Final paper describing the process my team used to examined the relationship between CNET phone review ratings and a phone's price, operating system, and core hardware specifications. The paper ended with our key insights and recommendations for phone manufacturer executives based on these outcomes.
- The document discusses a marketing research project conducted by a team for Apple to understand customer satisfaction with the latest iOS 10 update and how it compares to competitor operating systems like Android.
- A survey of 38 students found that while overall satisfaction with iOS 10 was high, Android scored higher on certain features like GPS maps. Battery life, interface design, and maps were most important to customers.
- The recommendations include prioritizing improvements to interface, battery life, and maps for iOS 11 and focusing marketing efforts on brand prestige and appealing to potential switchers from Android.
App Stores - Category Analysis (Apple App Store)PRIORI DATA
In this presentation, we go through various steps of increasing our understanding of an app store category: How big is it, how fast is it growing, how popular is it with developers, which markets are bigger and how difficult is it to reach a certain app store rank? Every view is from Priori Data PRO, a platform that is offered to developers free of charge when they partner with us at Priori Data https://prioridata.com/early_adopter_program.
If you have any question, please reach out. anders@prioridata.com
Este documento trata sobre varios temas relacionados con la informática. Explica que la informática juega un papel fundamental en la vida moderna y que es importante aprender sobre tecnología. También describe la "Hora del Código" como un movimiento global que ofrece tutoriales de una hora para enseñar programación sin experiencia previa. Finalmente, resume los tres tipos principales de lenguajes de programación: de bajo nivel, intermedio y alto nivel.
I aspire to join an organization that provides me with adequate challenges and opportunities to grow as a person while applying my knowledge, skills and innovativeness towards the fulfilment of the organization goals.
Ahmed Khaldi is an experienced oil and gas professional seeking a position as a shift supervisor or trainer. He has over 26 years of experience in operations, commissioning, and plant maintenance. His background includes roles as an operator, senior operator, and shift supervisor for facilities processing natural gas liquids, gas, and condensates. He is proficient in DCS systems and has extensive training in safety, instrumentation, and equipment maintenance.
This document provides an overview of the College and Community Innovation Program which supports partnerships between colleges and institutes across Canada and industry for developing innovations. It describes various innovation projects between colleges and industry partners in environmental science and technology, renewable energy and conservation, life sciences and health, and agriculture areas. The projects focus on issues like environmental cleanup, green energy technologies, health technologies, sustainable agriculture practices, and more. The document also lists several Industrial Research Chairs for Colleges in related fields.
Este documento resume la auditoría anual realizada a la Administración Federal de Ingresos Públicos (AFIP) en su rol de Certificador Licenciado. Se revisó la infraestructura tecnológica, los procedimientos y la documentación. Se encontraron seis observaciones principales relacionadas con la falta de pruebas completas del plan de contingencia, información errónea brindada a solicitantes, incumplimiento de estándares para dispositivos criptográficos, informes incompletos, falta de publicación de informes de auditoría y lista de
- The document discusses a marketing research project conducted by a team for Apple to understand customer satisfaction with the latest iOS 10 update and how it compares to competitor operating systems like Android.
- A survey of 38 students found that while overall satisfaction with iOS 10 was high, Android scored higher on certain features like GPS maps. Battery life, interface design, and maps were most important to customers.
- The recommendations include prioritizing improvements to interface, battery life, and maps for iOS 11 and focusing marketing efforts on brand prestige and appealing to potential switchers from Android.
App Stores - Category Analysis (Apple App Store)PRIORI DATA
In this presentation, we go through various steps of increasing our understanding of an app store category: How big is it, how fast is it growing, how popular is it with developers, which markets are bigger and how difficult is it to reach a certain app store rank? Every view is from Priori Data PRO, a platform that is offered to developers free of charge when they partner with us at Priori Data https://prioridata.com/early_adopter_program.
If you have any question, please reach out. anders@prioridata.com
Este documento trata sobre varios temas relacionados con la informática. Explica que la informática juega un papel fundamental en la vida moderna y que es importante aprender sobre tecnología. También describe la "Hora del Código" como un movimiento global que ofrece tutoriales de una hora para enseñar programación sin experiencia previa. Finalmente, resume los tres tipos principales de lenguajes de programación: de bajo nivel, intermedio y alto nivel.
I aspire to join an organization that provides me with adequate challenges and opportunities to grow as a person while applying my knowledge, skills and innovativeness towards the fulfilment of the organization goals.
Ahmed Khaldi is an experienced oil and gas professional seeking a position as a shift supervisor or trainer. He has over 26 years of experience in operations, commissioning, and plant maintenance. His background includes roles as an operator, senior operator, and shift supervisor for facilities processing natural gas liquids, gas, and condensates. He is proficient in DCS systems and has extensive training in safety, instrumentation, and equipment maintenance.
This document provides an overview of the College and Community Innovation Program which supports partnerships between colleges and institutes across Canada and industry for developing innovations. It describes various innovation projects between colleges and industry partners in environmental science and technology, renewable energy and conservation, life sciences and health, and agriculture areas. The projects focus on issues like environmental cleanup, green energy technologies, health technologies, sustainable agriculture practices, and more. The document also lists several Industrial Research Chairs for Colleges in related fields.
Este documento resume la auditoría anual realizada a la Administración Federal de Ingresos Públicos (AFIP) en su rol de Certificador Licenciado. Se revisó la infraestructura tecnológica, los procedimientos y la documentación. Se encontraron seis observaciones principales relacionadas con la falta de pruebas completas del plan de contingencia, información errónea brindada a solicitantes, incumplimiento de estándares para dispositivos criptográficos, informes incompletos, falta de publicación de informes de auditoría y lista de
How to maximize mobile website & app ROICompuware APM
Are you maximizing the return on your mobile investment? With mobile services your customers expect quick anytime transactions that work flawlessly.
Failure to identify and resolve a slow – or worse – malfunctioning mobile service will result in lost customers and irreparable brand damage.
Join featured speakers, Julie Ask, VP and Principal Analyst from independent research firm Forrester Research, Inc., and Compuware CTO APM Solutions Imad Mouline to learn:
- How to quantify the return on investment for your mobile services
- What growing mobile Web adoption and rising customer expectations mean for mobile service owners
- Common challenges that prohibit companies from capitalizing on the mobile opportunity
- Best practices to deliver quality mobile Web and application experiences to all end-users
The Mobile Shift: How Mobile is Changing Consumer BehaviorJames Burnes
The world is rapidly changing as mobile devices are quickly becoming the new norm for communications and information gathering. The introduction of the Apple iPhone shifted the use and expectation of smart phone devices from businessmen to housewives. “The Mobile Shift” seminar will teach you and your colleagues how consumer behavior is changing and how your business can capitalize on this emerging, dominant technology to grow your business.
