The dataset originates from the book "Practical Statistics for Data Scientists" by Peter Bruce, Andrew Bruce, and Peter Gedeck.

Context:

A company selling a high-value service wants to determine which of two web presentations is more effective at selling. Due to the high value and infrequent nature of the sales, as well as the lengthy sales cycle, it would take too long to accumulate enough sales data to identify the superior presentation. Therefore, the company uses a proxy variable to measure effectiveness.

A proxy variable stands in for the true variable of interest, which may be unavailable, too costly, or too time-consuming to measure directly. In this case, the proxy variable is the amount of time users spend on a detailed interior page that describes the service.

Content:

The dataset includes a total of 36 sessions across the two web presentations: 21 sessions for page A and 15 sessions for page B. The goal is to determine if users spend more time on page B compared to page A. If users spend more time on page B, it would suggest that page B is more effective at engaging potential customers, and therefore, does a better selling job.

The time is expressed in hundredths of seconds. For example, a value of 0.1 indicates 10 seconds, and a value of 2.53 indicates 253 seconds.

A/B Testing Software Market Outlook

The global A/B Testing Software market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach USD 4.5 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of around 15.8% during the forecast period. The significant growth factor contributing to this market is the escalating need for data-driven decision-making processes across various industries, which has led to an increased adoption of A/B testing software to optimize user experiences and improve conversion rates.

The surge in digital transformation initiatives is a major growth driver for the A/B Testing Software market. As enterprises increasingly shift their operations online, the need to enhance digital customer experiences becomes paramount. A/B testing software plays a critical role in enabling businesses to experiment with different versions of web pages, mobile apps, and email campaigns to determine which variations perform better, thereby facilitating data-driven decisions that can lead to higher conversion rates and improved ROI.

Another significant growth factor is the rising adoption of A/B testing in the e-commerce sector. Online retailers are continuously striving to optimize their websites and applications to improve user engagement and sales. A/B testing software offers these retailers the ability to test different elements such as product displays, call-to-action buttons, and checkout processes, helping them identify the most effective strategies to increase customer retention and sales. The growing competition in the e-commerce market further fuels the demand for advanced A/B testing solutions.

Moreover, advancements in artificial intelligence (AI) and machine learning (ML) are propelling the A/B Testing Software market. AI and ML algorithms can analyze vast amounts of data generated from A/B tests, providing deeper insights and more accurate predictions of user behavior. This technological enhancement not only makes A/B testing more efficient but also allows for more complex and nuanced experiments, ultimately leading to better optimization outcomes. Companies that leverage these advanced technologies in their A/B testing processes gain a significant competitive edge.

In the realm of digital transformation, Ai Price Optimization emerges as a pivotal tool for businesses looking to maximize their revenue streams. By leveraging advanced algorithms and data analytics, Ai Price Optimization enables companies to dynamically adjust their pricing strategies based on real-time market conditions, consumer behavior, and competitive pricing. This approach not only helps in achieving optimal pricing but also enhances customer satisfaction by offering prices that are perceived as fair and competitive. As businesses increasingly adopt digital solutions to stay ahead in the competitive landscape, the integration of Ai Price Optimization with A/B testing software can provide a comprehensive strategy for optimizing both pricing and user experiences. This synergy allows businesses to test various pricing models and their impact on conversion rates, ultimately leading to more informed and effective pricing decisions.

Component Analysis

The A/B Testing Software market is categorized into two main components: Software and Services. The software segment leads the market, driven by the increasing demand for sophisticated testing tools that offer robust analytics and reporting capabilities. These tools enable businesses to conduct comprehensive tests on websites, mobile apps, and email campaigns, providing actionable insights that drive optimization strategies. The software is continually evolving, integrating features like multivariate testing and AI-driven analytics, which enhance its utility and effectiveness.

Within the software segment, cloud-based solutions are gaining remarkable traction due to their scalability, flexibility, and cost-efficiency. Cloud-based A/B testing software allows organizations to conduct tests without the need for significant upfront investments in infrastructure. This accessibility is particularly advantageous for small and medium enterprises (SMEs) that may have limited IT resources. Additionally, cloud solutions offer seamless integration with other digital marketing tools, further enhancing their appeal.

