Apax Researchers, Downtown Kissimmee, FL (2026)

02/27/2025

For each question, write your essay with the following structure.

Introduction
Summary of the information and facts in the readings, lecture, and class discussions.
Your critical analysis
Conclusion

The Early Years of Sports and Television in the US and Europe: Compare and contrast the development of sports broadcasting in the United States and Europe during the early years of television. How did cultural, technological, and economic factors shape the relationship between sports and television in these regions. (35 points)

The Political Economy of Global Television Broadcasting: Analyze the political and economic factors that have influenced the globalization of sports broadcasting. How have media policies, market dynamics, and technological advancements contributed to expanding sports media industries worldwide? (35 points)

Sports Broadcasting Economics and Media National Policies: Discuss the relationship between sports broadcasting economics and national media policies. How do government regulations and market forces interact to shape the broadcasting landscape, particularly in the context of major sporting events? (35 points)

Sports Fans, Sponsorship, and Broadcasting Nexus: Examine the interconnected roles of sports fans, sponsors, and broadcasters in the modern sports media industry. How do these stakeholders influence each other, and how does this dynamic impact the commercialization and globalization of sports? (35 points)

Expert answer

DRAFT / STUDY TIPS: How Have Cultural, Technological, and Economic Factors Shaped the Global Evolution of Sports Broadcasting? A Critical Analysis of Historical Development, Political Economy, National Policies, and Stakeholder Dynamics

The Early Years of Sports and Television in the US and Europe: How Did Cultural, Technological, and Economic Factors Shape the Relationship Between Sports and Television?
Introduction
The emergence of television in the mid-20th century reshaped the sports industry, transforming local competitions into national spectacles and global events. However, the evolution of sports broadcasting followed distinct trajectories in the United States and Europe, shaped by cultural values, technological progress, and economic models. Exploring these differences provides insight into the global sports media landscape we see today, where commercial interests, public service obligations, and fan experiences intersect.

Historical Context: The Birth of Sports Broadcasting
In the United States, the first televised sporting event aired in 1939 — a college baseball game on NBC. By the 1950s, television networks recognized the commercial power of live sports, with leagues like the NFL, MLB, and NBA strategically negotiating broadcast deals. The Super Bowl’s debut in 1967 cemented sports as prime-time entertainment, blending athletic competition with advertising and celebrity culture.

Meanwhile, in Europe, public service broadcasters like the BBC (UK) and ARD (Germany) dominated early sports coverage. Events like the FA Cup Final and the Olympics were broadcast free-to-air, emphasizing accessibility and national unity over profit. The priority was to serve public interests, not to maximize revenue.

Cultural Influences on Broadcasting Models
Cultural values played a central role in shaping sports media. In the US, sports were quickly woven into consumer culture. Athletes like Muhammad Ali and Joe Namath became larger-than-life figures, and television broadcasts amplified their personalities to create compelling storylines. Networks packaged games as entertainment products, using commercial breaks to sell everything from soda to sneakers.

In contrast, European sports maintained stronger ties to local and national identities. Football clubs often represented working-class communities or regional identities — for example, FC Barcelona symbolized Catalan nationalism. Broadcasters celebrated this communal aspect, focusing coverage on collective experiences rather than individual star power.

Even commentary styles reflected these differences. American broadcasters adopted high-energy, personality-driven coverage, while European commentators leaned toward a more reserved, analytical approach. The cultural divergence influenced how sports were marketed, consumed, and understood by audiences.

Technological Innovations and Broadcasting Strategies
Technological advancements were critical in shaping sports broadcasting. The US led the way with innovations like instant replay (introduced by CBS in 1963), slow-motion, and multi-camera setups. These techniques enhanced the viewing experience, turning games into polished spectacles. The development of cable TV and satellite broadcasting further expanded reach, with ESPN launching as a 24/7 sports network in 1979.

In Europe, technological progress was initially slower, but satellite broadcasting in the 1960s and 1970s helped unify fragmented markets. The European Broadcasting Union (EBU) coordinated cross-border coverage of events like the Olympics, fostering a pan-European sports culture. By the late 20th century, networks like Sky Sports used satellite technology to disrupt public broadcasters' dominance, introducing pay-TV models that mirrored US practices.

Economic Structures and Commercialization
Economic factors drove the commercialization of sports in both regions, albeit on different timelines. In the US, commercial networks relied on advertising revenue, which fueled intense competition for sports rights. The NFL’s $9.3 million deal with CBS in 1962 marked the beginning of an escalating arms race for broadcast rights, culminating in the Super Bowl becoming the most-watched television event annually.