How are consumer behaviors changing as mobile platforms and the mobile web advance? This presentation gives an overview into how the Mobile Shift is changing the opportunities and ways businesses must consider interacting with consumers.
Presented by James Burnes, Founder and CEO of Mobiltopia, a mobile strategy and app/site developer.
There will be more change in the next 10 years than there has been in the previous 100. This paper describes these expected foundational shifts and explains how we can manage them to our advantage.
In their latest discussion presentation "Winning the Game", Geoff Hollingworth, Ericsson North America Evangelist, in collaboration with Jason Hoffman, founder and CTO of Joyent, discuss what these changes will mean for devices, the cloud and the network.
This interactive presentation is supported by 8 videos. It describes the foundational changes that will occur across industries and networks, and attempts to explain how we can manage them to our advantage. The target audience of this paper is those who are involved in planning, building and profitably operating digital networks.
Learn how designers can take an analytical approach to designing emails and use data to make smart decisions for the end user. We'll dive deep into how email opens on mobile devices have (and haven't) affected click patterns, why email market share is so important, and deliver tactics to help you deliver more strategically designed emails to your subscribers.
Organizations will be affected by smartphone dock technology and need to determine how to interact with customers and partners using the technology. Software companies will also be directly impacted and must decide whether to support or ignore docking capabilities. There are risks to both supporting the new technology or ignoring it. Potential winners include Verizon Wireless and Android OS, while losers could be Comcast and those with separate devices. Corporations may benefit through reduced costs, but must adapt security and access to systems for the new technology.
Android Benchmarking And Its AuthenticityOSMAN SHEIKH
1. The document outlines a research project investigating android benchmarking tools and how manufacturers may fake benchmark scores.
2. The research will evaluate the Antutu benchmarking application across 12 test cases examining different hardware and software components.
3. The goal is to analyze Antutu's testing methods and determine if it accurately reflects device performance or if scores can be manipulated. Any methods manufacturers use to cheat benchmarks will be reported.
China mobile phone design industry report, 2009 2010 ResearchInChina
Mobile phone design industry has undergone fundamental changes.
First, mobile phone design and production patterns have changed completely. The upstream and downstream of industry chain penetrate into each other. The traditional mobile phone design companies that focused on buying chip sets, designing circuit boards and adding cases have gradually quitted. Instead, the upstream, midstream and downstream of industry chain are integrated vertically so that enterprises can master the difference of mobile phones more effectively.
The document discusses smartphones and their platforms. It begins with a brief history of major smartphones and platforms. It then addresses questions about how smartphones differ from regular phones, the importance of apps, lock-in effects, and whether smartphones or PCs will dominate. Currently, no single platform or manufacturer dominates the fragmented smartphone industry, though Nokia and Android have led in market share. Going forward, Android may continue growing due to its open ecosystem, but the situation will likely remain competitive.
Mobile application testing is becoming increasingly important as mobile usage grows. The document discusses strategies for testing mobile applications across different platforms, carriers, and devices. It also describes 360logica's approach to mobile application testing, including maintaining a lab with many device types, creating manual and automated test suites, and providing end-to-end testing services.
Social networking has surpassed email as the most popular online activity. Facebook leads the social networking industry in the United States, receiving the largest share of display ad impressions. As smartphone and tablet usage increases, more people are accessing the internet on mobile devices. Collaboration platforms help large organizations stay connected in real-time across departments and locations. Dell and Motorola use Salesforce's Chatter platform to improve sales productivity, approve partner deals, and keep legal, finance, and operations teams aligned on contracts. Customers report improvements in key metrics like sales, customer satisfaction, and employee productivity after implementing Chatter. Force.com allows businesses to build applications faster and at half the cost of traditional on-premise software.
The document discusses considerations for developing a mobile application versus a mobile web site. It notes that mobile apps can access native device features but have a higher development and maintenance cost. Mobile websites have a lower cost but cannot access certain device capabilities and may have a less rich user experience. The document also examines different types of mobile apps and strategies for marketing an app within app marketplaces.
The document describes research conducted to identify improvements to the HTC EVO 4G smartphone. Several analyses were performed including a cell phone survey, task analysis, verbal protocol analysis, analysis of similar systems, interviews, and timeline analyses. The analyses revealed issues with the phone's power button location, unstructured redundancy between home screen buttons, and multiple steps required to access contacts from the dialer. Recommendations will be provided to address these problems and improve the user experience.
How is mobility transforming the enterprise? What is the fizzle that drives success? What are the key tools and trends to keep in mind for 2015?
Every year we advise our customers and partners on the top trends in mobile and what it means for them. This year we've expanded this by looking specifically at enterprise mobility trends based on insights from customers, research and more.
Term PaperMobile Computing and Social NetworksDue Week 10 and w.docxmattinsonjanel
Term Paper:Mobile Computing and Social Networks
Due Week 10 and worth 200 points
There are thousands of iPhone Apps, iPad Apps, and Android Apps that have been developed to perform a myriad of tasks and processes. Initially, most of these applications were games intended to be played on mobile devices. The popularity of these applications led businesses to ponder whether some of their business process applications that run on desktop platforms and the Web could be redesigned to run on mobile devices. The answer was a resounding yes! For example, Nationwide developed the Nationwide® Mobile, a free iPhone App that allows its insurance policyholders to file a claim on the spot when an accident occurs. The application can snap pictures of the accident and attach them to the claim data and upload the claim information to a server. This reduces the length of time to process a claim filed this way. Nationwide competitors have followed suit and developed iPhone, iPad, and Android applications of their own. Policyholders can receive messages via Facebook or Twitter. Other business processes that have been reengineered as a result of mobile computing include Quick Response (QR) codes which have replaced one-dimensional bar codes. They are read using mobile devices, accepting credit card payments from an iPhone, iPad or Android device, depositing checks using an iPhone without visiting a bank, and many more. Write a ten to fifteen (10-15) page term paper in which you:
1. Assess the effectiveness and efficiency mobile-based applications provide to capture geolocation data and customer data, and quickly upload to a processing server without users having to use a desktop system.
