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.

Site-specific multiple linear regression models were developed for eight sites in Ohio—six in the Western Lake Erie Basin and two in northeast Ohio on inland reservoirs--to quickly predict action-level exceedances for a cyanotoxin, microcystin, in recreational and drinking waters used by the public. Real-time models include easily- or continuously-measured factors that do not require that a sample be collected. Real-time models are presented in two categories: (1) six models with continuous monitor data, and (2) three models with on-site measurements. Real-time models commonly included variables such as phycocyanin, pH, specific conductance, and streamflow or gage height. Many of the real-time factors were averages over time periods antecedent to the time the microcystin sample was collected, including water-quality data compiled from continuous monitors. Comprehensive models use a combination of discrete sample-based measurements and real-time factors. Comprehensive models were useful at some sites with lagged variables (< 2 weeks) for cyanobacterial toxin genes, dissolved nutrients, and (or) N to P ratios. Comprehensive models are presented in three categories: (1) three models with continuous monitor data and lagged comprehensive variables, (2) five models with no continuous monitor data and lagged comprehensive variables, and (3) one model with continuous monitor data and same-day comprehensive variables. Funding for this work was provided by the Ohio Water Development Authority and the U.S. Geological Survey Cooperative Water Program.

Summary : Fuel demand is shown to be influenced by fuel prices, people's income and motorization rates. We explore the effects of electric vehicle's rates in gasoline demand using this panel dataset.

Files : dataset.csv - Panel dimensions are the Brazilian state ( i ) and year ( t ). The other columns are: gasoline sales per capita (ln_Sg_pc), prices of gasoline (ln_Pg) and ethanol (ln_Pe) and their lags, motorization rates of combustion vehicles (ln_Mi_c) and electric vehicles (ln_Mi_e) and GDP per capita (ln_gdp_pc). All variables are all under the natural log function, since we use this to calculate demand elasticities in a regression model.

adjacency.csv - The adjacency matrix used in interaction with electric vehicles' motorization rates to calculate spatial effects. At first, it follows a binary adjacency formula: for each pair of states i and j, the cell (i, j) is 0 if the states are not adjacent and 1 if they are. Then, each row is normalized to have sum equal to one.

regression.do - Series of Stata commands used to estimate the regression models of our study. dataset.csv must be imported to work, see comment section.

dataset_predictions.xlsx - Based on the estimations from Stata, we use this excel file to make average predictions by year and by state. Also, by including years beyond the last panel sample, we also forecast the model into the future and evaluate the effects of different policies that influence gasoline prices (taxation) and EV motorization rates (electrification). This file is primarily used to create images, but can be used to further understand how the forecasting scenarios are set up.

Sources: Fuel prices and sales: ANP (https://www.gov.br/anp/en/access-information/what-is-anp/what-is-anp) State population, GDP and vehicle fleet: IBGE (https://www.ibge.gov.br/en/home-eng.html?lang=en-GB) State EV fleet: Anfavea (https://anfavea.com.br/en/site/anuarios/)

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

Source/Credit: Michael Grogan https://github.com/MGCodesandStats https://github.com/MGCodesandStats/datasets/blob/master/cars.csv

Sample dataset for regression analysis. Given 5 attributes (age, gender, miles driven per day, debt, and income) predict how much someone will spend on purchasing a car. All 5 of the input attributes have been scaled to be in 0 to 1 range. Training set has 723 training examples. Test set has 242 test examples.

This dataset will be used in an upcoming Galaxy Training Network tutorial (https://training.galaxyproject.org/training-material/topics/statistics/) on use of feedforward neural networks for regression analysis.

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Description:

The data consists of Marks of students including their study time & number of courses. The dataset is downloaded from UCI Machine Learning Repository.

Properties of the Dataset:
Number of Instances: 100
Number of Attributes: 3 including the target variable.

The project is simple yet challenging as it is has very limited features & samples. Can you build regression model to capture all the patterns in the dataset, also maitaining the generalisability of the model?

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the student marks wrt multiple features.
• Also evaluate the models & compare their respective scores like R2, RMSE, etc.