The services segment encompasses consulting, implementation, and support services, which are crucial for the successful deployment and operation of

Dataset

This dataset was created by Osuolale Emmanuel

Released under CC BY-SA 3.0

Contents

Newly released AB Testing Software Market analysis report by Future Market Insights reveals that global sales of AB Testing Software Market in 2023 are estimated at USD 1,211.3 million. With a 11.7% projected growth rate during 2023 to 2033, the market is expected to reach a valuation of USD 3,673.5 million by 2033.

AB Testing Software Market size was valued at USD 716.94 Million in 2024 and is projected to reach USD 1727.5 Million by 2031, growing at a CAGR of 11.62% from 2024 to 2031.

Global AB Testing Software Market Overview

Running an A/B test that directly compares different variations against a current experience helps the user to be focused. It asks questions about the changes in the website or application to collect data on the impact of the changes to better the experience better. Web ranking depends a great deal on A/B testing, which is expected to propel the AB Testing Software globally. In addition, google supports and encourages A/B testing and it has stated that performing an A/B or multivariate test in no way carries any inherent risk to the website's search rank. A/B testing permits individuals as well as teams and companies to make careful and conscious changes for better user experiences while collecting data from the results.

It helps to create a better opinion about the user experience to help the programmer in the long run. The users' acknowledgments are the most prominent driver of the global AB Testing Software Market. Furthermore, another plus point of the software is the option to run different experiments. This software helps to kick off the experiments and awaits visitors to participate. Their interaction with each experience is counted, measured, and compared to determine each of the performances of the app or webpage to make the user experience better.

All of these factors are expected to bode well for the global AB Testing Software Market. However, AB Testing Software requires very expensive apparatus, which can result in a high initial setup cost. These factors are likely to restrict the use of AB Testing Software particularly in SME sectors. Also, fluctuating prices of raw materials may slow down the growth of the market.

A/B Testing Tools Market Outlook

The global A/B Testing Tools market size was estimated at USD 0.8 billion in 2023 and is projected to reach USD 2.3 billion by 2032, growing at a CAGR of 12.5% during the forecast period. This robust growth is driven by the increasing demand for data-driven decision-making and optimization of digital experiences across various industries.

One of the primary growth factors for the A/B Testing Tools market is the surge in digital transformation across multiple sectors. With businesses increasingly moving their operations online, there is a heightened need to optimize user experiences on digital platforms. A/B testing tools enable organizations to make data-backed decisions, leading to improved customer satisfaction and higher conversion rates. Companies are keen to leverage these tools to gain a competitive edge in the crowded digital marketplace.

Another significant growth driver is the rapid adoption of mobile devices and applications. As mobile traffic continues to overtake desktop traffic, businesses are focusing more on optimizing their mobile platforms. A/B testing tools designed for mobile optimization allow organizations to experiment with different user interfaces and functionalities, ensuring a seamless and engaging experience for mobile users. This focus on mobile optimization is expected to propel the market further.

The growing emphasis on personalized marketing is also fueling the demand for A/B testing tools. Personalized marketing strategies, which involve tailoring content and offers to individual user preferences, have proven to be highly effective. A/B testing tools allow marketers to test various personalization strategies to determine the most effective ones. This capability is driving higher adoption rates, particularly in industries like retail and e-commerce where personalized user experiences are critical for success.

In terms of regional outlook, North America is expected to dominate the A/B Testing Tools market during the forecast period. The region's strong technological infrastructure, coupled with the early adoption of advanced digital marketing tools, supports this dominance. Additionally, the presence of major market players and high investment in R&D activities further bolster market growth in this region. However, the Asia Pacific region is anticipated to exhibit the highest CAGR, driven by rapid digitalization and increasing internet penetration in countries like China and India.

Conversion Rate Optimization Software plays a pivotal role in enhancing the effectiveness of A/B testing tools by providing businesses with the ability to fine-tune their digital strategies. This software aids in analyzing user behavior and identifying key areas for improvement, allowing companies to implement changes that can significantly boost their conversion rates. By integrating Conversion Rate Optimization Software with A/B testing tools, businesses can gain deeper insights into customer preferences and tailor their digital experiences accordingly. This synergy not only improves user satisfaction but also drives higher engagement and revenue growth. As the digital landscape becomes increasingly competitive, leveraging such software becomes essential for businesses aiming to optimize their online presence and achieve their marketing objectives.