2. Evaluate benefits realized by consumers because of the ability to gain access to their own data via mobile applications.
3. Examine the challenges of developing applications that run on mobile devices because of the small screen size.
4. Describe the methods that can be used to decide which platform to support, i.e., iPhone, iPad, Windows Phone, or Android.
5. Mobile applications require high availability because end users need to have continuous access to IT and IS systems.
•Discuss ways of providing high availability.
•Mobile devices are subjected to hacking at a higher rate than non-mobile devices. Discuss methods of making mobile devices more secure.
•Use at least three (3) quality resources in this assignment. Note: Wikipedia and similar Websites do not qualify as quality resources.
Your assignment must follow these formatting requirements:
•Be typed, double spaced, using Times New Roman font (size 12), with one-inch margins on all sides; citations and references must follow APA or school-specific format. Check with your professor for any additional instructions.
•Include a cover page containing the title of the assignment, the student’s name, the professor’s name, the course title, and the date. The cover page and the reference page are not included in the required assig ...
The document discusses emerging technologies and their impact on business and society over the next few years. It covers topics like the internet of things, augmented reality, cloud computing, mobile apps, and how businesses can adapt to remain relevant. Key points include that the internet will continue to transform everything; the internet of things will connect more devices than people; mobile apps and cloud-based services will become more important than software tied to devices; and businesses must focus on the customer experience across all channels to succeed going forward.
This paper presents the design, implementation, and evaluation of CenceMe, a mobile application that uses sensors in phones to infer users' activities and social contexts and shares this information through social networks. The key aspects are:
1. CenceMe uses a split-level classification approach, with some classification done on phones and some on backend servers, to improve scalability and resiliency.
2. It implements power-aware duty cycling of sensors, uploads, and radios to extend battery life while maintaining responsiveness.
3. A user study with 22 participants tested CenceMe continuously for 3 weeks and provided insights into how people relate to and use personal sensing applications.
The survey of over 400 mobile industry professionals explored technical issues, policies, and their impact on the industry. Respondents wanted more information on device performance and control over applications. Fragmented APIs were seen as a major barrier to application development. There was no consensus on whether the browser is currently the main application environment, but most agreed it will be in 1-3 years if standards-based interfaces and support for markup languages and push data are adopted.
Mobile World Congress 2017 Recap: The Future of ConnectivityIan Beacraft
A recap of the fourteen trends that defined Mobile World Congress 2017, through the lens of Epsilon Agency's innovation platform of Cognition, Connection and Immersion. Here you'll find the best of the show.
How to maximize mobile website & app ROICompuware APM
Are you maximizing the return on your mobile investment? With mobile services your customers expect quick anytime transactions that work flawlessly.
Failure to identify and resolve a slow – or worse – malfunctioning mobile service will result in lost customers and irreparable brand damage.
Join featured speakers, Julie Ask, VP and Principal Analyst from independent research firm Forrester Research, Inc., and Compuware CTO APM Solutions Imad Mouline to learn:
- How to quantify the return on investment for your mobile services
- What growing mobile Web adoption and rising customer expectations mean for mobile service owners
- Common challenges that prohibit companies from capitalizing on the mobile opportunity
- Best practices to deliver quality mobile Web and application experiences to all end-users
The Mobile Shift: How Mobile is Changing Consumer BehaviorJames Burnes
The world is rapidly changing as mobile devices are quickly becoming the new norm for communications and information gathering. The introduction of the Apple iPhone shifted the use and expectation of smart phone devices from businessmen to housewives. “The Mobile Shift” seminar will teach you and your colleagues how consumer behavior is changing and how your business can capitalize on this emerging, dominant technology to grow your business.
How are consumer behaviors changing as mobile platforms and the mobile web advance? This presentation gives an overview into how the Mobile Shift is changing the opportunities and ways businesses must consider interacting with consumers.
Presented by James Burnes, Founder and CEO of Mobiltopia, a mobile strategy and app/site developer.
There will be more change in the next 10 years than there has been in the previous 100. This paper describes these expected foundational shifts and explains how we can manage them to our advantage.
In their latest discussion presentation "Winning the Game", Geoff Hollingworth, Ericsson North America Evangelist, in collaboration with Jason Hoffman, founder and CTO of Joyent, discuss what these changes will mean for devices, the cloud and the network.
This interactive presentation is supported by 8 videos. It describes the foundational changes that will occur across industries and networks, and attempts to explain how we can manage them to our advantage. The target audience of this paper is those who are involved in planning, building and profitably operating digital networks.
Learn how designers can take an analytical approach to designing emails and use data to make smart decisions for the end user. We'll dive deep into how email opens on mobile devices have (and haven't) affected click patterns, why email market share is so important, and deliver tactics to help you deliver more strategically designed emails to your subscribers.
Organizations will be affected by smartphone dock technology and need to determine how to interact with customers and partners using the technology. Software companies will also be directly impacted and must decide whether to support or ignore docking capabilities. There are risks to both supporting the new technology or ignoring it. Potential winners include Verizon Wireless and Android OS, while losers could be Comcast and those with separate devices. Corporations may benefit through reduced costs, but must adapt security and access to systems for the new technology.
Android Benchmarking And Its AuthenticityOSMAN SHEIKH
1. The document outlines a research project investigating android benchmarking tools and how manufacturers may fake benchmark scores.
2. The research will evaluate the Antutu benchmarking application across 12 test cases examining different hardware and software components.
3. The goal is to analyze Antutu's testing methods and determine if it accurately reflects device performance or if scores can be manipulated. Any methods manufacturers use to cheat benchmarks will be reported.
China mobile phone design industry report, 2009 2010 ResearchInChina
Mobile phone design industry has undergone fundamental changes.
First, mobile phone design and production patterns have changed completely. The upstream and downstream of industry chain penetrate into each other. The traditional mobile phone design companies that focused on buying chip sets, designing circuit boards and adding cases have gradually quitted. Instead, the upstream, midstream and downstream of industry chain are integrated vertically so that enterprises can master the difference of mobile phones more effectively.
The document discusses smartphones and their platforms. It begins with a brief history of major smartphones and platforms. It then addresses questions about how smartphones differ from regular phones, the importance of apps, lock-in effects, and whether smartphones or PCs will dominate. Currently, no single platform or manufacturer dominates the fragmented smartphone industry, though Nokia and Android have led in market share. Going forward, Android may continue growing due to its open ecosystem, but the situation will likely remain competitive.