European public broadcasters, funded through license fees, initially resisted this commercialization. Their mission was to make major sporting events accessible to all citizens. However, the rise of private networks in the 1980s and 1990s — such as Rupert Murdoch’s Sky Sports — changed the game. Sky’s landmark deal to broadcast the English Premier League in 1992 for £304 million introduced subscription-based models, prioritizing revenue generation over universal access.

The shift toward pay-TV reflected a broader global trend: the monetization of sports media rights. Today, elite leagues generate billions from international broadcasting deals, though this has sparked debates about sports' growing inaccessibility for lower-income fans.

Case Studies: The Olympics and FIFA World Cup
The Olympics and FIFA World Cup illustrate these contrasting approaches. In the US, NBC’s Olympic coverage is meticulously packaged for ratings, with events delayed for prime-time slots and narratives crafted around American athletes. This strategy maximizes advertising revenue but often frustrates viewers who want real-time coverage.

In Europe, the World Cup has traditionally aired on public broadcasters, with matches seen as communal events. Even as pay-TV networks gained prominence, regulations in many countries required major sporting events to remain on free-to-air channels, safeguarding public access. However, the growing cost of rights fees has strained this model, leading to hybrid arrangements where some games are exclusive to subscription services.

How Have Cultural, Technological, and Economic Factors Shaped the Global Evolution of Sports Broadcasting? A Critical Analysis of Historical Development, Political Economy, National Policies, and Stakeholder Dynamics

02/27/2025

Questions....
Describe your assigned client’s situation. Why are they presenting to the clinic? What medications are they currently taking?
Assess the applicable clinical practice guideline (CPG) for your assigned client linked on the same page in the lesson where the client case is located. What treatment is recommended by the CPG for your client’s situation?
Discuss your personal professional assessment of the client’s situation provided in the scenario. What pharmacological treatment is necessary and why?
Reflect on additional questions you have about your assigned client that may influence treatment. What else do you need to know? What follow-up assessments, labs, or conversations are required to ensure optimal health outcomes.
One scholarly source must be used and APA format.

Scenario...
Lalisa Makok, a 55-year-old female client (DOB: 12/5/1969), presents to the clinic complaining of frequent heartburn and nausea daily, beginning after omeprazole was discontinued one month ago. She was originally seen by the NP complaining of these symptoms and prescribed omeprazole for an eight-week trial. During the eight-week trial, her symptoms resolved. Since the omeprazole was discontinued one month ago, the client has been experiencing daily heartburn and nausea. According to the CPG, the NP decides to send the client for a diagnostic endoscopy. The NP also considers what medications to prescribe to the client to relieve her symptoms.

Past Medical History: High Cholesterol

Allergies: Penicillin

Medications: rosuvastatin (Crestor) 20mg PO daily

Social History: She has never smoked ci******es and drinks wine once per month.

Physical Exam:

Height: 5 feet 2 inches
Weight: 154 lbs
Body Mass Index (BMI): 28.2
Blood Pressure (BP): 114/68
Heart Rate (HR): 63
Respiratory Rate (RR): 17
Oxygen Saturation (O2 Sat): 96% on RA
Temperature (TEMP): 98.7 oral

Expert answer

DRAFT / STUDY TIPS: Is Long-Term Proton Pump Inhibitor Use Necessary for Managing Chronic Gastroesophageal Reflux Disease? A Critical Analysis of Clinical Practice Guidelines and Pharmacological Treatment
Introduction
Gastroesophageal reflux disease (GERD) is a prevalent condition characterized by persistent acid reflux, leading to symptoms such as heartburn and nausea. Patients frequently require pharmacological intervention to manage symptoms effectively. The case of Lalisa Makok, a 55-year-old female experiencing daily heartburn and nausea following the discontinuation of omeprazole, raises important questions regarding long-term proton pump inhibitor (PPI) use, clinical practice guidelines (CPGs) for GERD management, and the appropriate pharmacological interventions to ensure symptom relief while minimizing potential risks. This paper critically examines the CPG recommendations, evaluates the pharmacological treatment options, and reflects on the necessary follow-up assessments for optimal patient care.