A set of databases has been curated to cater to the academic community's research needs in the realm of Machine Learning algorithm performance, particularly in scenarios with limited sample sizes and regression problems. These databases encompass varying sample sizes, data dimensionality, and the linearity of the response variable.

1) Data Introduction • The Student Performance (Multiple Linear Regression) Dataset is designed to analyze the relationship between students’ learning habits and academic performance. Each sample includes key indicators related to learning, such as study hours, sleep duration, previous test scores, and the number of practice exams completed.

2) Data Utilization (1) Characteristics of the Student Performance (Multiple Linear Regression) Dataset: • The target variable, Hours Studied, quantitatively represents the amount of time a student has invested in studying. The dataset is structured to allow modeling and inference of learning behaviors based on correlations with other variables.

(2) Applications of the Student Performance (Multiple Linear Regression) Dataset: • AI-Based Study Time Prediction Models: The dataset can be used to develop regression models that estimate a student’s expected study time based on inputs like academic performance, sleep habits, and engagement patterns. • Behavioral Analysis and Personalized Learning Strategies: It can be applied to identify students with insufficient study time and design personalized study interventions based on academic and lifestyle patterns.

This dataset contains the data used in the paper: "The Impact of Altitude Training on NCAA Division I Female Swimmers’ Performance" being submitted to the International Journal of Performance Analysis in Sport.

We include Stata syntax (dummy_dataset_create.do) that creates a panel dataset for negative binomial time series regression analyses, as described in our paper "Examining methodology to identify patterns of consulting in primary care for different groups of patients before a diagnosis of cancer: an exemplar applied to oesophagogastric cancer". We also include a sample dataset for clarity (dummy_dataset.dta), and a sample of that data in a spreadsheet (Appendix 2).

The variables contained therein are defined as follows:

case: binary variable for case or control status (takes a value of 0 for controls and 1 for cases).

patid: a unique patient identifier.

time_period: A count variable denoting the time period. In this example, 0 denotes 10 months before diagnosis with cancer, and 9 denotes the month of diagnosis with cancer,

ncons: number of consultations per month.

period0 to period9: 10 unique inflection point variables (one for each month before diagnosis). These are used to test which aggregation period includes the inflection point.

burden: binary variable denoting membership of one of two multimorbidity burden groups.

We also include two Stata do-files for analysing the consultation rate, stratified by burden group, using the Maximum likelihood method (1_menbregpaper.do and 2_menbregpaper_bs.do).

Note: In this example, for demonstration purposes we create a dataset for 10 months leading up to diagnosis. In the paper, we analyse 24 months before diagnosis. Here, we study consultation rates over time, but the method could be used to study any countable event, such as number of prescriptions.

These four labeled data sets are targeted at ordinal quantification. The goal of quantification is not to predict the label of each individual instance, but the distribution of labels in unlabeled sets of data.

With the scripts provided, you can extract CSV files from the UCI machine learning repository and from OpenML. The ordinal class labels stem from a binning of a continuous regression label.

We complement this data set with the indices of data items that appear in each sample of our evaluation. Hence, you can precisely replicate our samples by drawing the specified data items. The indices stem from two evaluation protocols that are well suited for ordinal quantification. To this end, each row in the files app_val_indices.csv, app_tst_indices.csv, app-oq_val_indices.csv, and app-oq_tst_indices.csv represents one sample.

Our first protocol is the artificial prevalence protocol (APP), where all possible distributions of labels are drawn with an equal probability. The second protocol, APP-OQ, is a variant thereof, where only the smoothest 20% of all APP samples are considered. This variant is targeted at ordinal quantification tasks, where classes are ordered and a similarity of neighboring classes can be assumed.

Usage

You can extract four CSV files through the provided script extract-oq.jl, which is conveniently wrapped in a Makefile. The Project.toml and Manifest.toml specify the Julia package dependencies, similar to a requirements file in Python.

Preliminaries: You have to have a working Julia installation. We have used Julia v1.6.5 in our experiments.

Data Extraction: In your terminal, you can call either

make

(recommended), or

julia --project="." --eval "using Pkg; Pkg.instantiate()"
julia --project="." extract-oq.jl

Outcome: The first row in each CSV file is the header. The first column, named "class_label", is the ordinal class.

