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.

This dataset is based on the unit and regression testing available on https://github.com/USEPA/Stormwater-Management-Model/actions

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.

The variable selection problem in the context of Linear Regression for large databases is analysed. The problem consists in selecting a small subset of independent variables that can perform the prediction task optimally. This problem has a wide range of applications. One important type of application is the design of composite indicators in various areas (sociology and economics, for example). Other important applications of variable selection in linear regression can be found in fields such as chemometrics, genetics, and climate prediction, among many others. For this problem, we propose a Branch & Bound method. This is an exact method and therefore guarantees optimal solutions. We also provide strategies that enable this method to be applied in very large databases (with hundreds of thousands of cases) in a moderate computation time. A series of computational experiments shows that our method performs well compared with well-known methods in the literature and with commercial software.

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/)

Abstract The valuation of real estate, which assists in the definition of market value, is an important science with a wide field of action, which includes the collection of taxes, commercial transactions, insurance and judicial expertise. This study presents the construction of a linear regression model to determine the market value (dependent variable) of residential apartments in the city of Fortaleza-CE. The studied database presents 17,493 apartments, divided into 227 plan types in a total of 154 projects launched between the years of 2011 and 2014. The model developed was obtained using Multiple Linear Regression associated with the Ridge Regression technique to solve the existing multicollinearity problem. In the analysis of 30 variables (12 quantitative and 18 dummy type qualitative variables), an equation with 6 variables was reached, which meets the theoretical assumptions for its existence.

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

This dataset is having data of customers who buys clothes online. The store offers in-store style and clothing advice sessions. Customers come in to the store, have sessions/meetings with a personal stylist, then they can go home and order either on a mobile app or website for the clothes they want.

The company is trying to decide whether to focus their efforts on their mobile app experience or their website.

This CSV dataset (numbered 1–8) demonstrates the construction processes of the regression models using machine learning methods, which are used to plot Fig. 2–7. The CSV file of 1.LSM_R^2 (plotting Fig. 2) shows the data of the relationship between estimated values and actual values when the least-squares method was used for a model construction. In the CSV file 2.PCR_R^2 (plotting Fig. 3), the number of the principal components was varied from 1 to 5 during the construction of a model using the principal component regression. The data in the CSV file 3.SVR_R^2 (plotting Fig. 4) is the result of the construction using the support vector regression. The hyperparameters were decided by the comprehensive combination from the listed candidates by exploring hyperparameters with maximum R2 values. When a deep neural network was applied to the construction of a regression model, NNeur., NH.L. and NL.T. were varied. The CSV file 4.DNN_HL (plotting Fig. 5a)) shows the changes in the relationship between estimated values and actual values at each NH.L.. Similarly, changes in the relationships between estimated values and actual values in the case NNeur. or NL.T. were varied in the CSV files 5.DNN_ Neur (plotting Fig. 5b)) and 6.DNN_LT (plotting Fig. 5c)). The data in the CSV file 7.DNN_R^2 (plotting Fig. 6) is the result using optimal NNeur., NH.L. and NL.T.. In the CSV file 8.R^2 (plotting Fig. 7), the validity of each machine learning method was compared by showing the optimal results for each method. Experimental conditions Supply volume of the raw material: 25–125 mL Addition rate of TiO2: 5.0–15.0 wt% Operation time: 1–15 min Rotation speed: 2,200–5,700 min-1 Temperature: 295–319 K Nomenclature NNeur.: the number of neurons NH.L.: the number of hidden layers NL.T.: the number of learning times

The primary objective from this project was to acquire historical shoreline information for all of the Northern Ireland coastline. Having this detailed understanding of the coast’s shoreline position and geometry over annual to decadal time periods is essential in any management of the coast.The historical shoreline analysis was based on all available Ordnance Survey maps and aerial imagery information. Analysis looked at position and geometry over annual to decadal time periods, providing a dynamic picture of how the coastline has changed since the start of the early 1800s.Once all datasets were collated, data was interrogated using the ArcGIS package – Digital Shoreline Analysis System (DSAS). DSAS is a software package which enables a user to calculate rate-of-change statistics from multiple historical shoreline positions. Rate-of-change was collected at 25m intervals and displayed both statistically and spatially allowing for areas of retreat/accretion to be identified at any given stretch of coastline.The DSAS software will produce the following rate-of-change statistics:Net Shoreline Movement (NSM) – the distance between the oldest and the youngest shorelines.Shoreline Change Envelope (SCE) – a measure of the total change in shoreline movement considering all available shoreline positions and reporting their distances, without reference to their specific dates.End Point Rate (EPR) – derived by dividing the distance of shoreline movement by the time elapsed between the oldest and the youngest shoreline positions.Linear Regression Rate (LRR) – determines a rate of change statistic by fitting a least square regression to all shorelines at specific transects.Weighted Linear Regression Rate (WLR) - calculates a weighted linear regression of shoreline change on each transect. It considers the shoreline uncertainty giving more emphasis on shorelines with a smaller error.The end product provided by Ulster University is an invaluable tool and digital asset that has helped to visualise shoreline change and assess approximate rates of historical change at any given coastal stretch on the Northern Ireland coast.