Patient Case Analysis
Lalisa Makok presents to the clinic with recurrent daily heartburn and nausea, which began after stopping an eight-week omeprazole trial. Her medical history includes high cholesterol, managed with rosuvastatin. She has no history of smoking, drinks alcohol minimally, and has a BMI of 28.2, placing her in the overweight category. Her vital signs are stable, with a blood pressure of 114/68 mmHg and a heart rate of 63 bpm. Given her symptoms' recurrence after discontinuation of omeprazole, the NP has decided to send her for a diagnostic endoscopy, following the CPG recommendations.

Clinical Practice Guideline Recommendations
The current American College of Gastroenterology (ACG) guidelines for GERD management emphasize the stepwise approach to treatment, starting with lifestyle modifications and pharmacotherapy as needed. PPIs, such as omeprazole, are the most effective treatment for GERD, particularly in patients with persistent symptoms. The guidelines recommend an initial eight-week course of PPIs, after which patients should be assessed for symptom recurrence. If symptoms return upon discontinuation, long-term PPI therapy or alternative treatments, such as H2-receptor antagonists (H2RAs), may be considered (Katz et al., 2022).

Additionally, the American Gastroenterological Association (AGA) 2022 guidelines recommend endoscopic evaluation for patients with recurrent GERD symptoms after stopping PPIs, particularly if symptoms are frequent and impact quality of life. This aligns with the NP’s decision to refer Lalisa for a diagnostic endoscopy. The CPG also suggests a step-down approach, where PPIs are tapered instead of abruptly discontinued, or a switch to H2RAs may be explored to reduce rebound acid hypersecretion (Spechler et al., 2021).

Pharmacological Treatment Considerations
Given Lalisa’s symptom recurrence after discontinuing omeprazole, a pharmacological approach is necessary to manage her condition effectively.

1. Proton Pump Inhibitors (PPIs) as the Primary Treatment
PPIs are the most effective therapy for GERD, reducing gastric acid secretion by inhibiting the H+/K+ ATPase enzyme in the stomach lining. Omeprazole effectively controlled Lalisa’s symptoms during the initial trial, indicating that PPI therapy was beneficial for her. Given her symptom recurrence, a long-term, lowest-effective-dose approach could be considered to balance efficacy and minimize potential risks, such as osteoporosis, vitamin B12 deficiency, and kidney disease associated with prolonged PPI use (Vaezi et al., 2018).

2. H2-Receptor Antagonists (H2RAs) as an Alternative
If long-term PPI use is not preferred, H2RAs such as famotidine could be an alternative. H2RAs are less potent than PPIs but still provide symptomatic relief by blocking histamine receptors in the stomach. However, they may be less effective for severe GERD and are associated with tolerance over time (Scarpignato et al., 2016). Given Lalisa’s persistent symptoms, H2RAs may be considered if she prefers to avoid long-term PPI therapy.

3. Prokinetic Agents and Other Adjunct Therapies
Prokinetic agents, such as metoclopramide, may be useful in GERD patients with delayed gastric emptying, but their side effects (e.g., tardive dyskinesia) often limit their use. Other adjunctive measures, such as alginates (e.g., Gaviscon), may provide symptomatic relief by forming a protective barrier against acid reflux.

Professional Assessment and Recommendations
Lalisa’s case suggests PPI therapy was effective, but abrupt discontinuation led to symptom relapse, likely due to rebound acid hypersecretion. Given the ACG and AGA recommendations, the following pharmacological management plan is proposed:

Restart a PPI (e.g., omeprazole 20 mg once daily) for long-term symptom control. The lowest effective dose should be used to minimize adverse effects.
Gradual tapering of PPI therapy should be considered if discontinuation is attempted again in the future. A step-down approach, transitioning to H2RAs or alternate-day PPI dosing, may prevent rebound acid hypersecretion.
Consider adjunct therapy with alginates (e.g., Gaviscon) or H2RAs (famotidine) as needed. This may help in cases of breakthrough symptoms.
Lifestyle modifications should be reinforced, including:
Avoiding late-night meals and lying down immediately after eating.
Limiting dietary triggers such as spicy foods, caffeine, and alcohol.
Weight management strategies, given her BMI of 28.2.

Is Long-Term Proton Pump Inhibitor Use Necessary for Managing Chronic Gastroesophageal Reflux Disease? A Critical Analysis of Clinical Practice Guidelines and Pharmacological Treatment

02/16/2025

Drawing upon relevant theory, how does personal data shape and influence our everyday lives, and what opportunities or challenges arise from this? Choose one or two case studies to analyze this question in detail.