Further Reading

Implementation of our experiments: https://github.com/mirkobunse/regularized-oq

The meta-learning method proposed in this paper addresses the issue of small-sample regression in the application of engineering data analysis, which is a highly promising direction for research. By integrating traditional regression models with optimization-based data augmentation from meta-learning, the proposed deep neural network demonstrates excellent performance in optimizing glass fiber reinforced plastic (GFRP) for wrapping concrete short columns. When compared with traditional regression models, such as Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Radial Basis Function Neural Networks (RBFNN), the meta-learning method proposed here performs better in modeling small data samples. The success of this approach illustrates the potential of deep learning in dealing with limited amounts of data, offering new opportunities in the field of material data analysis.

In power analysis for multivariable Cox regression models, variance of the estimated log-hazard ratio for the treatment effect is usually approximated by inverting the expected null information matrix. Because in many typical power analysis settings assumed true values of the hazard ratios are not necessarily close to unity, the accuracy of this approximation is not theoretically guaranteed. To address this problem, the null variance expression in power calculations can be replaced with one of alternative expressions derived under the assumed true value of the hazard ratio for the treatment effect. This approach is explored analytically and by simulations in the present paper. We consider several alternative variance expressions, and compare their performance to that of the traditional null variance expression. Theoretical analysis and simulations demonstrate that while the null variance expression performs well in many non-null settings, it can also be very inaccurate, substantially underestimating or overestimating the true variance in a wide range of realistic scenarios, particularly those where the numbers of treated and control subjects are very different and the true hazard ratio is not close to one. The alternative variance expressions have much better theoretical properties, confirmed in simulations. The most accurate of these expressions has a relatively simple form - it is the sum of inverse expected event counts under treatment and under control scaled up by a variance inflation factor.

BackgroundOverweight/ obesity among under-five children is an emerging public health issue of the twenty-first century. Due to the quick nutritional and epidemiological change, non-communicable diseases, premature death, disability, and reproductive disorders have grown in low-income countries. Besides, little attention has been given. Therefore, we aimed to explore spatial variations and predictors of overweight/obesity among under-five children in Ethiopia using a geospatial technique.MethodsA total weighted sample of 3,609 under-five children was included in the study. A cross-sectional study was conducted using a nationally representative sample of the 2019 Ethiopia Mini Demographic and Health Survey data set. ArcGIS version 10.8 was used to explore the spatial variation of obesity. SaTScan version 9.6 software was used to analyze the spatial cluster detection of overweight/obesity. Ordinary least square and geographically weighted regression analysis were employed to assess the association between an outcome variable and explanatory variables. A p-value of less than 0.05 was used to declare it statistically significant.ResultsThe spatial distribution of overweight/obesity among under-five children in Ethiopia was clustered (Global Moran’s I = 0.27, p-value

