Matching Items (17)

136435-Thumbnail Image.png

PREDICTIVE ANALYTICS IN HOTEL RESERVATIONS

Description

Service providers in the hotel industry are interested in identifying the factors that contribute to consumers' choice of hotel booking method. In an effort to determine these factors we used

Service providers in the hotel industry are interested in identifying the factors that contribute to consumers' choice of hotel booking method. In an effort to determine these factors we used the predictive analytic tool of logistic regression. In particular, we concentrated on the choice of booking directly on a hotel website as compared to a third-party website. We found that consumers with children were 2.94 times more likely to use a hotel's website. We found that consumers who place a high importance on cost were 1.42 times more likely to use a third-party website for booking a hotel. These results could be useful for hotel marketing and sales representatives to better understand the preferences of their customers and improve the hotel reservation services provided. Predicting consumer needs and choices have the potential to optimize sales and increase profits.

Contributors

Agent

Created

Date Created
  • 2015-05

A Study of Sun Devil Athletics’ Men’s Basketball with Information Related to Travel Partnership

Description

We created a sufficient database that can be used by the SDA for extensive analysis as well as a starting foundation for further development. The design of the database revolved

We created a sufficient database that can be used by the SDA for extensive analysis as well as a starting foundation for further development. The design of the database revolved around the men’s basketball team and includes data for conferences, teams, players, and the historic schedule of teams past performances. This design can be used as a template for future sports that would like to be added to the database. The queries we ran that tested the functionality of the database show the utility and accessibility that is possible with the data currently in the database. The visuals included assist our examples by exhibiting how the results gathered by the queries can be transformed into figures that may be more visually appealing than the raw data. We came up with example questions that could be potential questions the SDA may have regarding current and past performance statistics. We expect that as a continuation of this project, the SDA will be able to utilize it to their advantage to analyze and improve the performance levels of other teams.

Contributors

Agent

Created

Date Created
  • 2020-05

137105-Thumbnail Image.png

FOOT STRIKE AND INJURY RATES IN ENDURANCE RUNNERS: A RETROSPECTIVE STUDY- REVISITED

Description

Although the sport and exercise of running has a great amount of benefits to anyone's health, there is a chance of injury that can occur. There are many variables that

Although the sport and exercise of running has a great amount of benefits to anyone's health, there is a chance of injury that can occur. There are many variables that can contribute to running injury. However, because of the vast amount of footsteps a frequent runner takes during their average run, foot strike pattern is a significant factor to be investigated in running injury research. This study hypothesized that due to biomechanical factors, runners that exhibited a rear foot striking pattern would display a greater incidence of chronic lower extremity injury in comparison to forefoot striking counterparts. This hypothesis would support previous studies conducted on the topic. Student-athletes in the Arizona State University- Men's and Women's Track & Field program, specifically those who compete in distance events, were given self reporting surveys to provide injury history and had their foot strike patterns analyzed through video recordings. The survey and analysis of foot strike patterns resulted in data that mostly followed the hypothesized pattern of mid-foot and forefoot striking runners displaying a lower average frequency of injury in comparison to rear foot strikers. The differences in these averages across all injury categories was found to be statistically significant. One category that displayed the most supportive results was in the average frequency of mild injury. This lead to the proposed idea that while foot strike patterns may not be the best predictor of moderate and severe injuries, they may play a greater role in the origin of mild injury. Such injuries can be the gateway to more serious injury (moderate and severe) that are more likely to have their cause in other sources such as genetics or body composition for example. This study did support the idea that foot strike pattern can be the main predictor in incidence of running injuries, but also displayed that it is one of many major factors that contribute to injuries in runners.

Contributors

Agent

Created

Date Created
  • 2014-05

147645-Thumbnail Image.png

Using Logistic Regression to Predict Stock Trends Based on Bag-of-Words Representations of News Article Headlines

Description

We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a

We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones Industrial Average. The results showed that a tri-gram bag led to a 49% trend accuracy, a 1% increase when compared to the single-gram representation’s accuracy of 48%.

Contributors

Agent

Created

Date Created
  • 2021-05

134937-Thumbnail Image.png

The Value Added of the ASU Spirit Squad to Sun Devil Athletics

Description

Several studies on cheerleading as a sport can be found in the literature; however, there is no research done on the value added to the experience at a university, to

Several studies on cheerleading as a sport can be found in the literature; however, there is no research done on the value added to the experience at a university, to an athletic department or at a particular sport. It has been the feeling that collegiate and professional cheerleaders are not given the appropriate recognition nor credit for the amount of work they do. This contribution is sometimes in question as it depends on the school and the sports teams. The benefits are believed to vary based on the university or professional teams. This research investigated how collegiate cheerleaders and dancers add value to the university sport experience. We interviewed key personnel at the university and conference level and polled spectators at sporting events such as basketball and football. We found that the university administration and athletic personnel see the ASU Spirit Squad as value added but spectators had a totally different perspective. The university acknowledges the added value of the Spirit Squad and its necessity. Spectators attend ASU sporting events to support the university and for the entertainment. They enjoy watching the ASU Spirit Squad perform but would continue to attend ASU sporting events even if cheerleaders and dancers were not there.