Component Analysis

The A/B Testing Tools market is segmented by component into software and services. The software segment holds a significant share of the market, driven by the increasing demand for comprehensive testing solutions that offer ease of use and integration with other digital marketing tools. Software solutions provide various functionalities such as split testing, multivariate testing, and funnel analysis, enabling businesses to conduct thorough and efficient experiments. The continuous advancements in software capabilities, including AI-driven analytics, are likely to further enhance the adoption of A/B testing software.

The services segment, although smaller compared to software, is gaining traction as businesses seek expert consulting and implementation support. Service providers offer a range of services including strategy development, test implementation, and result analysis. These services are particularly valuable for organizations that lack in-house expertise in A/B testing. Additionally, ongoing support and training services ensure that businesses can effectively utilize

AB-Testing-Project

Experiment Overview: Free Trial Screener

At the time of this experiment, Udacity courses currently have two options on the course overview page: "start the free trial", and "access course materials". If the student clicks "start the free trial", they will be asked to enter their credit card information, and then they will be enrolled in a free trial for the paid version of the course. After 14 days, they will automatically be charged unless they cancel first. If the student clicks "access course materials", they will be able to view the videos and take the quizzes for free, but they will not receive coaching support or a verified certificate, and they will not submit their final project for feedback.

In the experiment, Udacity tested a change where if the student clicked "start the free trial", they were asked how much time they had available to devote to the course. If the student indicated 5 or more hours per week, they would be taken through the checkout process as usual. If they indicated fewer than 5 hours per week, a message would appear indicating that Udacity courses usually require a greater time commitment for successful completion, and suggesting that the student might like to access the course materials for free. At this point, the student would have the option to continue enrolling in the free trial or access the course materials for free instead. This screenshot shows what the experiment looks like.

1. Experiment Design

1.1 Unit of Diversion (provided by Udacity)

The unit of diversion is a cookie, although if the student enrols in the free trial, they are tracked by user-id from that point forward. The same user-id cannot enrol in the free trial in free trial twice. For users that do not enrol, their user-id is not tracked in the experiment, even if they were signed in when they visited the course overview page.

1.2 Initial Hypothesis

The hypothesis was that this might set clearer expectations for students upfront, thus reducing the number of frustrated students who left the free trial because they didn't have enough time—without significantly reducing the number of students to continue past the free trial and eventually complete the course. If this hypothesis held true, Udacity could improve the overall student experience and improve coaches' capacity to support students who are likely to complete the course. (Provided by Udacity)

Based on the information above, we can set some initial hypothesis: (these are just iniinitial hypothesis and we will revise them further)

1. H0: the change has no effect on the number of students who enrol on the free trial.
   H1: the change reduces the number of students who enrol on the free trial.

2. H0: the change has no effect on the number of students who leave the free trial.
   H1: the change reduces the number of students who leave the free trial.

3. H0: the change has no effect on the probability of students who continue the free trial after 14 days.
   H1: the change increases the probability of students who continue the free trial after 14 days.
   (since we cannot say the number will be increased or decreased here, we use probability.)

1.3 Metric Choice

there are seven choices from Udacity below.

• Number of cookies: That is, a number of unique cookies to view the course overview page. (dmin=3000)
• Number of user-ids: That is, the number of users who enrol in the free trial. (dmin=50)
• Number of clicks: That is, the number of unique cookies to click the "Start free trial" button (which happens before the free trial screener is the trigger). (dmin=240)
• Click-through-probability: That is, the number of unique cookies to click the "Start free trial" button divided by the number of unique cookies to view the course overview page. (dmin=0.01)
• Gross conversion: That is, the number of user-number of user-ids to complete checkout and enrol in the free trial divided by the number of unique cookies to click the "Start free trial" button. (dmin= 0.01)
• Retention: That is, the number of user-ids to remain enrolled past the 14-day boundary (and thus make at least one payment) divided by the number of user-ids to complete checkout. (dmin=0.01)
• Net conversion: That is, number of user-ids to renumber of user-ids to remain enrolled past the 14-day boundary (and thus make at least one payment) divided by the number of user-ids to remain enrolled past the 14-day boundary (and thus make at least one payment) divided by the number of unique cookies to click the "Start free trial" button. (dmin= 0.0075)

dmin means the practical significance boundary for each metric, that is, the difference that would have to be observed before that was a meaningful change for the business, is given in par...

A/B Testing Technology Market Outlook

The global A/B Testing Technology market size was valued at approximately USD 900 million in 2023 and is projected to reach around USD 2.5 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 12.1% during the forecast period. This growth is primarily driven by the increasing adoption of data-driven decision-making processes across various industries to enhance user experience and optimize business outcomes.