Analysis-ready tabular data from "Predicting spatial-temporal patterns of diet quality and large herbivore performance using satellite time series" in Ecological Applications, Kearney et al., 2021. Data is tabular data only, summarized to the pasture scale. Weight gain data for individual cattle and the STARFM-derived Landsat-MODIS fusion imagery can be made available upon request. Resources in this dataset:Resource Title: Metadata - CSV column names, units and descriptions. File Name: Kearney_et_al_ECOLAPPL_Patterns of herbivore - metada.docxResource Description: Column names, units and descriptions for all CSV files in this datasetResource Title: Fecal quality data. File Name: Kearney_etal2021_Patterns_of_herbivore_Data_FQ_cln.csvResource Description: Field-sampled fecal quality (CP = crude protein; DOM = digestible organic matter) data and phenology-related APAR metrics derived from 30 m daily Landsat-MODIS fusion satellite imagery. All data are paddock-scale averages and the paddock is the spatial scale of replication and week is the temporal scale of replication. Fecal samples were collected by USDA-ARS staff from 3-5 animals per paddock (10% - 25% of animals in each herd) weekly during each grazing season from 2014 to 2019 across 10 different paddocks at the Central Plains Experimental Range (CPER) near Nunn, CO. Samples were analyzed at the Grazingland Animal Nutrition Lab (GANlab, https://cnrit.tamu.edu/index.php/ganlab/) using near infrared spectroscopy (see Lyons & Stuth, 1992; Lyons, Stuth, & Angerer, 1995). Not every herd was sampled every week or every year, resulting in a total of 199 samples. Samples represent all available data at the CPER during the study period and were collected for different research and adaptive management objectives, but following the basic protocol described above. APAR metrics were derived from the paddock-scale APAR daily time series (all paddock pixels averaged daily to create a single paddock-scale time series). All APAR metrics are calculated for the week that corresponds to the week that fecal quality samples were collected in the field. See Section 2.2.4 of the corresponding manuscript for a complete description of the APAR metrics. Resource Title: Monthly ADG. File Name: Kearney_etal2021_Patterns_of_herbivore_Data_ADG_monthly_cln.csvResource Description: Monthly average daily gain (ADG) of cattle weights at the paddock scale and the three satellite-derived metrics used to build regression model to predict AD: crude protein (CP), digestible organic matter (DOM) and aboveground net herbaceous production (ANHP). Data table also includes stocking rate (animal units per hectare) used as an interaction term in the ADG regression model and all associated data to derive each of these variables (e.g., sampling start and end dates, 30 m daily Landsat-MODIS fusion satellite imagery-derived APAR metrics, cattle weights, etc.). We calculated paddock-scale average daily gain (ADG, kg hd-1 day-1) from 2000-2019 for yearlings weighed approximately every 28-days during the grazing season across 6 different paddocks with stocking densities of 0.08 – 0.27 animal units (AU) ha-1, where one AU is equivalent to a 454 kg animal. It is worth noting that AU’s change as a function of both the number of cattle within a paddock and the size of individual animals, the latter of which changes within a single grazing season. This becomes important to consider when using sub-seasonal weight data for fast-growing yearlings. For paddock-scale ADG, we first calculated ADG for each individual yearling as the difference between the weights obtained at the end and beginning of each period, divided by the number of days in each period, and then averaged for all individuals in the paddock. We excluded data from 2013 due to data collection inconsistencies. We note that most of the monthly weight data (97%) is from 3 paddocks where cattle were weighed every year, whereas in the other 3 paddocks, monthly weights were only measured during 2017-2019. Apart from the 2013 data, which were not comparable to data from other years, the data represents all available weight gain data for CPER to maximize spatial-temporal coverage and avoid potential bias from subjective decisions to subset the data. Data may have been collected for different projects at different times, but was collected in a consistent way. This resulted in 269 paddock-scale estimates of monthly ADG, with robust temporal, but limited spatial, coverage. CP and DOM were estimated from a random forest model trained from the five APAR metrics: rAPAR, dAPAR, tPeak, iAPAR and iAPAR-dry (see manuscript Section 2.3 for description). APAR metrics were derived from the paddock-scale APAR daily time series (all paddock pixels averaged daily to create a single paddock-scale time series). All APAR metrics are calculated as the average of the approximately 28-day period that corresponds to the ADG calculation. See Section 2.2.4 of the manuscript for a complete description of the APAR metrics. ANHP was estimated from a linear regression model developed by Gaffney et al. (2018) to calculate net aboveground herbaceous productivity (ANHP; kg ha-1) from iAPAR. We averaged the coefficients of 4 spatial models (2013-2016) developed by Gaffney et al. (2018), resulting in the following equation: ANHP = -26.47 + 2.07(iAPAR) We first calculated ANHP for each day of the grazing season at the paddock scale, and then took the average ANHP for the 28-day period. REFERENCES: Gaffney, R., Porensky, L. M., Gao, F., Irisarri, J. G., Durante, M., Derner, J. D., & Augustine, D. J. (2018). Using APAR to predict aboveground plant productivity in semi-aid rangelands: Spatial and temporal relationships differ. Remote Sensing, 10(9). doi: 10.3390/rs10091474 Resource Title: Season-long ADG. File Name: Kearney_etal2021_Patterns_of_herbivore_Data_ADG_seasonal_cln.csvResource Description: Season-long observed and model-predicted average daily gain (ADG) of cattle weights at the paddock scale. Also includes two variables used to analyze patterns in model residuals: percent sand content and season-long aboveground net herbaceous production (ANHP). We calculated observed paddock-scale ADG for the entire grazing season from 2010-2019 (excluding 2013 due to data collection inconsistencies) by averaging seasonal ADG of each yearling, determined as the difference between the end and starting weights divided by the number of days in the grazing season. This dataset was available for 40 paddocks spanning a range of soil types, plant communities, and topographic positions. Data may have been collected for different projects at different times, but was collected in a consistent way. We note that there was spatial overlap among a small number paddock boundaries across different years since some fence lines were moved in 2012 and 2014. Model-predicted paddock-scale ADG was derived using the monthly ADG regression model described in Sections 2.3.3 and 2.3.4. of the associated manuscript. In short, we predicted season-long cattle weight gains by first predicting daily weight gain for each day of the grazing season from the monthly regression model using a 28-day moving average of model inputs (CP, DOM and ANHP ). We calculated the final ADG for the entire grazing season as the average predicted ADG, starting 28-days into the growing season. Percent sand content was obtained as the paddock-scale average of POLARIS sand content in the upper 0-30 cm. ANHP was calculated on the last day of the grazing season fusing a linear regression model developed by Gaffney et al. (2018) to calculate net aboveground herbaceous productivity (ANHP; kg ha-1) from satellite-derived integrated absorbed photosynthetically active radiation (iAPAR) (see Section 3.1.2 of the associated manuscript). We averaged the coefficients of 4 spatial models (2013-2016) developed by Gaffney et al. (2018), resulting in the following equation: ANHP = -26.47 + 2.07(iAPAR) REFERENCES: Gaffney, R., Porensky, L. M., Gao, F., Irisarri, J. G., Durante, M., Derner, J. D., & Augustine, D. J. (2018). Using APAR to predict aboveground plant productivity in semi-aid rangelands: Spatial and temporal relationships differ. Remote Sensing, 10(9). doi: 10.3390/rs10091474