Mobile application testing is becoming increasingly important as mobile usage grows. The document discusses strategies for testing mobile applications across different platforms, carriers, and devices. It also describes 360logica's approach to mobile application testing, including maintaining a lab with many device types, creating manual and automated test suites, and providing end-to-end testing services.
Social networking has surpassed email as the most popular online activity. Facebook leads the social networking industry in the United States, receiving the largest share of display ad impressions. As smartphone and tablet usage increases, more people are accessing the internet on mobile devices. Collaboration platforms help large organizations stay connected in real-time across departments and locations. Dell and Motorola use Salesforce's Chatter platform to improve sales productivity, approve partner deals, and keep legal, finance, and operations teams aligned on contracts. Customers report improvements in key metrics like sales, customer satisfaction, and employee productivity after implementing Chatter. Force.com allows businesses to build applications faster and at half the cost of traditional on-premise software.
The document discusses considerations for developing a mobile application versus a mobile web site. It notes that mobile apps can access native device features but have a higher development and maintenance cost. Mobile websites have a lower cost but cannot access certain device capabilities and may have a less rich user experience. The document also examines different types of mobile apps and strategies for marketing an app within app marketplaces.
The document describes research conducted to identify improvements to the HTC EVO 4G smartphone. Several analyses were performed including a cell phone survey, task analysis, verbal protocol analysis, analysis of similar systems, interviews, and timeline analyses. The analyses revealed issues with the phone's power button location, unstructured redundancy between home screen buttons, and multiple steps required to access contacts from the dialer. Recommendations will be provided to address these problems and improve the user experience.
How is mobility transforming the enterprise? What is the fizzle that drives success? What are the key tools and trends to keep in mind for 2015?
Every year we advise our customers and partners on the top trends in mobile and what it means for them. This year we've expanded this by looking specifically at enterprise mobility trends based on insights from customers, research and more.
Term PaperMobile Computing and Social NetworksDue Week 10 and w.docxmattinsonjanel
Term Paper:Mobile Computing and Social Networks
Due Week 10 and worth 200 points
There are thousands of iPhone Apps, iPad Apps, and Android Apps that have been developed to perform a myriad of tasks and processes. Initially, most of these applications were games intended to be played on mobile devices. The popularity of these applications led businesses to ponder whether some of their business process applications that run on desktop platforms and the Web could be redesigned to run on mobile devices. The answer was a resounding yes! For example, Nationwide developed the Nationwide® Mobile, a free iPhone App that allows its insurance policyholders to file a claim on the spot when an accident occurs. The application can snap pictures of the accident and attach them to the claim data and upload the claim information to a server. This reduces the length of time to process a claim filed this way. Nationwide competitors have followed suit and developed iPhone, iPad, and Android applications of their own. Policyholders can receive messages via Facebook or Twitter. Other business processes that have been reengineered as a result of mobile computing include Quick Response (QR) codes which have replaced one-dimensional bar codes. They are read using mobile devices, accepting credit card payments from an iPhone, iPad or Android device, depositing checks using an iPhone without visiting a bank, and many more. Write a ten to fifteen (10-15) page term paper in which you:
1. Assess the effectiveness and efficiency mobile-based applications provide to capture geolocation data and customer data, and quickly upload to a processing server without users having to use a desktop system.
2. Evaluate benefits realized by consumers because of the ability to gain access to their own data via mobile applications.
3. Examine the challenges of developing applications that run on mobile devices because of the small screen size.
4. Describe the methods that can be used to decide which platform to support, i.e., iPhone, iPad, Windows Phone, or Android.
5. Mobile applications require high availability because end users need to have continuous access to IT and IS systems.
•Discuss ways of providing high availability.
•Mobile devices are subjected to hacking at a higher rate than non-mobile devices. Discuss methods of making mobile devices more secure.
•Use at least three (3) quality resources in this assignment. Note: Wikipedia and similar Websites do not qualify as quality resources.
Your assignment must follow these formatting requirements:
•Be typed, double spaced, using Times New Roman font (size 12), with one-inch margins on all sides; citations and references must follow APA or school-specific format. Check with your professor for any additional instructions.
•Include a cover page containing the title of the assignment, the student’s name, the professor’s name, the course title, and the date. The cover page and the reference page are not included in the required assig ...
The document discusses emerging technologies and their impact on business and society over the next few years. It covers topics like the internet of things, augmented reality, cloud computing, mobile apps, and how businesses can adapt to remain relevant. Key points include that the internet will continue to transform everything; the internet of things will connect more devices than people; mobile apps and cloud-based services will become more important than software tied to devices; and businesses must focus on the customer experience across all channels to succeed going forward.
This paper presents the design, implementation, and evaluation of CenceMe, a mobile application that uses sensors in phones to infer users' activities and social contexts and shares this information through social networks. The key aspects are:
1. CenceMe uses a split-level classification approach, with some classification done on phones and some on backend servers, to improve scalability and resiliency.
2. It implements power-aware duty cycling of sensors, uploads, and radios to extend battery life while maintaining responsiveness.
3. A user study with 22 participants tested CenceMe continuously for 3 weeks and provided insights into how people relate to and use personal sensing applications.
The survey of over 400 mobile industry professionals explored technical issues, policies, and their impact on the industry. Respondents wanted more information on device performance and control over applications. Fragmented APIs were seen as a major barrier to application development. There was no consensus on whether the browser is currently the main application environment, but most agreed it will be in 1-3 years if standards-based interfaces and support for markup languages and push data are adopted.
Mobile World Congress 2017 Recap: The Future of ConnectivityIan Beacraft
A recap of the fourteen trends that defined Mobile World Congress 2017, through the lens of Epsilon Agency's innovation platform of Cognition, Connection and Immersion. Here you'll find the best of the show.
Similar to Managerial Statistics II - Hardware or Software: What Leads to Higher Review Ratings? (20)
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
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Watch the video recording at https://youtu.be/5vjwGfPN9lw
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Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
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Managerial Statistics II - Hardware or Software: What Leads to Higher Review Ratings?
1. Alberto Ciaraldi and Jameson Cook
Team 124
5 December 2015
Hardware or Software: What Leads to Higher Review Ratings?
1. Introduction
A. General Overview
We would like to know what aspects of a phone leads to a higher review rating from well
respected tech blogs. We have decided to analyze the software of a phone based on operating
system and the hardware of a phone based on hardware specifications of a phone’s internal
components, while considering every phone’s price at launch for their baseline model.