One of the key drivers of the A/B Testing Technology market is the escalating need for personalized user experiences. Businesses are increasingly focusing on delivering tailored experiences to their customers to stay competitive. A/B testing enables companies to experiment with different variants of web pages, mobile apps, or other digital platforms to determine which version yields the best performance. This drive for personalization is particularly pronounced in sectors like e-commerce and media, where user engagement and satisfaction directly impact revenue.

Another growth factor is the rapid digital transformation across various industries. Organizations are investing heavily in digital strategies to improve operational efficiency and customer engagement. The need to validate these digital strategies through robust testing mechanisms is fueling the demand for A/B testing solutions. The ability to make data-backed decisions helps businesses reduce risks associated with new implementations and ensures higher returns on investment.

The proliferation of mobile devices and applications is also augmenting the growth of the A/B Testing Technology market. With the increasing usage of smartphones and mobile internet, businesses are focusing on optimizing mobile user experiences. A/B testing tools are being extensively used to enhance mobile app performance, user interface design, and overall user satisfaction. This trend is expected to continue as mobile penetration increases globally.

Regionally, North America holds the largest share of the A/B Testing Technology market, driven by the presence of major technology companies and a high adoption rate of advanced testing solutions. The region's emphasis on technological innovation and customer-centric approaches further propels market growth. Meanwhile, the Asia Pacific region is expected to witness significant growth during the forecast period, attributable to the rapid digitalization efforts and increasing e-commerce activities in countries like China and India.

In the context of optimizing digital experiences, Product Optimization Tools play a crucial role in enhancing the effectiveness of A/B testing strategies. These tools are designed to streamline the process of testing and refining various product features, ensuring that businesses can deliver the most engaging and efficient user experiences. By integrating Product Optimization Tools into their A/B testing frameworks, companies can gain deeper insights into user behavior, allowing for more targeted and impactful optimizations. This integration not only improves the accuracy of test results but also accelerates the overall optimization cycle, enabling businesses to respond swiftly to market demands and consumer preferences.

Component Analysis

The A/B Testing Technology market is segmented by component into software and services. The software segment dominates the market, owing to the extensive use of A/B testing tools and platforms that offer various functionalities such as test creation, implementation, and result analysis. These software solutions are designed to be user-friendly, allowing businesses to perform tests without requiring extensive technical expertise. This ease of use is a significant factor driving the adoption of A/B testing software.

In addition, advancements in software capabilities, such as real-time data analysis and integration with other marketing tools, are enhancing the value proposition of A/B testing solutions. For instance, many A/B testing platforms now offer AI-driven recommendations, automated test setups, and multivariate testing capabilities. These advancements enable more sophisticated and efficient testing processes, thereby attracting more users to adopt these tools.

On the other hand, the services segment, which includes consulting, training, and support services, is also growing steadily. As businesses increasingly recognize the importance of A/B testing, they

This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by sex. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-sex.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level. These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents. In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders.) Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning. Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020. Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis will almost certainly be miscalculating or counting the same values multiple times. To analyze the most current data, only use the latest extract date. Antibody tests that are missing dates are not included in the dataset; as dates are identified, these events are added. Lags between occurrence and report of cases and tests can be assessed by comparing counts and rates across multiple data extract dates. For further details, visit: • https://www1.nyc.gov/site/doh/covid/covid-19-data.page • https://github.com/nychealth/coronavirus-data

Dataset

This dataset was created by Mohamed-El haddad

Contents

This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by week of testing. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/trends/antibody-by-week.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level. These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents. In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders.) Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning. Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020. Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis will almost certainly be miscalculating or counting the same values multiple times. To analyze the most current data, only use the latest extract date. Antibody tests that are missing dates are not included in the dataset; as dates are identified, these events are added. Lags between occurrence and report of cases and tests can be assessed by comparing counts and rates across multiple data extract dates. For further details, visit: • https://www1.nyc.gov/site/doh/covid/covid-19-data.page • https://github.com/nychealth/coronavirus-data

Dataset

This dataset was created by Tetiana Klimonova

Contents

It contains the following files:

This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by modified ZIP Code Tabulation Area (ZCTA) of residence. Modified ZCTA reflects the first non-missing address within NYC for each person reported with an antibody test result. This unit of geography is similar to ZIP codes but combines census blocks with smaller populations to allow more stable estimates of population size for rate calculation. It can be challenging to map data that are reported by ZIP Code. A ZIP Code doesn’t refer to an area, but rather a collection of points that make up a mail delivery route. Furthermore, there are some buildings that have their own ZIP Code, and some non-residential areas with ZIP Codes. To deal with the challenges of ZIP Codes, the Health Department uses ZCTAs which solidify ZIP codes into units of area. Often, data reported by ZIP code are actually mapped by ZCTA. The ZCTA geography was developed by the U.S. Census Bureau. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-modzcta.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level. These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents. In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders) Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning. Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020. Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis wi

Siemens Healthineers' Atellica IM Sars-CoV-2 Total (COV2T) test has a sensitivity of 100 percent and a specificity of 99.8 percent. Specificity is the ability of a test to give a true negative test, that means that the person has not been exposed to SARS-CoV-2 and therefore not developed antibodies.

A/B testing is an effective method to assess the potential impact of two treatments. For A/B tests conducted by IT companies like Meta and LinkedIn, the test users can be connected and form a social network. Users’ responses may be influenced by their network connections, and the quality of the treatment estimator of an A/B test depends on how the two treatments are allocated across different users in the network. This paper investigates optimal design criteria based on some commonly used outcome models, under assumptions of network-correlated outcomes or network interference. We demonstrate that the optimal design criteria under these network assumptions depend on several key statistics of the random design vector. We propose a framework to develop algorithms that generate rerandomization designs meeting the required conditions of those statistics under a specific assumption. Asymptotic distributions of these statistics are derived to guide the specification of parameters in the algorithms. We validate the proposed algorithms using both synthetic and real-world networks.

Dataset

This dataset was created by Mohammad Bahmanabadi

Released under MIT

Contents

This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by ZIP Code Tabulation Area (ZCTA) neighborhood poverty group. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-poverty.csv

Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level.

These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents.

In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders.)

Neighborhood-level poverty groups were classified in a manner consistent with Health Department practices to describe and monitor disparities in health in NYC. Neighborhood poverty measures are defined as the percentage of people earning below the Federal Poverty Threshold (FPT) within a ZCTA. The standard cut-points for defining categories of neighborhood-level poverty in NYC are: • Low: <10% of residents in ZCTA living below the FPT • Medium: 10% to <20% • High: 20% to <30% • Very high: ≥30% residents living below the FPT The ZCTAs used for classification reflect the first non-missing address within NYC for each person reported with an antibody test result.

Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning. Rates for poverty were calculated using direct standardization for age at diagnosis and weighting by the US 2000 standard population. Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020.

Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis will almost certainly be miscalculating or counting the same values multiple times. To analyze the most current data, only use the latest extract date. Antibody tests that are missing dates are not included in the dataset; as dates are identified, these events are added. Lags between occurrence and report of cases and tests can be assessed by comparing counts and rates across multiple data extract dates.

For further details, visit: • https://www1.nyc.gov/site/doh/covid/covid-19-data.page • https://github.com/nychealth/coronavirus-data • https://data.cityofnewyork.us/Health/Modified-Zip-Code-Tabulation-Areas-MODZCTA-/pri4-ifjk

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Dataset Description:

This dataset contains website conversion data for Bluetooth speaker sales. The dataset tracks user sessions on different landing page variants, with the primary goal of analyzing conversion rates, user behavior, and other factors influencing sales. It includes detailed user engagement metrics such as time spent, pages visited, device type, sign-in methods, and geographical information.

Use Case:

This dataset can be used for various analytical tasks including:

A/B testing and multivariate analysis to compare landing page designs.
User segmentation by demographics (age, gender, location, etc.).
Conversion rate optimization (CRO) analysis.
Predictive modeling for conversion likelihood based on session characteristics.
Revenue and payment analysis.

The Fluorescent Antibody Test (FAT) market has emerged as a vital segment in the fields of clinical diagnostics and research, particularly due to its role in rapid disease identification and monitoring. This test utilizes antibodies labeled with fluorescent dyes to detect specific antigens in various biological samp

The statistic shows the percentage of public high school students in the United States scoring 3 or higher on at least one Advanced Placement Calculus Exam in 2010 by state. Nationally, the share of the graduating class that demonstrated a mastery of Calculus AB by scoring a 3 or higher on the AP Exam was 3.5 percent in 2010.