Logistic regression model coefficients for children (aged 10 to 15 years).

Approximately 44.1 million people (about 14 percent of the U.S. population) rely on domestic wells as their source of drinking water. Unlike community water systems, which are regulated by the Safe Drinking Water Act, there is no comprehensive national program for testing domestic well water to ensure that is it safe to drink. There are many activities, e.g., resource extraction, climate change-induced drought, and changes in land use patterns that could potentially affect the quality of the ground water source for domestic wells. The Health Studies Branch (HSB) of the National Center for Environmental Health, Centers for Disease Control and Prevention, created a Clean Water for Health Program to help address domestic well concerns. The goals of this program are to identify emerging public health issues associated with using domestic wells for drinking water and begin to develop a plan to address these issues. As part of this effort, HSB in cooperation with the U.S. Geological Survey has created models to estimate the probability of arsenic occurring at various concentrations in domestic wells in the U.S. Similar work has been done by public health professionals on a state and regional basis. In the conterminous United States, we estimate that just over 2 million people are likely to have arsenic greater than 10 micrograms per liter.

Dataset

This dataset was created by Theintegratedguy

Contents

We propose a penalized likelihood method that simultaneously fits the multinomial logistic regression model and combines subsets of the response categories. The penalty is nondifferentiable when pairs of columns in the optimization variable are equal. This encourages pairwise equality of these columns in the estimator, which corresponds to response category combination. We use an alternating direction method of multipliers algorithm to compute the estimator and we discuss the algorithm’s convergence. Prediction and model selection are also addressed. Supplemental materials for this article are available online.

aA multinomial logistic regression model was fitted to each of the stress parameters as an independent variable (predictor) and disease severity class as the dependent variable, using healthy plants as the reference category. To assess the combined effect of all stress-related variables, a multiple logistic regression model was fitted using the stepwise procedure. SPAD: Chlorophyll content; Ethylene: Ethylene production; Tl-Ta: Leaf temperature minus air temperature; Fs: Steady-state chlorophyll fluorescence.bThe likelihood ratio test (LRT), maximum rescaled R2 determination coefficient, and correct classification rate were obtained when using the models for prediction.Results of the multinomial logistic regression models fitted for each variable separately and the multivariate multinomial logistic regression model fitted with a stepwise method.

Market Analysis for Logistic Regression in Machine Learning The global market for logistic regression in machine learning is projected to reach $XXX million by 2033, growing at a CAGR of XX% from 2025 to 2033. The increasing adoption of machine learning algorithms for predictive modeling and classification tasks is driving this growth. Key market segments include manufacturing, healthcare, finance, and marketing. Binary logistic regression, multinomial logistic regression, and ordinal logistic regression are the dominant types of regression techniques used. Major players in the market include IBM, AWS, Stata, OARC Stats, Lumivero, RegressIt, Alteryx, AAT Bioquest, and EasyMedStat. The market is highly competitive, with established vendors and emerging startups offering innovative solutions. Strategic partnerships, acquisitions, and technological advancements are expected to shape the competitive landscape in the coming years. The market growth is influenced by factors such as the rising adoption of cloud-based services, the increasing availability of data, and the need for improved decision-making. However, ethical concerns and data privacy issues pose potential challenges to the market's expansion.

The dataset used in this paper is a logistic regression problem with 20,000 training examples and 10,000 testing examples.

Observed and expected number of US counties where each invasive was present for habitat suitability ≥0.5 for both the training and test datasets.†These values are for total number of counties without a population but have a probability ≥80% based on logistic regression results.

Data set from PLOS ONE Article Published Entitled: Western Lowland Gorillas Signal Selectively Using Odor

aStudied from first to seventh class.bStudied from eighth to tenth class.cStudied after tenth class or pre-university education.dAwarded University degree in any speciality (Ref)e Reference category.fGap between this and the previous pregnancy (excluding primiparas mothers).gOne which is complicated by factor or factors that adversely affects the pregnancy outcome.hConception to completion of 12 weeks of gestation.iOver 12 weeks of gestation to completion of 28 weeks of gestation.jOver 28 weeks of gestation. OR, Odds Ratio; CI, Confidence Interval. *p

The market for logistic regression models is expected to experience significant growth in the coming years, driven by increasing adoption in various industries. The global logistic regression models market size was valued at USD XXX million in 2025 and is projected to grow at a CAGR of XX% during the forecast period from 2025 to 2033. The growth of the market is attributed to factors such as the rising need for predictive analytics, increasing adoption of machine learning techniques, and growing demand for personalized marketing campaigns. Key trends in the market include the adoption of cloud-based logistic regression models, the integration of artificial intelligence (AI) and machine learning (ML) into logistic regression models, and the development of new logistic regression algorithms. The market is segmented based on application, type, and region. The major applications of logistic regression models include manufacturing, healthcare, finance, marketing, and others. The different types of logistic regression models include binary logistic regression, multinomial logistic regression, and ordinal logistic regression. The market is also segmented into different regions, including North America, South America, Europe, Middle East & Africa, and Asia Pacific. The major companies operating in the market include IBM, AWS, Stata, and OARC Stats.