Drawing upon relevant theory and examples, examine how platforms and devices have been built to capture and monetize our personal data, and how this influences our everyday lives. Consider the ethical implications of this.

Drawing upon relevant theory and examples, examine the privacy challenges around the control and ownership of personal data. Consider the ethical implications of this.

https://youtu.be/87D_wOEELFQ

Expert answer

DRAFT / STUDY TIPS:
How Does Personal Data Shape and Influence Everyday Life? Opportunities and Challenges in the Digital Age

Introduction
The proliferation of digital technologies has led to an unprecedented collection, analysis, and utilization of personal data. Every online transaction, social media interaction, and smart device usage contributes to an expansive data ecosystem that shapes individuals' experiences and decisions. This essay critically examines how personal data influences everyday life, exploring both opportunities and challenges associated with its usage. It draws upon theories such as surveillance capitalism (Zuboff, 2019) and data colonialism (Couldry & Mejias, 2019) while analyzing case studies on targeted advertising and predictive policing.

The Role of Personal Data in Everyday Life
Personal data influences various aspects of modern living, from consumer behavior to governance. The pervasive nature of data collection allows businesses and institutions to optimize services, improve customer experiences, and enhance operational efficiency. However, it also raises concerns about autonomy, privacy, and ethical implications.

1. Consumer Behavior and Targeted Advertising
Targeted advertising exemplifies how personal data shapes individual choices. Companies leverage vast datasets, including browsing history, purchase behavior, and demographic information, to personalize advertisements (Turow, 2011). Through machine learning algorithms, platforms like Google and Facebook predict user preferences and tailor content accordingly. Studies show that personalized ads increase consumer engagement and conversion rates significantly (Lambrecht & Tucker, 2013). However, critics argue that excessive personalization narrows exposure to diverse perspectives, reinforcing filter bubbles (Pariser, 2011).

Moreover, the ethical concerns surrounding data collection practices persist. The Cambridge Analytica scandal highlighted how political entities manipulate personal data to influence voter behavior (Cadwalladr & Graham-Harrison, 2018). This case underscores the dangers of unregulated data usage and its impact on democracy.

2. Predictive Policing and Social Governance
Another domain where personal data significantly impacts everyday life is law enforcement through predictive policing. This approach utilizes historical crime data, social behavior patterns, and AI-driven analytics to anticipate criminal activity (Brayne, 2021). Proponents argue that predictive policing enhances resource allocation and crime prevention. For instance, cities like Los Angeles implemented predictive models to identify high-risk areas, leading to more proactive policing (Shapiro, 2019).

However, critics contend that predictive policing perpetuates racial biases and systemic inequalities. A study by Lum & Isaac (2016) found that algorithmic models disproportionately target marginalized communities due to historically biased crime data. The reliance on flawed datasets amplifies pre-existing biases, leading to over-policing of specific neighborhoods. This raises ethical questions regarding fairness, accountability, and transparency in AI-driven governance.

Opportunities and Challenges
While personal data facilitates innovation and efficiency, it also presents notable challenges.

Opportunities
Enhanced User Experience: Data-driven personalization improves recommendations, navigation, and services in various sectors, from healthcare to entertainment.

Economic Growth: The data economy fosters innovation, enabling businesses to optimize operations and drive targeted marketing strategies (OECD, 2020).

Public Safety Improvements: Predictive analytics aid disaster management, medical diagnoses, and cybersecurity advancements.

Challenges
Privacy Erosion: The commodification of personal data raises concerns about surveillance, consent, and digital autonomy (Zuboff, 2019).

Bias and Discrimination: Data-driven decision-making often reflects societal biases, leading to discriminatory outcomes (Noble, 2018).

Regulatory Gaps: Inconsistent global data protection laws hinder effective governance, as seen in contrasting policies like the GDPR and more lenient regulations in other regions (ICO, 2021).

The Influence of Personal Data on Everyday Life: Theories, Case Studies, and Ethical Implications of Data Monetization and Privacy Challenges

02/15/2025

Theme 1 (c.1885 – 1939)

1. Explain how influential sexology has been in the ‘creation’ of sexual identities and diverse sexualities.

2. Discuss the ways in which male homosexual behaviour was perceived by the law and society between 1885 and 1939.

3. Explain how and why female homosexuality and bisexuality have been relegated to the margins of sexuality during the 19th and early twentieth centuries?