This information could be useful for business executives at phone manufacturing companies,
because consumers in the 21st century are increasingly using internet reviews by well trusted
sources to help determine their purchasing decisions. Our outcomes will provide insights to
which attributes of a phone lead to higher ratings online, that can in return lead to heightened
customer demand to increase sales and revenue. This issue is extremely interesting, because the
report investigates how a phone’s software affects ratings as well as hardware. We will also be
able to give specific recommendations for the hardware components that lead to higher ratings
most directly; therefore, we can tell executives what hardware to prioritize.
B. Relevant Sources
A comprehensive report, titled “Gigahertz, Megapixels, and Millimeters - Do Specs Matter At
All?” from Android Central, another influential tech blog, discusses the topic of whether
hardware features and specs really matter to the end user. However, the article is very qualitative
and does not use any hard data or analysis of that data to reach conclusions. We think our model
will show direct correlations between individual phone specifications and the way that the phone
is viewed by reputable tech websites. This will give us specific recommendations and ideas on
how to improve future phone ratings by changing certain internal smartphone components.
In an article called “The Phone Specs That Matter,” TechHive states which phone specifications
do matter to consumers, which we predict would also increase CNET ratings. Again it is only
qualitative, so our model using specific data will clearly defend or negate these assertions.
C. Our Project
2. We intend to contribute useful and statistically significant findings about the relationship
between the 100 most recently reviewed phones on CNET and their respective phone price,
hardware specifications, and operating system. Using the 100 most recently reviewed phones as
our data points adds a sense of consistency and timeliness to the information that may not have
been present if we’d researched phones that were too old, no longer in the marketplace, or
released at very different times.
Our project differs from other analyses on this question, because other analyses in this topic
focus solely on whether to care about specs at all, but do not include any type of analytical
research using data. There is a large amount of speculation and theories present on the web in
regards to this question, however they reside on the qualitative side. This analysis will create a
very specific, data-driven results that will illustrate what variables affect a phone’s rating.
D. Hypothesis
Similarly to TechHive’s article, we expect to see a positive correlation between a phone’s rating
on CNET and certain component specs, such as screen resolution, screen size, processing speed,
RAM, and camera megapixels. We also believe that a phone’s operating system will either
negatively or positively impact a phone’s rating significantly. We think that iOS phones will
have higher ratings, Windows Phone and BlackBerry OS phones will have lower ratings, and are
unsure what effect Android will have on ratings due to the large number and variety of Android
phones. Price is a very important factor in every consumer’s decision making process, so we
think that price will have the greatest impact on review scores.
2. Key Results
Our model gave us conclusive findings on the relationship that a phone’s price, hardware, and
software have on its’ review rating on CNET. One of the most interesting findings wasn’t just
the coefficients of these relationships, but the relationships that mattered and were statistically
significant.
Through backwards elimination to find the best fit model, we found that weight, RAM, battery
size, memory, thickness, and secondary camera megapixels do not have a statistically significant
relationship with CNET rating. In our best fit model, we found that, on average, choice of
operating system has a more significant correlation with review rating than any individual
hardware component; however, the hardware components combined have more of an influence
on rating than any particular operating system.
The hardware components that do have a significant correlation with rating are main camera
megapixels and screen resolution, with respect to their magnitude of correlation from highest to
lowest. Additionally, we found that price had a lower correlation with rating than camera and
3. software, but higher than screen resolution. We found that processor speed and screen size did
not have a statistically significant relationship with CNET rating.
Below is the individual relationship between CNET rating with camera and screen resolution
respectively.
3. Data
A. Data Sources
To get the dependent variable, the CNET review ranking, we went through the most recent 100
reviewed phones and recorded their rating manually.
To get the independent variables, the phone’s specs, we used GSM Arena to manually record the
hardware specifications for all of the 100 most recently reviewed phones on CNET. GSM Arena
provided us with a detailed report of all the different components that make up cellular phones.
4. B. Dependent Variable:
1) CNET Rating: The score that CNET gave the phone when reviewing it upon launch out
of a 1-10 scale.
C. Independent Variables:
1) Baseline Price ($): The initial price at launch for the baseline model of the phone.
2) Main Camera Megapixels (MP): The number of megapixels of the phone’s back camera.
3) Secondary Camera Megapixels (MP): The number of megapixels of the phone’s front
camera.
4) Screen Size (Inches): The diagonal length in inches of the phone’s screen.
5) Screen Resolution (Pixels per Inch): The amount of pixels within every square inch of the
phone’s display, known as PPI.
6) Processor Speed: The speed of the phone’s CPU taken by multiplying the number of
cores by the processing speed of each individual CPU core in GHz for the phone’s
baseline model.
7) RAM (GB): The amount of RAM the phone has in gigabytes.
8) Base Memory Storage Capacity (GB): The amount of gigabytes of internal memory
storage for the baseline model of the phone.
9) Battery Size (mAh): The size of the phone’s battery in Milliamp Hours.
10) Thickness (mm): The thickness of the phone in millimeters.
11) Weight (Grams): The weight of the phone in grams.
12) iOS: Indicator variable determining whether or not the phone’s operating system is
Apple’s proprietary OS iOS. We used iOS as our base case for our indicator variables.
13) Android: Indicator variable determining whether or not the phone’s operating system is
Google’s open source Android.
14) Windows Phone: Indicator variable determining whether or not the phone’s operating
system is Microsoft’s proprietary OS that can be licensed to third-party OEMs, such as
Nokia.
15) BlackBerry OS: Indicator variable determining whether or not the phone’s operating
system is Microsoft’s proprietary OS.
D. Potential Criticism & Supplemental Data:
The used data came from two separate, respected websites. For the phone’s specifications, all
data was credible, because GSM Arena is notorious for being the most accurate and qualified site
for hardware specifications on the Internet. CNET review ratings are credible because they are
one of the oldest running tech blogs that remains to be an industry leader with very high traffic.
Someone might say that review ratings are subjective, however that is the very nature of ratings,
so criticizing such a process would be irrelevant to the point that is being tried to be solved.
5. We had to record the data manually, which could have led to human error in the data.
Additionally, three people manually recorded the data, so someone could have been recording
the data differently than others. To prevent internal errors, we created a unified method for
recording the data and all looked over the websites together, to ensure that our data recording
was consistent overall. Furthermore, as a final measure, one person went through all of the data
to make sure that all data entries appeared consistent within each variable’s respective range, and
identified and fixed any possible errors.
