable where motion picture studios incurred higher production and marketing costs for
original franchises with sequels than films without sequels. Furthermore, another finding within
this study indicated that production costs are inversely related to marketing costs for sequels than
for non-sequel films.
In another conducted by Karen S. F. Buzzard, audience research reports are used, via the
Nielsen ratings system. In this study, Buzzard claims the factors that determine the renewal of
television programs are consumer demographics and research and development. This provides
an economic base for the broadcast industry, where there is a dual purpose for the
implementation of revenues for broadcasting firms, but it also further serves to provide the
criteria for programing selections (Stavinsky, 1995, 1998). Buzzard then further elaborates that

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the Federal Communications Commission’s (FCC’s) deceitful deregulation policies implemented
during the 1970s and 1980s not only broadened the market for entry by new firms, but it also
modified the focal point of the target audience of marketing research from the traditional
“nuclear family” to a more specified individual demographic and geographic viewership
(Buzzard, 2002). The study concluded that not only has the ratings system diverged towards
newer target audiences, but that firms that dominate the ratings market, such as Nielsen, tend to
be slow in research and development, but are quick to dominate new entrants when challenged.
Furthermore, it’s the investment and entrepreneurial functions which gives rise to the greatest
barriers to entry within the ratings market; and monopolistic companies, such as Nielsen, exploit
this weakness. This exacerbates innovation and research in the ratings market which leads the
market towards the unnatural equilibrium. In addition to these findings, the study approaches the
methodology of how television programs are greenlit for a second season by television studios.
Specifications of the Models
The research attempts to determine the extent of a relationship between the renewal of a
television program for a second season and the compositional structure of the television series
itself. Using econometric models with second season as a dependent variable, the research uses
regression analysis to determine if compositional structures has a statistically significant impact
on the television programs renewal. For empirical testing, this research builds two models to test
the hypothesis that all of our expected signs for the estimated coefficients will be positive. The
first being the Ordinary Least Squares (OLS) model and the second a Binary Choice model. The

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binary choice model is used in the study due to the dependent variable is set to 1 if the television
program is renewed for a second season and 0 if it is not. Furthermore, the binary choice model
is a better solution when estimating qualitative choices since linear probability models dispense
estimated probabilities that lie below 0 or 1 - which are values that are impractical (Halcoussis,
2005).
The majority of independent variables to be regressed within the model are comprised of
dummy variables that will be assigned numerical values of 0s and 1s. Additionally, the
descriptive stats of the regressions within the appendix will exclude these categorical dummy
variables. This is because there is no quantitative form of measurement available for these
variables. The data set consists of three distinct sub – groups of variables: lead character sex,
specified genre, and appropriate parental ratings. The only time series variables included within
the model consist of number of episodes per season and number of minutes per episode for each
television program. The most relevant variables will be ultimately included in the model and the
others excluded to mitigate multicollinearity. By fitting the statistical models with compositional
effects of the television programs, the models are more likely to be more powerful for
determining whether or not the television programing will be renewed for a second season.
Data Description
This data was collected for a five year time period (2010 – 2014) and relevant shows for
the period were the top 100 television programs for each year. In total, 500 hundred television
programs are included for all five years. Relevant data for all of the variables was obtained

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through appropriate sites that monitor, track and summarize television listings, see references for
the websites used. The dependent variable SNDSEASON is a dummy variable that relays the
fact that the television program will be renewed for a second season which is assigned 1, 0
otherwise. The independent variable MALE is a dummy variable which ascertains the relevant
sex of the lead character for the television program, 1 if the lead character in TV show is male, 0
otherwise. The independent sub – group of variables that determine the parental ratings of the
television program is represented by five dummy variables which are; TVY, TVY7, TVPG,
TV14, and TVMA, see appendix table 1. There are actually six ratings and TVG was
determined to be the base. TVY represents the television program is appropriate for all children,
including children ages 2 – 6. TVY7 represents that the television program is appropriate for
children ages 7 years and older. TVPG represents that the television program contains material
that is unsuitable for younger children with the program containing one or more of the following:
some suggestive dialog, infrequent coarse language, some sexual situations and or moderate
violence. TV14 represents television programs that contain material unsuitable for children
under 14 years of age. The program may contain one or more of the following: intensely
suggestive dialogue, strong coarse language, intense sexual situations and or intense violence.
The last rating is TVMA which represents programing only suitable for children over the age of
17 and the rated program may contain one or more of the following: crude indecent language,
explicit sexual activity and or graphic violence (TV Parental Guidelines, 2015). The next
subgroup of independent variables, also dummy variables, are inclusive of the genre in which the