Contributors

Created

Date Created
  • 2017-05

148455-Thumbnail Image.png

THE IMPACT OF RACE AND OTHER LARGE-SCALE PREDICTORS ON THE INCIDENCE OF MELANOMA SKIN CANCER-A BIOSTATISTICAL ANALYSIS

Description

Melanoma is one of the most severe forms of skin cancer and can be life-threatening due to metastasis if not caught early on in its development. Over the past decade,

Melanoma is one of the most severe forms of skin cancer and can be life-threatening due to metastasis if not caught early on in its development. Over the past decade, the U.S. Government added a Healthy People 2020 objective to reduce the melanoma skin cancer rate in the U.S. population. Now that the decade has come to a close, this research investigates possible large-scale risk factors that could lead to incidence of melanoma in the population using logistic regression and propensity score matching. Logistic regression results showed that Caucasians are 14.765 times more likely to get melanoma compared to non-Caucasians; however, after adjustment using propensity scoring, this value was adjusted to 11.605 times more likely for Caucasians than non-Caucasians. Cholesterol, Chronic Obstructive Pulmonary Disease, and Hypertension predictors also showed significance in the initial logistic regression. By using the results found in this experiment, the door has been opened for further analysis of larger-scale predictors and gives public health programs the initial information needed to create successful skin safety advocacy plans.

Contributors

Created

Date Created
  • 2021-05

136078-Thumbnail Image.png

Marketing in the Third Wave of Democratization

Description

During the Third Wave of Democratization, the United States has influenced many different cultures through politics and social interests. The way in which this has occurred is through their marketing

During the Third Wave of Democratization, the United States has influenced many different cultures through politics and social interests. The way in which this has occurred is through their marketing and advertising. Many companies are the reason that the United States is a super power today.

Contributors

Agent

Created

Date Created
  • 2015-05

129025-Thumbnail Image.png

Impact of communities, health, and emotional-related factors on smoking use: comparison of joint modeling of mean and dispersion and Bayes’ hierarchical models on add health survey

Description

Background
The analysis of correlated binary data is commonly addressed through the use of conditional models with random effects included in the systematic component as opposed to generalized estimating equations

Background
The analysis of correlated binary data is commonly addressed through the use of conditional models with random effects included in the systematic component as opposed to generalized estimating equations (GEE) models that addressed the random component. Since the joint distribution of the observations is usually unknown, the conditional distribution is a natural approach. Our objective was to compare the fit of different binary models for correlated data in Tabaco use. We advocate that the joint modeling of the mean and dispersion may be at times just as adequate. We assessed the ability of these models to account for the intraclass correlation. In so doing, we concentrated on fitting logistic regression models to address smoking behaviors.
Methods
Frequentist and Bayes’ hierarchical models were used to predict conditional probabilities, and the joint modeling (GLM and GAM) models were used to predict marginal probabilities. These models were fitted to National Longitudinal Study of Adolescent to Adult Health (Add Health) data for Tabaco use.
Results
We found that people were less likely to smoke if they had higher income, high school or higher education and religious. Individuals were more likely to smoke if they had abused drug or alcohol, spent more time on TV and video games, and been arrested. Moreover, individuals who drank alcohol early in life were more likely to be a regular smoker. Children who experienced mistreatment from their parents were more likely to use Tabaco regularly.
Conclusions
The joint modeling of the mean and dispersion models offered a flexible and meaningful method of addressing the intraclass correlation. They do not require one to identify random effects nor distinguish from one level of the hierarchy to the other. Moreover, once one can identify the significant random effects, one can obtain similar results to the random coefficient models. We found that the set of marginal models accounting for extravariation through the additional dispersion submodel produced similar results with regards to inferences and predictions. Moreover, both marginal and conditional models demonstrated similar predictive power.

Contributors

Agent

Created

Date Created
  • 2017-02-03

132857-Thumbnail Image.png

Predictive Modeling of 4th Down Selection in Power 5 Conference: Data Analytics

Description

Predictive analytics have been used in a wide variety of settings, including healthcare,
sports, banking, and other disciplines. We use predictive analytics and modeling to
determine the impact

Predictive analytics have been used in a wide variety of settings, including healthcare,
sports, banking, and other disciplines. We use predictive analytics and modeling to
determine the impact of certain factors that increase the probability of a successful
fourth down conversion in the Power 5 conferences. The logistic regression models
predict the likelihood of going for fourth down with a 64% or more probability based on
2015-17 data obtained from ESPN’s college football API. Offense type though important
but non-measurable was incorporated as a random effect. We found that distance to go,
play type, field position, and week of the season were key leading covariates in
predictability. On average, our model performed as much as 14% better than coaches
in 2018.

Contributors

Created

Date Created
  • 2019-05

132858-Thumbnail Image.png

Predictive Modeling of 4th Down Selection in Power 5 Conference: Data Analytics

Description

Predictive analytics have been used in a wide variety of settings, including healthcare, sports, banking, and other disciplines. We use predictive analytics and modeling to determine the impact of certain

Predictive analytics have been used in a wide variety of settings, including healthcare, sports, banking, and other disciplines. We use predictive analytics and modeling to determine the impact of certain factors that increase the probability of a successful fourth down conversion in the Power 5 conferences. The logistic regression models predict the likelihood of going for fourth down with a 64% or more probability based on 2015-17 data obtained from ESPN’s college football API. Offense type though important but non-measurable was incorporated as a random effect. We found that distance to go, play type, field position, and week of the season were key leading covariates in predictability. On average, our model performed as much as 14% better than coaches in 2018.

Contributors

Agent

Created

Date Created
  • 2019-05