Theme 2 (c.1950-1967)

4. What efforts were made by the law and the police to prosecute and curtail homosexual offending and with what implications?

5. Explore the factors which delayed the implementation of the Wolfenden Report’s legal recommendations regarding homosexual offences and how were these challenged by various agencies.

6. Explain the reasons that Scotland and Northern Ireland were not included in the S*xual Offences Act of 1967.

Theme 3 (c. 1980-1990)

7. How did the gay liberation movement contrast with existing LGBTQ activism, and to what extent was it a successful shift?

8. To what extent did the emergence of post-Wolfenden psychiatric approaches to same-sex desire prove to be a damaging development.

10. To what extent were the 1980s problematic years for the LGBTQ community?

https://youtu.be/8wQ0eLtk-AA

Expert answer

DRAFT / STUDY TIPS:
How Influential Has S*xology Been in the ‘Creation’ of S*xual Identities and Diverse S*xualities?

Introduction
The emergence of sexology in the late 19th and early 20th centuries played a pivotal role in shaping contemporary understandings of sexual identities and diverse sexualities. Pioneering sexologists such as Richard von Krafft-Ebing, Havelock Ellis, and Magnus Hirschfeld developed early classifications of sexual behavior, which contributed to the medicalization of sexuality and the conceptualization of sexual identities. By systematically categorizing sexual behaviors, sexologists influenced both scientific discourse and societal attitudes toward sexuality. However, their work also reinforced normative assumptions, pathologized certain identities, and contributed to the legal and social regulation of sexuality. This essay examines how sexology influenced the creation of sexual identities and diverse sexualities, considering both its contributions and limitations.

The Medicalization of S*xuality
S*xology emerged at a time when European societies were experiencing rapid industrialization, urbanization, and shifts in social structures. As sexuality became a subject of scientific inquiry, it was increasingly understood through a medical lens. Richard von Krafft-Ebing’s Psychopathia S*xualis (1886) was one of the first works to systematically classify non-normative sexual behaviors, introducing terms such as “homosexuality” and “sadism.” Although his work was primarily focused on pathologizing sexual deviance, it inadvertently provided a framework that later allowed individuals to articulate their sexual identities within medical discourse.

Havelock Ellis, in contrast, took a more descriptive approach in Studies in the Psychology of S*x (1897-1928). He argued that homosexuality was an inborn trait rather than a moral failing or mental illness. While his work contributed to a more neutral or even positive understanding of same-sex attraction, it remained deeply influenced by essentialist and biological determinist perspectives.

Magnus Hirschfeld, a German physician and early LGBTQ+ rights activist, further advanced the field of sexology by advocating for sexual diversity. Through his Institute for S*xual Science in Berlin (founded in 1919), he promoted the idea that sexuality existed on a spectrum rather than being strictly binary. Hirschfeld’s work not only challenged legal persecution of homosexuality but also influenced the conceptualization of sexual identities as distinct and legitimate.

The Role of Classification in Identity Formation
One of the most significant impacts of sexology was the creation of taxonomies that categorized individuals based on their sexual behaviors and attractions. Prior to the late 19th century, sexual behavior was often judged in moral or religious terms rather than being seen as a defining characteristic of an individual’s identity. S*xologists changed this by delineating sexual orientations as intrinsic to a person’s nature. This shift had profound consequences:

Recognition and Visibility: By naming and defining categories such as “homosexual,” “bisexual,” and “heterosexual,” sexologists gave individuals language to describe their experiences. This facilitated community formation among those who identified with these categories.

Pathologization and Regulation: At the same time, the classification of non-heteronormative sexualities often led to their medicalization as disorders requiring treatment or correction. This reinforced stigma and justified legal and social repression.

Legal and Social Implications: The influence of sexology extended to legal frameworks, where medical and psychological definitions of sexuality were used to justify both criminalization and, in some cases, early decriminalization efforts. For example, Hirschfeld’s advocacy influenced Weimar-era Germany’s relatively progressive attitudes toward homosexuality, though these were later reversed under N**i rule.

The Evolution of LGBTQ+ Identities, Legal Perceptions, and Activism (1885–1990): A Historical Analysis

02/14/2025

Data
The data hotel_booking.csv file (which, note, you can download on the "Econ 337 – Assignment 1 (Week 15)" section of the course’s Moodle page) includes hotel demand data.