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story of the television program falls under which are; COMEDY, DRAMA, FAMILY,
MYSTERY, REALITY, ROMANCE and SCIFI where the genre of HORROR was designated to
be the base, see appendix table 2. The third sub – group of categories were designated as dummy
variables and are comprise of the method of how the programing is aired via subscription
services or contractual arrangements. These variables are; BROADCAST and CABLE in which
the base was determined to be SATALITE. The last two independent variables are time series in
nature which measure the amount of the available programing per television show within each
season. These two variables are; MINUTES which measure the number of minutes per episode
during a television season and EPISODES which measure the amount of episodes aired during
each programing season.
Economic Model
The model was initially estimated using the Ordinary Least Squares (OLS) method.
However, due to the dependent variable being a dummy variable, the Binary Choice Model is
also used for this research. The ordinary least squares model is as follows:
𝑆𝑁𝐷𝑆𝐸𝐴𝑆𝑂𝑁 = 𝐵0 + 𝐵1 · 𝑀𝐴𝐿𝐸 + 𝐵2 · 𝑇𝑉𝑌 + 𝐵3 · 𝑇𝑉𝑌7 − 𝐵4 · 𝑇𝑉𝑃𝐺 − 𝐵5 · 𝑇𝑉14 +
𝐵6 · 𝑇𝑉𝑀𝐴 + 𝐵7 · 𝐶𝑂𝑀𝐸𝐷𝑌 + 𝐵8 · 𝐷𝑅𝐴𝑀𝐴+ 𝐵9 · 𝐹𝐴𝑀𝐼𝐿𝑌+ 𝐵10 · 𝑀𝑌𝑆𝑇𝐸𝑅𝑌+
𝐵11 · 𝑅𝐸𝐴𝐿𝐼𝑇𝑌+ 𝐵12 · 𝑅𝑂𝑀𝐴𝑁𝐶𝐸 − 𝐵13 · 𝑆𝐶𝐼𝐹𝐼 + 𝐵14 · 𝐸𝑃𝐼𝑆𝑂𝐷𝐸𝑆 − 𝐵15 · 𝐵𝑅𝑂𝐴𝐷𝐶𝐴𝑆𝑇 + 𝐵16
· 𝐶𝐴𝐵𝐿𝐸 + 𝐵17 · 𝑀𝐼𝑁𝑈𝑇𝐸𝑆
To determine the appropriate model using ordinary least squares, the model had been run several
times to determine which estimated coefficient was statistically significant. Several regressions
were run removing various independent variables with the final model run was the semi – log
model in which the data for minutes and episodes gave more significant figures to the estimated

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coefficients, see appendix semi – log regression. However, MINUTES was determined to be an
irrelevant variable, with a statistical significance of 0.925 and it was concluded that there might
be some multicollinearity between MINUTES and EPISODES.
Results
Due to the nature of the model and the composition of the data, 𝑅2
can be ignored in this
instance. Once the semi – log OLS model was sufficient to show the most significant results,
multicollinearity was checked for with the following results. SPSS calculated the resulting
correlation coefficient of – 0.112, see appendix, table 3. These results mean the two variables
are negatively correlated, but not perfectly negatively correlated. Because the correlation
coefficient is close to 0, this indicates the two variables don’t tend to move together. Additional
analysis for multicollinearity was checked by regressing EPISODES on MINUTES, with the
following model:
𝐸𝑃𝐼𝑆𝑂𝐷𝐸𝑆 = 𝐵0 + 𝐵1 · 𝑀𝐼𝑁𝑈𝑇𝐸𝑆 + 𝑒
In addition to this, the data points surprisingly displayed a tremendous amount of negatively
related multicollinearity between the two variables, see appendix graph 1. Using the semi – log
regression model, seventeen regressions were run using the independent variables to calculate
the VIF with the following results. The three independent variables showed BROADCAST with
a VIF = 7.575, CABLE with a VIF = 7.092, and the ratings variable TV14 = 6.134. Further
examination of the correlation coefficient of the two variables, BROADCAST and CABLE
revealed a high VIF which made them highly correlated to one another with a correlation