Our model would be even more accurate if we could see how the phone is being received by the
public. We could analyze the amount of mentions on different social media platforms and the
general tone when the phone is mentioned in a post. Unfortunately, we do not have any access to
this data through Facebook, Twitter, or other social networks.
We would have liked to see how ratings affected phone sales to give better recommendations to
tech executives, however finding this data by ourselves would not have been consistent due to
differing phone release dates. Our MLR could have also been more accurate if we included the
release date of each phone to account for technological improvements that naturally occur over
time.
E. Descriptive Statistics and Scatter Plots
Below is the descriptive statistics for all independent variables. We will include the scatter plots
for all independent variables in the appendix, part A.
6. 1. Baseline Price:
For baseline price, there is a positive correlation and linear relationship, with a coefficient of
0.0029, between our dependent variable, CNET rating, and the phones’ baseline price, on
average, holding all other independent variables equal.
All descriptive statistics seem accurate and the data looks internally consistent.
Some companies may charge higher prices due to aggressive and well functioning marketing
campaigns, while some companies may charge a low price in order to increase sales and market
share.
2. Main Camera Megapixels:
For main camera megapixels, there is a positive correlation and linear relationship, with a
coefficient of 0.1237, between our dependent variable, CNET rating, and the amount of
megapixels on the back camera of the phone, on average, holding all other independent variables
equal.
All descriptive statistics seem accurate and the data looks internally consistent.
7. Some companies may segment towards different consumers that either care more or less about
having a high quality camera in their cell phone. Depending on their business strategy, this could
create extremes of a very high megapixel back camera as a core feature or a very low megapixel
back camera as an afterthought.
3. Secondary Camera Megapixels:
For baseline secondary camera megapixels, there is a positive correlation and linear relationship,
with a coefficient of 0.0917, between our independent variable, CNET rating, and the phones’
baseline secondary camera megapixels, on average, holding all other dependent variables equal.
All descriptive statistics seem accurate and the data looks internally consistent.
Similar to the megapixels for back phone cameras, companies can either provide a front facing
camera with high megapixels to appeal to younger audiences that appreciate selfies. On the other
side, they can not include a front facing camera or provide a low megapixel camera to bring
down overall cost.
4. Screen Size:
For screen size, there is a positive correlation and linear relationship, with a coefficient of
0.8696, between our dependent variable, CNET rating, and the phone’s screen size, on average,
holding all other independent variables equal.
All descriptive statistics seem accurate and the data looks internally consistent.
The outliers will come from companies’ business strategy as they can have bigger screen sizes
for pro and advanced users, while offering small screen sizes for younger or price-conscious
audiences.
5. Screen Resolution:
For the display’s pixels per inch, there is a positive correlation and linear relationship, with a
coefficient of 0.0058, between our dependent variable, CNET rating, and the phone's screen
resolution, on average, holding all other independent variables equal.
All descriptive statistics seem accurate and the data looks internally consistent.
There are examples of outliers that have the sharpest resolution technology offers, but demand a
high price. There are others that have low screen resolution, in order to be a budget device that
will most likely receive a lower rating.
8. 6. Processor Speed:
For the number of processing cores, there is a positive correlation and linear relationship, with a
coefficient of 0.0758, between our dependent variable, CNET rating, and the phone’s processor
speed, on average, holding all other independent variables equal.
All descriptive statistics seem accurate and the data looks internally consistent.
Phones that have a high processor speed may be pricier and get better reviews. Phones that have
lower processor speed may have a lower price or could use an operating system, such as iOS,
that doesn’t require a high amount of processing power.
7. RAM:
For RAM, there is a positive correlation and linear relationship, with a coefficient of 0.5599,
between our independent variable, CNET rating, and the phones’ RAM, on average, holding all
other dependent variables equal.
All descriptive statistics seem accurate and the data looks internally consistent.
Phones with a high RAM are going to be more expensive and thick, while phones that have
lower RAM are going to be cheaper to be sold as budget phones.
8. Base Memory Storage Capacity:
For base memory storage capacity, there is a positive correlation and linear relationship, with a
coefficient of 0.0353, between our independent variable, CNET rating, and the phone’s amount
of memory, on average, holding all other dependent variables equal.
All descriptive statistics seem accurate and the data looks internally consistent.
Some phones give high amounts of memory to incentivize pro users, while others include little to
be a budget phone and focus on other specs.
9. Battery Size:
For battery size, there is a positive correlation and linear relationship, with a coefficient of
0.0006, between our independent variable, CNET rating, and the phone’s battery size, on
average, holding all other dependent variables equal.
All descriptive statistics seem accurate and the data looks internally consistent.
9. Some companies focus on including big batteries in order to increase use time or allow the user
to do more complex functions on the phone. Other phones may sacrifice battery size in order to
increase other hardware or decrease thickness, weight, and price.
10. Thickness:
For thickness, there is a negative correlation and linear relationship, with a coefficient of -
.02469, between our independent variable, CNET rating, and the phone’s thickness, on average,
holding all other dependent variables equal.
All descriptive statistics seem accurate and the data looks internally consistent.
There are outliers that are thick in order to include more advanced processors and memory or to
have a more durable build quality. Some phones are made intentionally as thin as possible to be
attractive to the consumers and increase marketing power.
11. Weight:
For weight, there is a positive correlation and linear relationship, with a coefficient of 0.0107,
between our independent variable, CNET rating, and the phone’s weight, on average, holding all
other dependent variables equal.
All descriptive statistics seem accurate and the data looks internally consistent.
Phones that are very heavy include more hardware, have a better build quality, or have a bigger
screen. Phones that are light can be due to having a small amount of hardware or attempting to
be attractive to buyers.
12. iOS:
iOS, was our base case so we cannot determine its’ relationship with our dependent variable,
CNET rating, but only how it compares with the other indicator variables.
All descriptive statistics seem accurate and the data looks internally consistent.
13. Android:
For operating system, there is a negative correlation and linear relationship, with a coefficient of
-1.355, between our dependent variable, CNET rating, and Android as the OS of the phone, on
average, holding all other independent variables equal.
10. All descriptive statistics seem accurate and the data looks internally consistent.
We believe that this is because many companies that use Android change it to add new features
that cause bugs and a slow, confusing user experience.
14. Windows Phone:
For operating system, there is a negative correlation and linear relationship, with a coefficient of
-1.027, between our dependent variable, CNET rating, and Windows as the OS of the phone, on
average, holding all other independent variables equal.