The diamond is 58 times harder than any other mineral in the world, and its elegance as a jewel has long been appreciated. Forecasting diamond prices is challenging due to nonlinearity in important features such as carat, cut, clarity, table, and depth. Against this backdrop, the study conducted a comparative analysis of the performance of multiple supervised machine learning models (regressors and classifiers) in predicting diamond prices. Eight supervised machine learning algorithms were evaluated in this work including Multiple Linear Regression, Linear Discriminant Analysis, eXtreme Gradient Boosting, Random Forest, k-Nearest Neighbors, Support Vector Machines, Boosted Regression and Classification Trees, and Multi-Layer Perceptron. The analysis is based on data preprocessing, exploratory data analysis (EDA), training the aforementioned models, assessing their accuracy, and interpreting their results. Based on the performance metrics values and analysis, it was discovered that eXtreme Gradient Boosting was the most optimal algorithm in both classification and regression, with a R2 score of 97.45% and an Accuracy value of 74.28%. As a result, eXtreme Gradient Boosting was recommended as the optimal regressor and classifier for forecasting the price of a diamond specimen. Methods Kaggle, a data repository with thousands of datasets, was used in the investigation. It is an online community for machine learning practitioners and data scientists, as well as a robust, well-researched, and sufficient resource for analyzing various data sources. On Kaggle, users can search for and publish various datasets. In a web-based data-science environment, they can study datasets and construct models.

Site-specific multiple linear regression models were developed for one beach in Ohio (three discrete sampling sites) and one beach in Pennsylvania to estimate concentrations of Escherichia coli (E. coli) or the probability of exceeding the bathing-water standard for E. coli in recreational waters used by the public. Traditional culture-based methods are commonly used to estimate concentrations of fecal indicator bacteria, such as E. coli; however, results are obtained 18 to 24 hours post sampling and do not accurately reflect current water-quality conditions. Beach-specific mathematical models use environmental and water-quality variables that are easily and quickly measured as surrogates to estimate concentrations of fecal-indicator bacteria or to provide the probability that a State recreational water-quality standard will be exceeded. When predictive models are used for beach closure or advisory decisions, they are referred to as “nowcasts”. Software designed for model development by the U.S. Environmental Protection Agency (Virtual Beach) was used. The selected model for each beach was based on a combination of explanatory variables including, most commonly, turbidity, water temperature, change in lake level over 24 hours, and antecedent rainfall. Model results are used by managers to report water-quality conditions to the public through the Great Lakes NowCast in 2019 (https://pa.water.usgs.gov/apps/nowcast/). Model performance in 2019 (sensitivity, specificity, and accuracy) was compared to using the previous day's E. coli concentration (persistence method).

The SAS and R code and the accompanied data are related to the paper "The Advantages of Quantile Regression in Scientometrics"

Context

The reason behind providing the data-set is that currently I'm doing my Master's in Computer Science, in my second semester I have chosen Data Science class, so in this class they are teaching me Linear Regression, so I decided to provide a set of x and y values, which not only helps me and also helps others.

Content

The dataset contains x and y values: x values are just iterating values. y values depend on the equation y = mx+c.

Inspiration

Everyone on this planet should be familiar (at least Computer Science students, etc.) about Linear Regression, so calculate the trend line, R^2, coefficient and intercept values.

This data repository contains original files (fcs) of flow cytometry experiments. The data was used to demonstrate the use of stochastic regression to quantify subpopulations of cells that have distinctly different genome copies per cell within a heterogenous population of Escherichia coli (E. coli) cells. This new approach gives estimates of signal and noise, the former of which is used for analysis, and the latter is used to quantify uncertainty. By separating these two components, the signal and noise can be compared independently to evaluate measurement quality across different experimental conditions. The files contain experiments from a single stock of Escherichia coli cells that was diluted to different concentrations, stained with Hoechst33342, and acquired on a CytoFLEX LX under the same acquisition conditions. ?Control_Hoechst? is a biologic control sample stained only with Hoechst. ?RainbowBeads? is a control of hard-dyed fluorescent beads with 8 distinct peaks of known fluorescent intensities per manufacturer documentation. ?Test_double? indicates test samples with double fluorescent probe staining, the fractional number (e.g. 0.7) indicates the dilution factor from the stock, and the integer at the end represents the technical replicate.The downloaded Exp_20230921_1_Cyto-A-journal.zip file contains 14 files in .fcs format, which requires suitable software to read/analyze data (i.e., FCS Express).