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coefficient equal to – 0.914. Which is close to – 1 indicative of perfect negative correlation.
Running another OLS regression, and dropping BROADCAST, then the independent variable
CABLE became significant by 0.000. Furthermore, dropping TV14 which had a high VIF and a
negative correlation coefficient = – 0.479, made TVMA statistically significant at 0.002 since it
was capturing the same movement of the variable TV14, see appendix regression with no TV14
or BROADCAST. Although the data is not entirely comprised of time series data,
autocorrelation was checked due to the possibility that useful information might be missing from
the model. The Durbin – Watson (DW) was calculated using SPSS with the following result of
1.820, see appendix SPSS DW output. Checking for positive first – order autocorrelation, the
following one – sided test can be set up with the null hypothesis of no autocorrelation versus the
alternative hypothesis of positive autocorrelation. 𝐻0: 𝜌 ≤ 0 𝐻𝐴: 𝜌 > 0 With a lower bound of
1.795 and upper bound of 1.910 and the DW statistic lies between so the test is inconclusive.
Furthermore, the DW statistic is less than 2 so there is no need to check for negative
autocorrelation. Another method used to check for autocorrelation is the Cochrane – Orcutt
(CO) method. Before the CO method, the DW statistic is 1.820 with N = 500 and k = 15, after
calculating the AR (1) estimate of ρ = 0.102 and running the syntax command for CO, the new
DW then became 2.020, see appendix SPSS Output Cochrane – Orcutt method. This means that
the null hypothesis is not rejected of no positive autocorrelation; assume no autocorrelation. In
checking for heteroskedasticity, and using SPSS, a graph was made with the values of the
unstandardized residuals on the vertical axis contrasted with the Z factor variable, log minutes,

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on the horizontal axis, see appendix graph 2. Heteroskedasticity is a problem with the present
OLS model. The Park test was run with the proportional factor Z, log minutes variable was
chosen since its variance is larger than log episodes being (0.042 > 0.032). Running the semi –
log model regression and then squaring the error term observation to form the natural log
dependent variable which is run in a second regression, then the significance of the coefficient of
Z is tested with a t – test. The significance level of log minutes was 0.252, see appendix Park
test SPSS output. Since the proportionality factor Z is significantly different from zero, this is
evidence of heteroskedasticity within the OLS model. Additional testing for heteroskedasticity
was the White test, which was performed on the semi – log model. The results of the White test
were that our testable Chi-Square (125) – calculated by multiplying the number of observations
with the adjusted r-squared – was greater than our critical chi-square (90.53). Because of this, the
null hypotheses (errors are homeskedastic), was rejected. Proving that this model has
heteroskedasticity. Since it was determined that the semi – log model has heteroskedasticity, the
raw syntax for correcting the heteroskedasticity within the model was used which increased the
model’s standard errors and also decreased the model’s t – statistics. Also the coefficients did
not change using White standard errors forcibly correcting for the heteroskedasticity present
within the semi – log model. The last regression run was a binary choice model which was better
suited for the model since the model itself includes a majority of dummy variables both within
the dependent variable and the independent variables. The binary choice model tells us that if we
multiply the newly estimated coefficients by .25, this gives us the probability of a TV show