The dataset includes 40,060 observations of a resort hotel and 79,330 observations of a city hotel (a total of 119,390 observations). Each observation represents a hotel booking due to arrive between the 1st of July 2015 and the 31st of August 2017, including bookings that effectively arrived and bookings that were canceled.

These are the variables that you will encounter in hotel_booking.csv:

hotel: whether hotel is a resort hotel or a city hotel
is_canceled: value indicating if booking was canceled (=1) or not (=0)
lead_time: number of days between booking and arrival
arrival_date_year: year of arrival
arrival_date_month: month of arrival
stays_in_weekend_nights: number of weekend nights (Saturday or Sunday) guest stayed or booked to stay
stays_in_week_nights: number of weekday nights (Monday to Friday) guest stayed or booked to stay
adults: number of adults
children: number of children
babies: number of babies
country: country of origin
distribution_channel: booking distribution channel ("TA" stands for "Travel Agents", "TO" means "Tour Operators", "Direct" means it was directly booked by a customer, and "Corporate" means it was booked by a corporation)
is_repeated_guest: value indicating if the booking name was from a repeated guest (=1) or not (=0)
previous_cancellations: number of previous bookings that were cancelled by the customer prior to the current booking
previous_bookings_not_canceled: number of previous bookings not cancelled by the customer prior to the current booking
booking_changes: number of changes made to booking
deposit_type: type of deposit
adr: average daily rate (revenue per available room)
required_car_parking_spaces: number of car parking spaces required by customer
total_of_special_requests: number of special requests made by customer
To start, import pandas, numpy, and sklearn:

python

CopyEdit

import pandas as pd import numpy as np # for \texttt{sklearn}, check notebooks followed in class

After, load hotel_booking.csv as a pandas DataFrame:

python

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hotel_bookings = pd.read_csv("hotel_booking.csv")

In what follows, you will develop a model to predict whether a customer booking will be cancelled or not.

Exercises
Read each question carefully and answer ALL questions:

(a) Complete the following tasks which will be important for questions (b) to (f):
The dataset contains missing observations; remove these observations.
The type of a number of variables is not ready to be used in a regression. You can confirm this by running hotel_bookings.dtypes.
Detect all variables which are not ready to be used in a regression and change their type to “categorical” or “dummy”.
Example code for changing the format of two variables:

python

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cols = ["hotel", "arrival_date_month"] hotel_bookings[cols] = hotel_bookings[cols].astype("category")

Randomly split your data into a training and a test set with equal sizes. Set the random seed as your student number. Example:

python

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np.random.seed(WRITE_HERE_YOUR_STUDENT_ID)

Use np.random.choice or train_test_split() from sklearn (set random_state to your student ID).
🔹 NOTE: The training and test set created in (a.3) will be used in questions (b) to (f).

💡 (20 marks)

(b) This question is composed of 2 sub-questions:
Using all or many predictors, fit:
A linear model using least squares on your training set.
A logistic regression for the same prediction task.
Compare the test error of both models and discuss results.
Instead of relying only on the Bayes Classifier (which assigns observations based on a 50% probability threshold), the hotel company wants to minimize overbooking risks (customers booking more rooms than available under the assumption that some will cancel but actually show up).
Perform the prediction analyses from (b.1) using adjusted thresholds.
Compare and discuss your results, focusing on how the adjusted threshold affects the prediction outcomes and aligns with the company’s goal.
💡 (20 marks)

(c) Perform LDA and QDA
Using all or many predictors in your data, perform Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) on the training set to predict “is_canceled”.
Evaluate the test errors for both models.
Compare results with those obtained in question (b).
💡 (20 marks)

(d) Fit Ridge and Lasso regression models
Using all predictors in the data, fit:
Ridge regression
Lasso regression
Choose λ (lambda) using 5-fold and 10-fold cross-validation.
Report test errors for each model.
Report number of non-zero coefficient estimates as a function of λ.
Discuss findings.
💡 (20 marks)

(e) Feature selection and model performance comparison
Select 5 predictors of your choice from the dataset.
Using these 5 predictors, perform:
Linear and logistic regression
LDA and QDA
Ridge and Lasso regression
Compare classification errors or mean squared errors with models that included all predictors.
Did performance improve?
Now, start with predictors chosen by Lasso in (d) and apply the methods from (b) to (d) again.
Discuss results and differences observed.
💡 (20 marks)

Econ 337 Assignment 1: Hotel Booking Data Analysis Using Machine Learning (Regression, Classification, LDA, QDA, Ridge & Lasso)

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