If this Data Set is useful, and upvote is appreciated. This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school related features) and it was collected by using school reports and questionnaires. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In [Cortez and Silva, 2008], the two datasets were modeled under binary/five-level classification and regression tasks. Important note: the target attribute G3 has a strong correlation with attributes G2 and G1. This occurs because G3 is the final year grade (issued at the 3rd period), while G1 and G2 correspond to the 1st and 2nd-period grades. It is more difficult to predict G3 without G2 and G1, but such prediction is much more useful (see paper source for more details).

This data release supports the following publication: Mast, M. A., 2018, Estimating metal concentrations with regression analysis and water-quality surrogates at nine sites on the Animas and San Juan Rivers, Colorado, New Mexico, and Utah: U.S. Geological Survey Scientific Investigations Report 2018-5116. The U.S. Geological Survey (USGS), in cooperation with the U. S. Environmental Protection Agency (EPA), developed site-specific regression models to estimate concentrations of selected metals at nine USGS streamflow-gaging stations along the Animas and San Juan Rivers. Multiple linear-regression models were developed by relating metal concentrations in discrete water-quality samples to continuously monitored streamflow and surrogate parameters including specific conductance, pH, turbidity, and water temperature. Models were developed for dissolved and total concentrations of aluminum, arsenic, cadmium, iron, lead, manganese, and zinc using water-quality samples collected during 2005–17 by several agencies, using different collection methods and analytical laboratories. Calibration datasets in comma-separated format (CSV) include the variables of sampling date and time, metal concentrations (in micrograms per liter), stream discharge (in cubic feet per second), specific conductance (in microsiemens per centimeter at 25 degrees Celsius), pH, water temperature (in degrees Celsius), turbidity (in nephelometric turbidity units), and calculated seasonal terms based on Julian day. Surrogate parameters and discrete water-quality samples were used from nine sites including Cement Creek at Silverton, Colo. (USGS station 09358550); Animas River below Silverton, Colo. (USGS station 09359020); Animas River at Durango, Colo. (USGS station 09361500); Animas River Near Cedar Hill, N. Mex. (USGS station 09363500); Animas River below Aztec, N. Mex. (USGS station 09364010); San Juan River at Farmington, N. Mex. (USGS station 09365000); San Juan River at Shiprock, N. Mex (USGS Station 09368000); San Juan River at Four Corners, Colo. (USGS station 09371010); and San Juan River near Bluff, Utah (USGS station 09379500). Model archive summaries in pdf format include model statistics, data, and plots and were generated using a R script developed by USGS Kansas Water Science Center available at https://patrickeslick.github.io/ModelArchiveSummary/. A description of each USGS streamflow gaging station along with information about the calibration datasets also are provided.