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getting a 2nd season for that given factor. In our final model, after multiplying our estimated
coefficients – which we got through running Binary Logit in SPSS – by .25, we now have the
percentage chance that a TV show with any of our 15 independent variables will get a 2nd season.
Also in our final model, all of our variables except two (Sci-Fi & Minutes), had positive signs.
The two coefficients with unexpected signs are rather odd because most television programs that
are fairly popular do tend to be longer (at least up to a certain point) and tend to have some
element or at least a minor reference to Sci-Fi. Intuitively, you would think that these to variables
would have a positive impact on the chance of a TV series getting renewed for a 2nd season.
Most likely, this problem is being caused by certain variables that are missing from our model.
Limitations
Future directions for the model will include adding additional variables not considered such as
which television programs had won Emmy nominations and the amount of money spent by
television studios. These missing variables are most likely causing bias within our model and
could also help explain why some of our estimated coefficients have unexpected signs. Emmy
nomination winners could possibly help explain the effect of a television program’s composition
on the dependent variable which is the second season renewal. The other missing independent
variable that would capture the amount of money spent by television studios, in millions of U.S.
dollars per season, might make the model work better. Unfortunately due to the unavailable
information and time constraints, neither of the above suggested data was unable to be obtained.
Further research should also be placed on the recent phenomenon of streaming services in which

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television programing can be viewed. Unfortunately due to the recent development of this area
of the market no data was able to be obtained for this study.
Conclusion
This paper investigates the impact of compositional factors present within television
programing to determine relevance to the renewal of a second season. The results presented in
this paper are not conclusive with previous literature for two reasons. The first reason being that
there was not that many studies concurrently done with respect to television programing. The
second reason is that the two previous studies reviewed have different approaches to include
more traditional approaches such as capital investment and demographic means for causality.
Additionally this research concludes that there is a significant correlation between the three
subcategories of independent variables of ratings (TVY7, TVPG and TVMA), genres
(COMEDY, DRAMA and REALITY TV) and method of presentation (CABLE) that could
induce future television programing to be renewed for a second season. Furthermore, television
programing should include a cost – benefit analysis since programing is always unique and faces
their own individual challenges. Given the initial hypothesis stated which gave relevance to
compositional elements of the television program itself being correlated with the renewal of a
second season, future emphasis should be placed with respect to a new direction of study rather
than current economic thought entails within the field of media economics.

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References
Brealey, R., S. Myers, and F. Allen. (2008). Principles of Corporate Finance. 9th ed. New York:
McGraw – Hill/Irwin.
Buzzard, K.F. (2002). The Peoplemeter Wars: A Case Study of Technological Innovation and
Diffusion in the Ratings Industry. Journal of Media Economics. 15(4), 273 – 291.
Gong, J. J., Van der Stede, W. A., & Mark Young, S. (2011). Real Options in the Motion Picture
Industry: Evidence from Film Marketing and Sequels. Contemporary Accounting Research.
25(5), 1438 – 1466. Doi: 10.111/j.1911 – 3846.2011.01086.x
Halcoussis, D. (2005). Understanding Econometrics. Mason, OH: Thomson South – Western.
Palmer, L. (2013, September 24). Just what is a Television “Season” Anyway?
Filmschoolrejects.com. Retrieved March 22, 2015, from
http://filmschoolrejects.com/features/just-what-is-a-television-season-anyway.php

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Stavinsky, A.G. (1995). Guys in White Suits with Charts: Audience Research in Public TV.
Journal of Broadcasting and Electronic Media. 39, 177 – 198.
Stavinsky, A.G. (1998). Counting the House in Public Television: A History of Ratings Use.
Journal of Broadcasting and Electronic Media. 42, 520.
TV Parental Guidelines. (2015). Retrieved April 26, 2015, from
http://www.tvguidelines.org/ratings.htm
Websites with Television Program Data
1. http://www.imdb.com/
2. http://www.rottentomatoes.com/
3. http://www.hollywood.com/
4. http://www.tvb.org/
Appendix
Table 1.
Table 2.
OLS (Semi – Log) Regression
Model Summary
GENRE TV SHOWS
COMEDY 146
DRAMA 147
FAMILY 26
MYSTERY 33
REALITY TV 50
ROMANCE 1
SCIFI 62
RATINGS TV SHOWS
TVY 7
TVY7 22
TVPG 99
TV14 241
TVMA 105

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Model R R Square Adjusted R Square Std. Error of the Estimate
1 .383a
.147 .116 .3599
a. Predictors:(Constant),X17(MINUTES), X1 (MALE), X11 (REALITYTV), X12 (ROMANCE), X4 (TVPG), X14
(EPISODES), X13 (SCIFI), X2 (TVY), X10 (MYSTERY), X16 (CABLE), X6 (TVMA), X3 (TVY7), X7 (COMEDY), X9
(FAMILY), X8 (DRAMA), X5 (TV14), X15 (BTN)
ANOVAa
Model Sum of Squares df
Mean
Square F Sig.
1 Regression 10.718 17 .630 4.867 .000b
Residual 62.440 482 .130
Total 73.158 499
a. DependentVariable:Y (SECOND SEASON)
b. Predictors:(Constant),X17(MINUTES), X1 (MALE), X11 (REALITYTV), X12 (ROMANCE), X4 (TVPG), X14
(EPISODES), X13 (SCIFI), X2 (TVY), X10 (MYSTERY), X16 (CABLE), X6 (TVMA), X3 (TVY7), X7 (COMEDY), X9
(FAMILY), X8 (DRAMA), X5 (TV14), X15 (BTN)
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
95.0% Confidence Interval
for B
B Std. Error Beta
Lower
Bound
Upper
Bound
1 (Constant) .741 .213 3.478 .001 .322 1.159
X1 (MALE) .027 .034 .034 .789 .430 -.040 .095
X2 (TVY) .071 .170 .022 .415 .678 -.264 .405
X3 (TVY7) .074 .111 .040 .667 .505 -.144 .292
X4 (TVPG) -.033 .084 -.035 -.399 .690 -.198 .131
X5 (TV14)
-.123 .080 -.161
-
1.542
.124 -.280 .034
X6 (TVMA) .017 .086 .019 .203 .839 -.152 .187
X7 (COMEDY) .136 .070 .162 1.950 .052 -.001 .274

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X8 (DRAMA) .150 .069 .178 2.165 .031 .014 .285
X9 (FAMILY) .067 .115 .039 .580 .562 -.159 .292
X10 (MYSTERY) .108 .090 .070 1.193 .234 -.070 .285
X11
(REALITYTV)
.202 .084 .159 2.418 .016 .038 .367
X12
(ROMANCE)
.146 .368 .017 .395 .693 -.578 .870
X13 (SCIFI) -.038 .079 -.033 -.488 .626 -.193 .116
X15 (BTN)
-.113 .090 -.145
-
1.248
.213 -.290 .065
X16 (CABLE) .083 .086 .108 .963 .336 -.086 .253
Log Episodes .011 .033 .016 .341 .733 -.053 .076
Log Minutes -.004 .045 -.005 -.094 .925 -.094 .085
a. Dependent Variable: Y (SECOND SEASON)
Table 3.
Correlations
X14 (EPISODES) X17(MINUTES)
Pearson Correlation X14 (EPISODES) 1.000 -.112
X17(MINUTES) -.112 1.000
Sig. (1-tailed) X14 (EPISODES) . .006
X17(MINUTES) .006 .
N X14 (EPISODES) 500 500
X17(MINUTES) 500 500
Graph 1.

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Graph 2.

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Regressionwith TV14 and BROADCAST Omitted.
Model Summary
Model R R Square Adjusted R Square
Std. Error of the
Estimate
1 .370a
.137 .110 .3613
Descriptive Statistics
Mean Std. Deviation N
Y (SECOND SEASON) .822 .3829 500
Log Episodes 2.5231 .55057 500
Log Minutes 3.6442 .46800 500

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a. Predictors:(Constant),Log Minutes, X1 (MALE), X11 (REALITYTV), X12
(ROMANCE), X4 (TVPG), X13 (SCIFI), X2 (TVY), X10 (MYSTERY), X16 (CABLE),
Log Episodes, X6 (TVMA), X3 (TVY7), X7 (COMEDY), X9 (FAMILY), X8 (DRAMA)
ANOVAa
Model Sum of Squares Df Mean Square F Sig.
1 Regression 9.989 15 .666 5.103 .000b
Residual 63.169 484 .131
Total 73.158 499
Coefficientsa
Model
Unstandardized Coefficients Standardized Coefficients
t Sig.
95.0% Confidence Interval for B
B Std. Error Beta Lower Bound Upper Bound
1 (Constant) .614 .199 3.077 .002 .222 1.006
X1 (MALE) .023 .034 .029 .674 .501 -.044 .090
X2 (TVY) .136 .164 .042 .827 .409 -.187 .459
X3 (TVY7) .165 .092 .089 1.800 .072 -.015 .346
X4 (TVPG) .072 .043 .075 1.664 .097 -.013 .158
X6 (TVMA) .141 .045 .150 3.153 .002 .053 .229
X7 (COMEDY) .139 .070 .165 1.993 .047 .002 .276
X8 (DRAMA) .158 .069 .188 2.283 .023 .022 .294
X9 (FAMILY) .103 .112 .060 .917 .359 -.118 .324
X10 (MYSTERY) .114 .090 .074 1.261 .208 -.064 .292
X11 (REALITYTV) .213 .084 .167 2.550 .011 .049 .378
X12 (ROMANCE) .150 .369 .018 .406 .685 -.575 .875
X13 (SCIFI) -.030 .079 -.026 -.377 .706 -.184 .125
Log Episodes .010 .032 .014 .309 .758 -.054 .073
X16 (CABLE) .189 .036 .246 5.212 .000 .118 .260
Log Minutes -.029 .042 -.036 -.689 .491 -.112 .054
a. DependentVariable:Y (SECOND SEASON)

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Durbin – Watson SPSS Output Model Summaryb
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
Durbin-
Watson
1 .370a
.137 .110 .3613 1.820
a. Predictors:(Constant), (CABLE), LOGEPISODES, (ROMANCE), (SCIFI), (MALE), (MYSTERY), (TVY), (TVPG),
(REALITYTV), LOGMINUTES, (TVMA), (TVY7), (COMEDY), (FAMILY), (DRAMA)
b. DependentVariable:Y (SECOND SEASON)
ANOVAa
SPSS Output Cochrane – Orcutt method.
The Cochrane-Orcuttestimation method is used.
Iteration History
Rho (AR1)
Durbin-Watson
Mean Squared
ErrorsValue Std. Error
0 .088 .045 1.992 .129
1 .100 .045 2.016 .129
2 .102 .045 2.019 .129
3a
.102 .045 2.020 .129
The Cochrane-Orcuttestimation method is used.
a. The estimation terminated atthis iteration,because all the parameter estimates changed by less than
.001.

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Park Test SPSS Output.
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 18.074 1 18.074 1.316 .252b
Residual 6841.214 498 13.737
Total 6859.288 499
a. DependentVariable:LnResSquared
b. Predictors: (Constant), Log Minutes
Binary Choice Results
Run MATRIX procedure:
Error encountered in source line # 211
Error # 12581
A division by zero has been attempted.
Execution of this command stops.
HC Method
3
Criterion Variable
YSECONDS
Model Fit:
R-sq F df1 df2 p
.1365 .6660 15.0000 484.0000 .8185
Heteroscedasticity-Consistent Regression Results
Coeff SE(HC) t P>|t|
Constant .6138 .5522 1.1116 .2669
X1MALE .0231 .0948 .2434 .8078
X2TVY .1360 .4552 .2987 .7653
X3TVY7 .1653 .2542 .6503 .5158
X4TVPG .0723 .1202 .6011 .5481
X6TVMA .1409 .1237 1.1391 .2552
X7COMEDY .1392 .1933 .7200 .4719
X8DRAMA .1578 .1914 .8247 .4099
X9FAMILY .1032 .3114 .3314 .7405
X10MYSTE .1140 .2501 .4556 .6489
X11REALI .2132 .2315 .9212 .3574