This data set includes input data for the development of regression models to predict chloride from specific conductance (SC) data at 56 U. S. Geological Survey water quality monitoring stations in the eastern United States. Each site has 20 or more simultaneous observations of SC and chloride. Data were downloaded from the National Water Information System (NWIS) using the R package dataRetrieval. Datasets for each site were evaluated and outliers were removed prior to the development of the regression model. This file contains only the final input dataset for the regression models. Please refer to Moore and others (in review) for more details. Moore, J., R. Fanelli, and A. Sekellick. In review. High-frequency data reveal deicing salts drive elevated conductivity and chloride along with pervasive and frequent exceedances of the EPA aquatic life criteria for chloride in urban streams. Submitted to Environmental Science and Technology.

The partner company’s historical data could be utilized in developing a data-driven prediction model with project division details as its inputs and project division labor-hours as the desired output. The BIM models contain 42 design features and 1559 records, each record denoting a division of fabrication. The BIM design features are listed in Table 1. Labor-hours spent on each division were extracted from job costing databases serving as the output parameter in the regression model. Although the variables in Table 1 are all considered related, there are certain inter-correlations between them and some variables can be explained by others. For instance, material length and weight are highly correlated; by knowing one, the other can be deduced. Therefore, a variable selection technique is instrumental in removing these inter-correlations in an analytical manner. It is noteworthy that the dataset was linearly scaled prior to performing analyses in order not to reveal sensitive information of the partner company without distorting patterns and relationships inherent in the data.

This is data used for regression model in Stata format

In this study, we introduce the count-based Morgan fingerprint (C-MF) to represent chemical structures of contaminants and develop machine learning (ML)-based predictive models for their activities and properties. Compared with the binary Morgan fingerprint (B-MF), C-MF not only qualifies the presence or absence of an atom group but also quantifies its counts in a molecule. We employ six different ML algorithms (ridge regression, SVM, KNN, RF, XGBoost, and CatBoost) to develop models on 10 contaminant-related data sets based on C-MF and B-MF to compare them in terms of the model’s predictive performance, interpretation, and applicability domain (AD). Our results show that C-MF outperforms B-MF in nine of 10 data sets in terms of model predictive performance. The advantage of C-MF over B-MF is dependent on the ML algorithm, and the performance enhancements are proportional to the difference in the chemical diversity of data sets calculated by B-MF and C-MF. Model interpretation results show that the C-MF-based model can elucidate the effect of atom group counts on the target and have a wider range of SHAP values. AD analysis shows that C-MF-based models have an AD similar to that of B-MF-based ones. Finally, we developed a “ContaminaNET” platform to deploy these C-MF-based models for free use.

The Unified Forecast System (UFS) is a community-based, coupled, comprehensive Earth Modeling System. The ufs-weather-model (UFS-WM) is the model source of the UFS for NOAA’s operational numerical weather prediction applications. The UFS-WM Regression Test (RT) is the testing software to ensure that previously developed and tested capabilities in UFS-WM still work after code changes are integrated into the system. It is required that UFS-WM RTs are performed successfully on the required Tier-1 platforms whenever code changes are made to the UFS-WM. The results of the UFS-WM RTs are summarized in log files and these files will be committed to the UFS-WM repository along with the code changes. Currently, the UFS-WM RTs have been developed to support several applications targeted for operational implementations including the global weather forecast, subseasonal to seasonal forecasts, hurricane forecast, regional rapid refresh forecast, and ocean analysis.

At this time, there are 123 regression tests to support the UFS applications. The tests are evolving along with the development merged to the UFS-WM code repository. The regression test framework has been developed in the UFS-WM to run these tests on tier-1 supported systems. Each of the regression tests require a set of input data files and configuration files. The configuration files include namelist and model configuration files residing within the UFS-WM code repository. The input data includes initial conditions, climatology data, and fixed data sets such as orographic data and grid specification data. In addition, the regression test framework maintains baseline data created from certain revisions of the UFS-WM code repository. When code changes are not expected to alter baseline results, regression tests will be performed against current baseline and as a result, the regression test log files are created – revealing a summary of no change within the results. If the code changes are expected to alter model results, impact to the regression tests will be specified in the pull request. The code changes and model results will be reviewed and confirmed. Once the model results are confirmed, a new baseline will be generated. In some cases, new input data will need to be added or old data will need to be replaced, these data will be put in the input data location with proper timestamp, and regression tests will be performed with the updated data sets.

The regression test framework serves as a test system to maintain the functionalities in the UFS-WM. The input data and the baselines need to be maintained and updated during the code integration to support the regression tests.