Data from an optical turbidity sensor deployed at the stream station were recorded at 15-minute intervals by a data logger and uploaded every hour to the USGS database (Anderson, 2005; Wagner, 2006). Suspended-sediment samples were collected using equal width increments or grab sampling techniques (Edwards, 1999). The use of an optical sensor to continuously monitor turbidity provided an accurate estimate of sediment fluctuations without the collection and analysis costs associated with intensive sampling (OSW policy 2016.07; Rasmussen et al., 2009). Turbidity was used as a surrogate for suspended-sediment concentration (SSC), which is a measure of sedimentation and siltation. Regression models were developed between SSC and turbidity using turbidity data from the optical sensor and the SSC data collected from the suspended-sediment samples. For the West Fork of White River East of Fayetteville instantaneous turbidity measurements began on October 11, 2014 and ranged from 0.3 to 1480 Formazin Nephelometric Units (FNU). A total of 64 suspended-sediment samples were collected and ranged from 2 to 1780 milligrams per liter (mg/L) across a range of streamflow from 0.71 to 7770 cubic feet per second (cfs). There were 50 suspended-sediment samples used in the final model after removing quality assurance/quality control samples, samples corresponding with gaps in turbidity data, samples associated with sampling/fouling errors, and extraneous samples from the same storm event which would result in overfitting of the model.

thus preserving patient privacy and confidentiality.This Dataset contains sample data using the PCORnet Common Data Model for running the regression tests supplied with PopMedNet™.

This formatted dataset (AnalysisDatabaseGBD) originates from raw data files from the Institute of Health Metrics and Evaluation (IHME) Global Burden of Disease Study (GBD2017) affiliated with the University of Washington. We are volunteer collaborators with IHME and not employed by IHME or the University of Washington.

The population weighted GBD2017 data are on male and female cohorts ages 15-69 years including noncommunicable diseases (NCDs), body mass index (BMI), cardiovascular disease (CVD), and other health outcomes and associated dietary, metabolic, and other risk factors. The purpose of creating this population-weighted, formatted database is to explore the univariate and multiple regression correlations of health outcomes with risk factors. Our research hypothesis is that we can successfully model NCDs, BMI, CVD, and other health outcomes with their attributable risks.

These Global Burden of disease data relate to the preprint: The EAT-Lancet Commission Planetary Health Diet compared with Institute of Health Metrics and Evaluation Global Burden of Disease Ecological Data Analysis. The data include the following: 1. Analysis database of population weighted GBD2017 data that includes over 40 health risk factors, noncommunicable disease deaths/100k/year of male and female cohorts ages 15-69 years from 195 countries (the primary outcome variable that includes over 100 types of noncommunicable diseases) and over 20 individual noncommunicable diseases (e.g., ischemic heart disease, colon cancer, etc). 2. A text file to import the analysis database into SAS 3. The SAS code to format the analysis database to be used for analytics 4. SAS code for deriving Tables 1, 2, 3 and Supplementary Tables 5 and 6 5. SAS code for deriving the multiple regression formula in Table 4. 6. SAS code for deriving the multiple regression formula in Table 5 7. SAS code for deriving the multiple regression formula in Supplementary Table 7
8. SAS code for deriving the multiple regression formula in Supplementary Table 8 9. The Excel files that accompanied the above SAS code to produce the tables

For questions, please email davidkcundiff@gmail.com. Thanks.