All descriptive statistics seem accurate and the data looks internally consistent.
We believe that this is because Windows is a less fast and easy OS to use than iOS.
15. BlackBerry OS:
For operating system, there is a negative correlation and linear relationship, with a coefficient of
-1.317, between our dependent variable, CNET rating, and BlackBerry OS as the OS of the
phone, on average, holding all other independent variables equal.
All descriptive statistics seem accurate and the data looks internally consistent.
We believe that this is because BlackBerry has a much slower user experience than iOS that is
much more complex and confusing.
4. Modelling
A. Modelling Technique
11. We decided that the most efficient way to find the best fit model was to use backwards
elimination, with respect to standard error. We took our initial model and deleted the variable
with the highest p-value, re-ran the model, and checked if it had a lower standard error. We
continued to remove the variable with the highest p-value until we reached a new model that had
a higher standard error than the previous model, and then used the previous model as our best fit.
We created a multiple linear regression model finding the relationship between our independent
variable, which was the CNET rating score, and our dependent variables, which in the best fit
model follow below. To make the coefficients relevant between all variables, we multiplied each
variable's coefficient by its respective range:
CNET Ratings = 5.513
+ 0.963 (Price)
+ 1.076 (Camera)
+ 0.667 (Screen Size)
+ 0.959 (PPI)
– 0.768 (Speed)
– 1.355 (Android)
– 1.027 (Windows)
– 1.317 (BlackBerry)
Metrics for the model:
● R^2: 0.60
● Standard Error: 0.67
● Standard Error / Y Average: 0.09
● F-test: 4.75x10^15
12. ● Independent Variable p-values:
○ Baseline price: 0.016
○ Main camera megapixels: 0.019
○ Screen size: 0.109
○ Screen resolution: 0.021
○ Processor speed: 0.146
○ Android: 0.001
○ Windows Phone: 0.023
○ BlackBerry OS: 0.015
This model is very statistically significant, because the standard error/Y average is 0.09, which is
far below our benchmark of 0.2. The independent variables do not perfectly describe all
variations in the dependent variable, CNET rating, because the R^2 is 0.60 which is good but not
great. The model passes the F-test with a value of 4.75x10^15. Unfortunately, 2 independent
variables, screen size and processor speed, are not statistically significant with p-values of 0.109
and 0.146 respectively. The other 6 independent variables are all statistically significant with p-
values below 0.05.
B. Other Potential Models and Data
We could also have used 3 other models that we discussed in class, but did not find them helpful
in improving our model or did not have the relevant data to make them useful. The first is a
second order model that raises an independent variable to the second power to account for non-
linear relationships between independent and dependent variables. We looked at the trendlines of
our residual scatter plots for each independent variable and believe that all have a linear and non-
quadratic relationship, so we believe this model would not have expanded our analysis of this
problem.
We could have used autocorrelation, such as time series AR(1) model, to check if there was
correlation between the errors of phones that were released at consecutive time periods. We did
not know how to deal with time-series related data when we manually recorded our data, so we
did not record the release dates of phones. Autocorrelation would help the most when trying to
determine the affects time has on the expectations for the hardware specifications should have
every year, but we think that our observations were reviewed in a close enough time period that
this did not effect our data significantly.
As discussed earlier, we would have loved to have and include data on mass social responses to
phones to observe how it related to or led to CNET ratings. It would have also been interesting to
see how tech blog ratings were related to phone sales. If we had used phone sales data, a Cobb-
Douglass model would have been appropriate to use for our analyzation, but we did not have
access to or use sales data. Other correlations between CNET ratings and phone differences were
13. mostly hardware and software quality, which we attempted to represent through phone operating
system, but were not able to include phone manufacturer.
C. Suspect Points
Using SPSS, we got the following output for the residuals of our model.
14.
15. Outliers:
As you can see in the above outputs, there are 4 observations, at 24, 68, 71, and 93, that have
standardized residuals below -2 or above 2, with values of -2.5, 2.2, -3.7, and -2.2 respectively.
There are 5 observations, at 24, 68, 71, 93, and 94, that have studentized residuals below -2 or
above 2, with values of -2.7, 2.2, -3.7, -2.3, and 2.0 respectively. Observations that have
residuals below -2 or above 2 are considered outliers.
We believe that Observation 24, the Yota YotaPhone 2, is an outlier because it has an incredibly
high price and high processor speed for such a low rating, 6.0; we think this is due to poor
software integration. Observation 68, the HTC Desire 610, is an outlier because it has a very
high rating, 7.6, compared it’s extremely low component specifications in all areas; this is
because the phone is specifically made to be a budget phone, and simply delivers the best overall
package of good enough hardware accompanied with great software for a competitive price that
causes it to have favorable ratings. Observation 71, the Ascend Y550, is an outlier because it has
an incredibly low rating, at 3.7 which happened to be the lowest rating of all our observations,
even though it has decent hardware specifications; we believe this low rating is due to poor
integration of Android software. Observation 93, the Kyocera Hydro Icon, is an outlier because it
has a very low rating, 5.0, in relation to it’s medium level hardware specifications; again, we
think this is due to poor implementation of the Android software which hinders user experience
no matter how good hardware components are. Observation 94, the Nokia Lumia 635, is an
outlier, because it has a relatively high rating, 7.5, even though it has low hardware component
16. specs in screen resolution and processor speed; we think this is due to its’ cheap price, strong
build quality, and good implementation of the Windows Phone software.
Luckily, it is normal to have up to 5 observations that are outliers in a model that is to trying to
predict linear relationships with 95% accuracy out of a total of 100 observations. Therefore, we
did not do anything about the outliers and do not believe that they have affected our data
significantly. However, we will check each of these observations respective Cook’s D to see if
any of them asserted influence in the model, which would cause us to want to remove them.
Below is an included histogram plotting the frequency of standardized residuals within certain
buckets.
Leverage:
After comparing the data outputted by SPSS for leverage, we compared every observations
leverage with the model’s critical leverage value, (3*(k+1))/n, which was 0.27. We found 5
observations that have a leverage above 0.27. They are observations 9, 31, 39, 40, and 47 with
leverage values of 0.27, 0.37, 0.34, 0.27, and 0.38 respectively.
