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This document provides details on three different trouser options for men: 1) off-white trousers that are available in waist sizes 28-38 and cost £12.99-£15.99; 2) cream-colored trousers available in waist sizes 28-38 and 36-38 for £11.99-£15.99; and 3) silver metallic trousers available in sizes XS to XL for £22.99.
This document contains a collection name of "Spring sport coats 2013" and provides details of various sport coat combinations including the jacket, shirt, trouser, tie, button, and thread for each combination. In total there are 9 different combinations listed with colors ranging from medium blue and tan windowpane to navy sharkskin and blue/ivory check. Each combination provides the style number or name for the jacket, shirt, trouser, tie, button, and thread.
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Dynamic and variable ticket pricing strategies allow universities to alter ticket prices in an attempt to balance revenue generation between primary and secondary ticket markets. Dynamic ticket pricing sets prices based on an algorithm and fluctuating secondary market values, while variable ticket pricing predetermines prices for individual games based on factors like opponent and date. Some universities like Penn State have had success explaining variable pricing changes to fans, while others like Michigan faced backlash. Fresno State saw increased revenue from implementing dynamic pricing. Achieving the perfect balance requires maximizing primary market revenue while allowing enough secondary market activity to analyze pricing effectiveness, without angering fans.
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Ticketmaster is the world leader in selling tickets. After more than a decade of developing applications extensively on Oracle and MySQL, Ticketmaster made the move to MongoDB. The reasons for the move are generally in line with those of other companies – increased flexibility and performance, and decreased costs and time-to-market. In this session we’ll discuss how the conversion to MongoDB went at Ticketmaster and we’ll take a deeper dive into some of the successes and set-backs that we faced. We’ll give an overview of the MongoDB topology at Ticketmaster, discuss exactly what data we’re writing to MongoDB and comment on the MongoDB support model that we’re using. We’ll also touch on the transition from relational DBA to NoSQL DBA at Ticketmaster.
The document is a letter from a strategic consultant to the CEO of Ticketmaster recommending that Ticketmaster acquire Live Nation. The summary identifies that the consultant conducted an analysis of Ticketmaster, identified three strategic problems around expanding globally, simplifying its value chain, and developing defenses against competitors. The consultant recommends that acquiring Live Nation would help Ticketmaster address these problems by providing global expansion opportunities, simplifying the value chain by eliminating vendors, and gaining a shared fan base and sponsors to strengthen its competitive position.
I was the group leader in this project to analysis the Broadway industry, conduct primary & secondary research and provide recommendations for Broadway. The leave behind was submitted for a Marketing Research course.
Danielle Jabin is a data engineer at Spotify who works on A/B testing infrastructure. She describes Spotify's big data landscape, which includes over 40 million active users generating 1.5 TB of compressed data per day. Spotify collects this user data using Kafka for high-volume data collection, processes it using Hadoop on a large cluster, and stores aggregates in databases like PostgreSQL and Cassandra for analytics and visualization.
The document discusses different data structures and their implementations and applications. It covers arrays, linked lists, stacks, queues, binary trees, and binary search. The key points are:
- Arrays allow fast access but have fixed size; linked lists can grow dynamically but access is slower.
- Binary trees allow fast (O(log n)) search, insertion, and deletion operations due to their hierarchical structure.
- Stacks and queues are useful for modeling LIFO and FIFO data access with applications like function calls and job scheduling.
- Binary search runs in O(log n) time by recursively dividing the search space for sorted data.
The Top Skills That Can Get You Hired in 2017LinkedIn
We analyzed all the recruiting activity on LinkedIn this year and identified the Top Skills employers seek. Starting Oct 24, learn these skills and much more for free during the Week of Learning.
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3. 1. Introduction
Does average annual percentage of wins affect NFL season ticket pricing?
Does being a SuperBowl Champion affect season ticket prices?
Does being a consistent AFC or NFC division Champion affect season ticket
prices?
Are tickets more expensive in the NFC or the AFC?
Does household income affect ticket pricing?
2. Data
Dependent Variable:
The dependent variable is the average season ticket price for each team
between the years of 2012-2014 and is named TPrice in the regression. Its unit
of measure is U.S. dollars.
Independent Variable:
Average winning percentage between the years 2011-2013 (APW) measured as
a decimal.
Median Household income for the years 2011-2014 (MHI) measured in US
Dollars.
AFC or NFC (Conf) measured as 1 = AFC and 0 = NFC.
Number of Conference Championships (WConf) measured in integers.