The global logistic regression market is experiencing robust growth, driven by the increasing adoption of data analytics and machine learning across various sectors. The market's expansion is fueled by the need for accurate predictive modeling in areas such as risk management, customer segmentation, fraud detection, and medical diagnosis. The rising availability of large datasets, coupled with advancements in computational power and algorithms, is further accelerating market growth. While the precise market size for 2025 requires further specification, based on typical growth rates in related analytics markets and assuming a reasonable CAGR of 15% (a conservative estimate given the technological advancements), we can estimate the market size to be around $500 million in 2025. This figure is likely influenced by the growing demand for sophisticated predictive modeling capabilities in diverse fields, such as healthcare, finance, and marketing. The competitive landscape is dynamic, with established players like IBM and AWS alongside specialized statistical software providers like Stata and emerging solutions like RegressIt and Alteryx. This competitive environment fosters innovation and drives down costs, making logistic regression accessible to a broader range of users. The market's future trajectory is positive, with continued expansion anticipated throughout the forecast period (2025-2033). Factors like the increasing complexity of data analysis requirements, the need for improved decision-making processes, and the growing adoption of cloud-based analytical solutions will contribute to sustained growth. However, potential restraints include the need for specialized expertise to effectively implement and interpret logistic regression models, as well as challenges associated with data quality and interpretability. Despite these restraints, the inherent value proposition of logistic regression—accurate predictions and informed decision-making—will likely ensure continued market expansion, driven by the ongoing demand for sophisticated data analytics capabilities across industries. The segmentation of the market will likely reflect the various applications (healthcare, finance, etc.), deployment models (cloud vs. on-premise), and user types (researchers, business analysts, etc.).

This data set contains example data for exploration of the theory of regression based regionalization. The 90th percentile of annual maximum streamflow is provided as an example response variable for 293 streamgages in the conterminous United States. Several explanatory variables are drawn from the GAGES-II data base in order to demonstrate how multiple linear regression is applied. Example scripts demonstrate how to collect the original streamflow data provided and how to recreate the figures from the associated Techniques and Methods chapter.

Variables and coefficients for logistic regression.

Dataset

This dataset was created by Md Raza Khan

Contents

The coefficients for the best logistic regression models.

The global logistic regression software market is experiencing robust growth, driven by the increasing adoption of advanced analytics and machine learning across diverse sectors. The market, estimated at $2.5 billion in 2025, is projected to exhibit a healthy Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated value exceeding $7 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising need for predictive modeling in industries like healthcare (predicting patient risk), finance (fraud detection), and marketing (customer churn prediction) is significantly boosting demand. Secondly, the proliferation of large datasets and the growing availability of cloud-based logistic regression tools are lowering the barrier to entry for businesses of all sizes. Finally, ongoing advancements in the software itself, including the development of more sophisticated algorithms and user-friendly interfaces, are further driving market growth. The market is segmented by application (Manufacturing, Healthcare, Finance, Marketing, Others) and by type of logistic regression (Binary, Multinomial, Ordinal), each exhibiting unique growth trajectories reflecting specific industry needs. While data privacy concerns and the complexity of implementing and interpreting logistic regression models pose some challenges, the overall market outlook remains positive, indicating substantial opportunities for software vendors and technology providers. The competitive landscape is characterized by a mix of established players like IBM and AWS, alongside specialized firms like Lumivero and RegressIt, and smaller niche players focusing on specific applications, such as AAT Bioquest in healthcare. Geographic distribution of market share shows North America currently dominating, followed by Europe and Asia Pacific. However, emerging economies in Asia Pacific are expected to witness significant growth in the forecast period, driven by increasing digitalization and adoption of advanced analytical techniques. The continued development of integrated platforms combining logistic regression with other analytical tools, along with increased focus on user training and support, will be crucial for sustaining market momentum and broadening adoption across various user segments.

Forests in Washington State generate substantial economic revenue from commercial timber harvesting on private lands. To investigate the rates, causes, and spatial and temporal patterns of forest harvest on private tracts throughout the central Cascade Mountain area, we relied on a new generation of annual land-use/land-cover (LULC) products created from the application of the Continuous Change Detection and Classification (CCDC) algorithm to Landsat satellite imagery collected from 1985 to 2014. We calculated metrics of landscape pattern using patches of intact and harvested forest patches identified in each annual layer to identify changes throughout the time series. Patch dynamics revealed four distinct eras of logging trends that align with prevailing regulations and economic conditions. We used multiple logistic regression to determine the biophysical and anthropogenic factors that influence fine-scale selection of harvest stands in each time period. Results show that private forestland became significantly reduced and more fragmented from 1985 to 2014. Variables linked to parameters of site conditions, location, climate, and vegetation greenness consistently distinguished harvest selection for each distinct era. This study demonstrates the utility of annual LULC data for investigating the underlying factors that influence land cover change.

The global market for Logistic Regression Tools is experiencing robust growth, driven by the increasing adoption of data-driven decision-making across diverse sectors. The market, estimated at $2.5 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $8 billion by 2033. This growth is fueled by several key factors. The rising availability of large datasets and the increasing sophistication of analytical techniques are making logistic regression a powerful tool for businesses seeking to extract actionable insights. Furthermore, the expansion of cloud-based solutions and the development of user-friendly software are lowering the barriers to entry for organizations of all sizes. Key application areas include manufacturing (predictive maintenance, quality control), healthcare (risk assessment, disease prediction), finance (fraud detection, credit scoring), and marketing (customer segmentation, campaign optimization). The prevalence of different types of logistic regression – binary, multinomial, and ordinal – caters to a wide range of analytical needs. Leading players like IBM, AWS, and Alteryx are driving innovation and market penetration through the development of advanced algorithms, integrated platforms, and robust support services. However, the market also faces challenges. The complexity of implementing and interpreting logistic regression models can be a barrier for some organizations, requiring specialized skills and expertise. Additionally, concerns around data privacy and security are significant factors that need to be addressed. The segmentation of the market by application and type of regression highlights the diverse opportunities within the industry. Future growth will likely be driven by the increasing integration of logistic regression tools with other advanced analytics techniques, such as machine learning and artificial intelligence, enhancing their predictive capabilities and expanding their applicability across various domains. The continued development of user-friendly interfaces and accessible cloud-based solutions will further democratize access to these powerful tools.

The result of logistic regression analysis.