We believe that observation 9, the Apple iPhone 6s, has leverage because it has a relatively high
score, 8.9, while relatively very low values for important components, such as camera
megapixel, screen size, screen resolution, and processor speed; we think that it has a high score
due to its’ operating system, iOS. Observation 40, the Apple iPhone 6 plus, represents a similar
relationship between high score and low hardware specifications as stated above.
We believe that observation 31, the BlackBerry Classic, has leverage because it has a relatively
high score, 7.3, in comparison to its very poor hardware specifications, in camera megapixels,
screen size, screen resolution, and processor speed, for a phone of that score; BlackBerry has
made up for this through its operating system and hardware tailored towards its target audience’s
needs. Observation 47, the BlackBerry Leap, has leverage due to a similar problem as described
17. above; it has a relative high score, 6.6, in comparison to the specifications of its’ internal
components. Observation 39, the BlackBerry Passport, has leverage in the opposite way because
it has a very low score in comparison to its relatively high component specifications, in screen
resolution and processor speed and price; this happened because BlackBerry attempted to make a
better phone by adopting other companies’ product best practices instead of focusing on the
features their core users demanded.
Leverage alone is not a reason to remove an observation but we will check these observations’
respective Cook’s D to see if any of them asserted influence in the model, which would cause us
to want to remove them.
Cook’s Distance:
As you can see in the data above, there are no observations that have a Cook’s D value of above
0.5. All outliers and leverage points previously mentioned have a Cook’s D of less than 0.15, so
we do not believe that any observation points have affected our model greatly and will not
remove any points.
D. Collinearity
Below is our printed out correlation and variance inflationary factor (VIF) matrix tables for all
independent variables included in the final model.
As you can see, there is only one relationship between our independent variables that show signs
of multicollinearity. Variables may be collinear if they have correlations above 0.7 or below -0.7.
The correlation between Android and Windows Phone is -0.75, which is, as stated above, is
indicative of multicollinearity. This is completely normal, because there should be some
correlation between these two variables as they are indicator variables. Additionally, we decided
to ignore this present multicollinearity because it did not seem to have an effect on our model as
their respective p-values, t-stats, and coefficient signs all look correct.
18. Variance Inflationary Factors (VIF) are an even better way of identifying multicollinearity.
Relationships between variables will display signs of multicollinearity if their VIF is 4 or greater.
We got this VIF table by using the inverse Excel function on the correlation table. As in the
provided table above, the relationship between Android and Windows Phone have a VIF above
4. However, as stated above, we ignored this issue, because we did not see any massive impacts
that this multicollinearity had on our model.
E. Model Development Roadmap
To reach our final model, we used the backwards elimination model that we previously
described. In the appendix part B, there are printouts of the 8 MLR’s that we ran in order to get
to the final model. In the first model, we included all variables. By doing backwards elimination,
we removed weight, RAM, battery size, memory, thickness, and secondary camera megapixels
to get to the best fit model. When we removed processor speed, the standard error went up so we
put it back into the model, and that is our final model that is displayed above.
We then looked at all of the descriptive statistics and scatter plots for all our independent
variables and determined that we wanted to include all of them in order to get the most holistic
and representative view of the entire smartphone market.
We next looked at all observations’ respective residuals, identified all outliers, leverage points,
and influence points, and considered what to do with them. In the end, we thought all of them
were normal so we didn’t remove any individual observations.
We finally looked at the correlation between all independent variables to see if there was any
cases of multicollinearity. As discussed, we identified the multicollinear relationship between
Android and iOS, but concluded that it did not affect our model.
Summary
Through our research, we can conclude that hardware specs and software both have a very
significant impact on CNET ratings.
Many hardware components don’t have a relationship with CNET rating, but the ones that do
have a significant relationship with rating are parts of the core user experience of the phone.
Camera and screen resolution both have statistically significant and highly influential
relationship with CNET rating, because the camera and the screen of the phone are the two most
used pieces of hardware on a phone by its’ users. Reviewers highly value being able to take
quality pictures and having a very vibrant and clear display.
19. Interestingly, price had a strong positive relationship with CNET rating, which we believe is
explained by the fact that price represents overall value of a phone. If it is a higher priced phone,
than users will get a lot of quality out of that phone in hardware and software. It’s important to
price phones in a reasonable manner, but executives should focus on adding better camera and
screen components and spend time developing their operating system than pricing their phones
very competitively low.
Because processor speed and RAM both don’t have a statistically significant relationship with
CNET rating, it is critical to ensure users have a fast and fluid user experience through an
efficient and bug-free operating system.
If a phone was using Android, on average, the phone got a 1.355 lower review score than an iOS
phone due to a lower quality user experience for mass consumers and poor implementation by
phone manufacturers, which is usually caused by adding skins that then have bugs or slow the
phone down. To fix this, manufacturers should focus on creating as much of a bug free Android
experience for their phones by either using pure Android or only adding a few extra core features
that your target market clearly expresses a demanded need. If adding extra features, take the time
to ensure that they are bug-free and programmed well in an efficient manner in terms of speed.
If a phone was using BlackBerry, on average, the phone got a 1.317 lower review score than an
iOS phone due to a confusing, outdated, and buggy user experience. Only BlackBerry produces
Android phones, so they should focus on updating the operating system frequently by working
on improving overall OS efficiency every update and a few core apps every update, prioritizing
the most used first.
If a phone was using Windows Phone, on average, the phone got a 1.027 lower review score than
an iOS phone due to not having as many features as iOS that are user friendly, easy to
understand, and execute quickly. However, we think that Windows has the lowest negative
coefficient, because Microsoft has focused on improving efficiency of the OS with every update,
has a consistent user interface, and prioritizes most important core apps and features.
Additionally, Microsoft manufactures the majority of phones using Windows Phone so they are
able to integrate fast software with fast internal components.
In the end, we recommend that phone manufacturer executives should pay top dollar for the best
camera and screen displays on their phones that are present in the market and that they highly
prioritize making a phone’s operating system fast, fluid, and easy to understand over adding a
multitude of additional features.
Works Cited
20. Rubino, Daniel, Kevin Michaluk, Phil Nickinson, and Rene Ritchie. "Gigahertz,
Megapixels, and Millimeters - Do Specs Matter at All? - Talk Mobile." Android Central.
Mobile Nations, 15 July 2013. Web. 1 Nov. 2015.
Mies, Ginny. "The Phone Specs That Matter." TechHive. PC World, 7 Mar. 2014. Web.
1 Nov. 2015.
Appendix
A. Independent Variables Scatter Plots
1. Baseline Price:
2. Main Camera Megapixels: