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

“Next Time on….” An Econometric Regression on How Television Shows are Renewed for an Additional Season.

Econ 123 Econometric Project, Group 10Ismael 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 2


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 3


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 4


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 5


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 6


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 7


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 · TVY 7−B4 ·TVPG−B5 · TV 14+¿B6 · TVMA+B7 ·COMEDY +B8 · DRAMA+B9 ·FAMILY +B10 · MYSTERY +¿

B11 ·REALITY +B12 · ROMANCE−B13 · SCIFI+B14 · EPISODES−B15 · BROADCAST +B16 · CABLE+B17 · MINUTESTo 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, R2can 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 9


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. H 0 : ρ ≤0 H A : 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, 10


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 11


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 12


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

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .383a .147 .116 .3599

15

GENRE TV SHOWS COMEDY 146

DRAMA 147

FAMILY 26

MYSTERY 33

REALITY TV 50

ROMANCE 1

SCIFI 62

RATINGS TV SHOWSTVY 7

TVY7 22TVPG 99TV14 241TVMA 105

http://www.tvguidelines.org/ratings.htm


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. Dependent Variable: 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

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

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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


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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Regression with TV14 and BROADCAST Omitted.

Model Summary

Model R R Square Adjusted R Square

Std. Error of the

Estimate

1 .370a .137 .110 .3613

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Descriptive Statistics

Mean Std. Deviation N

Y (SECOND SEASON) .822 .3829 500

Log Episodes 2.5231 .55057 500

Log Minutes 3.6442 .46800 500


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


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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. Dependent Variable: Y (SECOND SEASON)

ANOVAa

SPSS Output Cochrane – Orcutt method.The Cochrane-Orcutt estimation 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-Orcutt estimation method is used.

a. The estimation terminated at this iteration, because all the parameter estimates changed by less

than .001.

Park Test SPSS Output.

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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. Dependent Variable: LnResSquared

b. Predictors: (Constant), Log Minutes

Binary Choice Results

Run MATRIX procedure:Error encountered in source line # 211

Error # 12581A 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 .2669X1MALE .0231 .0948 .2434 .8078X2TVY .1360 .4552 .2987 .7653X3TVY7 .1653 .2542 .6503 .5158X4TVPG .0723 .1202 .6011 .5481X6TVMA .1409 .1237 1.1391 .2552X7COMEDY .1392 .1933 .7200 .4719X8DRAMA .1578 .1914 .8247 .4099X9FAMILY .1032 .3114 .3314 .7405X10MYSTE .1140 .2501 .4556 .6489X11REALI .2132 .2315 .9212 .3574X12ROMAN .1499 1.0217 .1467 .8834X13SCIFI -.0296 .2174 -.1362 .8917LogEpiso .0100 .0895 .1115 .9112

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X16CABLE .1889 .1003 1.8830 .0603LogMinut -.0291 .1170 -.2490 .8035

Covariance Matrix of Parameter EstimatesColumns 1 - 12 Constant X1MALE X2TVY X3TVY7 X4TVPG X6TVMA X7COMEDY X8DRAMA X9FAMILY X10MYSTE X11REALIConstant .3049 -.0112 -.0044 -.0060 -.0051 -.0094 -.0452 -.0196 -.0437 -.0218 -.0335X1MALE -.0112 .0090 .0000 -.0029 .0004 -.0007 .0015 .0009 .0007 .0015 .0005X2TVY -.0044 .0000 .2072 .0254 .0064 .0022 .0006 -.0003 -.0592 -.0014 .0001X3TVY7 -.0060 -.0029 .0254 .0646 .0045 .0043 .0016 .0004 -.0165 -.0007 .0030X4TVPG -.0051 .0004 .0064 .0045 .0145 .0029 -.0008 -.0006 -.0039 -.0026 -.0020X6TVMA -.0094 -.0007 .0022 .0043 .0029 .0153 .0029 .0011 .0043 .0035 .0064X7COMEDY -.0452 .0015 .0006 .0016 -.0008 .0029 .0374 .0281 .0315 .0288 .0300X8DRAMA -.0196 .0009 -.0003 .0004 -.0006 .0011 .0281 .0366 .0266 .0305 .0297X9FAMILY -.0437 .0007 -.0592 -.0165 -.0039 .0043 .0315 .0266 .0970 .0272 .0309X10MYSTE -.0218 .0015 -.0014 -.0007 -.0026 .0035 .0288 .0305 .0272 .0626 .0311X11REALI -.0335 .0005 .0001 .0030 -.0020 .0064 .0300 .0297 .0309 .0311 .0536X12ROMAN .0039 .0063 -.0002 -.0016 -.0011 -.0084 .0262 .0301 .0254 .0288 .0269X13SCIFI -.0314 .0007 -.0043 -.0100 -.0020 .0024 .0294 .0286 .0336 .0295 .0297LogEpiso -.0255 .0010 -.0018 -.0019 -.0003 .0018 .0005 .0009 -.0026 .0022 .0027X16CABLE -.0063 .0004 -.0010 -.0041 .0016 -.0027 -.0005 -.0002 -.0047 .0002 -.0046LogMinut -.0535 .0006 .0020 .0031 .0006 .0001 .0036 -.0034 .0062 -.0040 -.0006Columns 13 - 16 X13SCIFI LogEpiso X16CABLE LogMinutConstant -.0314 -.0255 -.0063 -.0535X1MALE .0007 .0010 .0004 .0006X2TVY -.0043 -.0018 -.0010 .0020X3TVY7 -.0100 -.0019 -.0041 .0031X4TVPG -.0020 -.0003 .0016 .0006X6TVMA .0024 .0018 -.0027 .0001

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X7COMEDY .0294 .0005 -.0005 .0036X8DRAMA .0286 .0009 -.0002 -.0034X9FAMILY .0336 -.0026 -.0047 .0062X10MYSTE .0295 .0022 .0002 -.0040X11REALI .0297 .0027 -.0046 -.0006X12ROMAN .0271 -.0028 -.0032 -.0068X13SCIFI .0472 .0003 -.0003 .0003LogEpiso .0003 .0080 .0001 .0010X16CABLE -.0003 .0001 .0101 .0005LogMinut .0003 .0010 .0005 .0137

------ END MATRIX -----

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final group paper econ 123 group 10

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