SuperBowl Champion (SuperBowl) between the years 2011-2013 measured as
1 = yes and 0 = no.
Data Sources:
http://espn.go.com/nfl/standings
http://quickfacts.census.gov/qfd/states
http://www.fancostexperience.com
http://seatgeek.com/nfl-tickets/
4. 3. Regression Estimation Results and Final Model Evaluation
Source | SS df MS Number of obs = 32
-------------+------------------------------ F( 5, 26) = 9.83
Model | 56563.797 5 11312.7594 Prob > F = 0.0000
Residual | 29917.8263 26 1150.68563 R-squared = 0.6541
-------------+------------------------------ Adj R-squared = 0.5875
Total | 86481.6233 31 2789.72978 Root MSE = 33.922
------------------------------------------------------------------------------
Tprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
APW | 241.3388 68.73639 3.51 0.002 100.0492 382.6285
MHI | .002451 .0009807 2.50 0.019 .0004352 .0044667
Conf | -36.45062 12.39974 -2.94 0.007 -61.93866 -10.96259
Wconf | -2.721103 10.24442 -0.27 0.793 -23.7788 18.3366
SuperBowl | -26.67645 23.48798 -1.14 0.266 -74.95668 21.60379
_cons | -89.57484 56.40553 -1.59 0.124 -205.5181 26.36838
------------------------------------------------------------------------------
F-statistic
o The F-stat provided was 9.83, which is significant at the .05 level because
this value is much greater than 2.59.
R Squared & Adjusted R Squared
o The model as a whole showed an R-squared of 0.6541 which means the
variables used explain about 65.41% of the variation in season ticket prices
for NFL teams. However, the adjusted R^2 was lower than R^2 because the
regression model was penalized for using two variables that were
insignificant, Wconf and SuperBowl.
T-statistics measured at the critical value 1.96 & -1.96 (5% significance
level)
o The t-stat for average annual percentage of wins in a season (APW) was
3.51. The t-stat for APW is > 1.96 and is therefore significant to the model
at the 5% level.
o The median household income (MHI) had a t-stat of 2.50, which is significant
at the 5% level because 2.50 > 1.96.
o The conference (Conf), either AFC or NFC, the NFL team participates in had
a t-stat of -2.94, which is significant at the 5% level because -2.94 > -1.96.
o The t-stats for Wconf and Superbowl were -0.27 and -1.14, respectively, and
were insignificant at the 5% level because both values are less than the
critical value -1.96.
Interpretation of model coefficients:
o Holding all other x’s constant, a one unit change in APW will result in a
$241.34 rise in Tprice.
o Holding all other x’s constant, a one unit change in MHI will result in a
$0.002 rise in Tprice.
o Holding all other x’s constant, a one unit change in Conf will result in a
$36.45 fall in Tprice.
5. Questions Answered
o Does average annual percentage of wins (APW) affect NFL season ticket
pricing?
Yes, a high APW is positively correlated with an increase in season
ticket prices.
o Does being a SuperBowl Champion affect season ticket prices?
No, the regression revealed that being a SuperBowl Champion is
insignificant in regards to season ticket pricing.
o Does being a consistent AFC or NFC division champion affect season ticket
prices?
No, the regression revealed that being a consistent AFC or NFC
division champion is insignificant in regards to season ticket pricing.
o Are tickets more expensive in the NFC or the AFC?
We found the mean of Tprice to be higher if the NFL team participates
in the NFC Division rather than the AFC Division. (See results below,
NFC=0; AFC=1)
. summarize Tprice if Conf==0
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
Tprice | 16 159.0104 50.99176 79.79 278.3033
. summarize Tprice if Conf==1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
Tprice | 16 121.8392 49.27854 64.51667 230.83
o Does household income affect ticket pricing?
Yes, a higher household income yields a higher average season ticket
price.
3. Summary:
o We wanted to consider factors that may affect ticket prices for future season
ticket pricing for all 32 NFL teams. Five variables will be further analyzed
and tested to determine which of those variables have the most significant
effect on season ticket pricing. Collectively, data was collected from the
years 2011 to 2014. Regression analysis was used to analyze the data.
o The model examined the effect of all the variables that were obtained in this
research project on NFL season ticket prices. The model contained five
independent variables. Among those variables, three were significant at the
5% level. The significant variables were average annual percentage of wins
in a season, median household income, and whether the NFL team was in
the AFC or NFC conference. Alternatively, the two other variables used in the
regression, number of wins in a conference and Super Bowl wins, were not
significant at the 5% level which is surprising because one would think that
this would hold true in affecting future season ticket prices.