23
X12ROMAN .1499 1.0217 .1467 .8834
X13SCIFI -.0296 .2174 -.1362 .8917
LogEpiso .0100 .0895 .1115 .9112
X16CABLE .1889 .1003 1.8830 .0603
LogMinut -.0291 .1170 -.2490 .8035
Covariance Matrix of Parameter Estimates
Columns 1 - 12
Constant X1MALE X2TVY X3TVY7 X4TVPG X6TVMA
X7COMEDY X8DRAMA X9FAMILY X10MYSTE X11REALI
Constant .3049 -.0112 -.0044 -.0060 -.0051 -.0094
-.0452 -.0196 -.0437 -.0218 -.0335
X1MALE -.0112 .0090 .0000 -.0029 .0004 -.0007
.0015 .0009 .0007 .0015 .0005
X2TVY -.0044 .0000 .2072 .0254 .0064 .0022
.0006 -.0003 -.0592 -.0014 .0001
X3TVY7 -.0060 -.0029 .0254 .0646 .0045 .0043
.0016 .0004 -.0165 -.0007 .0030
X4TVPG -.0051 .0004 .0064 .0045 .0145 .0029
-.0008 -.0006 -.0039 -.0026 -.0020
X6TVMA -.0094 -.0007 .0022 .0043 .0029 .0153
.0029 .0011 .0043 .0035 .0064
X7COMEDY -.0452 .0015 .0006 .0016 -.0008 .0029
.0374 .0281 .0315 .0288 .0300
X8DRAMA -.0196 .0009 -.0003 .0004 -.0006 .0011
.0281 .0366 .0266 .0305 .0297
X9FAMILY -.0437 .0007 -.0592 -.0165 -.0039 .0043
.0315 .0266 .0970 .0272 .0309
X10MYSTE -.0218 .0015 -.0014 -.0007 -.0026 .0035
.0288 .0305 .0272 .0626 .0311
X11REALI -.0335 .0005 .0001 .0030 -.0020 .0064
.0300 .0297 .0309 .0311 .0536
X12ROMAN .0039 .0063 -.0002 -.0016 -.0011 -.0084
.0262 .0301 .0254 .0288 .0269
X13SCIFI -.0314 .0007 -.0043 -.0100 -.0020 .0024
.0294 .0286 .0336 .0295 .0297
LogEpiso -.0255 .0010 -.0018 -.0019 -.0003 .0018
.0005 .0009 -.0026 .0022 .0027
X16CABLE -.0063 .0004 -.0010 -.0041 .0016 -.0027
-.0005 -.0002 -.0047 .0002 -.0046
LogMinut -.0535 .0006 .0020 .0031 .0006 .0001
.0036 -.0034 .0062 -.0040 -.0006
Columns 13 - 16
X13SCIFI LogEpiso X16CABLE LogMinut
Constant -.0314 -.0255 -.0063 -.0535
X1MALE .0007 .0010 .0004 .0006
X2TVY -.0043 -.0018 -.0010 .0020

24
X3TVY7 -.0100 -.0019 -.0041 .0031
X4TVPG -.0020 -.0003 .0016 .0006
X6TVMA .0024 .0018 -.0027 .0001
X7COMEDY .0294 .0005 -.0005 .0036
X8DRAMA .0286 .0009 -.0002 -.0034
X9FAMILY .0336 -.0026 -.0047 .0062
X10MYSTE .0295 .0022 .0002 -.0040
X11REALI .0297 .0027 -.0046 -.0006
X12ROMAN .0271 -.0028 -.0032 -.0068
X13SCIFI .0472 .0003 -.0003 .0003
LogEpiso .0003 .0080 .0001 .0010
X16CABLE -.0003 .0001 .0101 .0005
LogMinut .0003 .0010 .0005 .0137
------ END MATRIX -----

Final Group Paper ECON 123 Group 10

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