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.

Home Value Insights: A Beginner's Regression Dataset

This dataset is designed for beginners to practice regression problems, particularly in the context of predicting house prices. It contains 1000 rows, with each row representing a house and various attributes that influence its price. The dataset is well-suited for learning basic to intermediate-level regression modeling techniques.

Features:

1. Square_Footage: The size of the house in square feet. Larger homes typically have higher prices.
2. Num_Bedrooms: The number of bedrooms in the house. More bedrooms generally increase the value of a home.
3. Num_Bathrooms: The number of bathrooms in the house. Houses with more bathrooms are typically priced higher.
4. Year_Built: The year the house was built. Older houses may be priced lower due to wear and tear.
5. Lot_Size: The size of the lot the house is built on, measured in acres. Larger lots tend to add value to a property.
6. Garage_Size: The number of cars that can fit in the garage. Houses with larger garages are usually more expensive.
7. Neighborhood_Quality: A rating of the neighborhood’s quality on a scale of 1-10, where 10 indicates a high-quality neighborhood. Better neighborhoods usually command higher prices.
8. House_Price (Target Variable): The price of the house, which is the dependent variable you aim to predict.

Potential Uses:

1. Beginner Regression Projects: This dataset can be used to practice building regression models such as Linear Regression, Decision Trees, or Random Forests. The target variable (house price) is continuous, making this an ideal problem for supervised learning techniques.

2. Feature Engineering Practice: Learners can create new features by combining existing ones, such as the price per square foot or age of the house, providing an opportunity to experiment with feature transformations.

3. Exploratory Data Analysis (EDA): You can explore how different features (e.g., square footage, number of bedrooms) correlate with the target variable, making it a great dataset for learning about data visualization and summary statistics.

4. Model Evaluation: The dataset allows for various model evaluation techniques such as cross-validation, R-squared, and Mean Absolute Error (MAE). These metrics can be used to compare the effectiveness of different models.

Versatility:

• The dataset is highly versatile for a range of machine learning tasks. You can apply simple linear models to predict house prices based on one or two features, or use more complex models like Random Forest or Gradient Boosting Machines to understand interactions between variables.

• It can also be used for dimensionality reduction techniques like PCA or to practice handling categorical variables (e.g., neighborhood quality) through encoding techniques like one-hot encoding.

• This dataset is ideal for anyone wanting to gain practical experience in building regression models while working with real-world features.

Students typically find linear regression analysis of data sets in a biology classroom challenging. These activities could be used in a Biology, Chemistry, Mathematics, or Statistics course. The collection provides student activity files with Excel instructions and Instructor Activity files with Excel instructions and solutions to problems.

Students will be able to perform linear regression analysis, find correlation coefficient, create a scatter plot and find the r-square using MS Excel 365. Students will be able to interpret data sets, describe the relationship between biological variables, and predict the value of an output variable based on the input of an predictor variable.

Despite recent papers on problems associated with full-model and stepwise regression, their use is still common throughout ecological and environmental disciplines. Alternative approaches, including generating multiple models and comparing them post-hoc using techniques such as Akaike's Information Criterion (AIC), are becoming more popular. However, these are problematic when there are numerous independent variables and interpretation is often difficult when competing models contain many different variables and combinations of variables. Here, we detail a new approach, REVS (Regression with Empirical Variable Selection), which uses all-subsets regression to quantify empirical support for every independent variable. A series of models is created; the first containing the variable with most empirical support, the second containing the first variable and the next most-supported, and so on. The comparatively small number of resultant models (n = the number of predictor variables) means that post-hoc comparison is comparatively quick and easy. When tested on a real dataset – habitat and offspring quality in the great tit (Parus major) – the optimal REVS model explained more variance (higher R2), was more parsimonious (lower AIC), and had greater significance (lower P values), than full, stepwise or all-subsets models; it also had higher predictive accuracy based on split-sample validation. Testing REVS on ten further datasets suggested that this is typical, with R2 values being higher than full or stepwise models (mean improvement = 31% and 7%, respectively). Results are ecologically intuitive as even when there are several competing models, they share a set of “core” variables and differ only in presence/absence of one or two additional variables. We conclude that REVS is useful for analysing complex datasets, including those in ecology and environmental disciplines.

ABSTRACT Regression analysis is highly relevant to agricultural sciences since many of the factors studied are quantitative. Researchers have generally used polynomial models to explain their experimental results, mainly because much of the existing software perform this analysis and a lack of knowledge of other models. On the other hand, many of the natural phenomena do not present such behavior; nevertheless, the use of non-linear models is costly and requires advanced knowledge of language programming such as R. Thus, this work presents several regression models found in scientific studies, implementing them in the form of an R package called AgroReg. The package comprises 44 analysis functions with 66 regression models such as polynomial, non-parametric (loess), segmented, logistic, exponential, and logarithmic, among others. The functions provide the coefficient of determination (R2), model coefficients and the respective p-values from the t-test, root mean square error (RMSE), Akaike’s information criterion (AIC), Bayesian information criterion (BIC), maximum and minimum predicted values, and the regression plot. Furthermore, other measures of model quality and graphical analysis of residuals are also included. The package can be downloaded from the CRAN repository using the command: install.packages(“AgroReg”). AgroReg is a promising analysis tool in agricultural research on account of its user-friendly and straightforward functions that allow for fast and efficient data processing with greater reliability and relevant information.

Multiple regression analysis for log HOMA-R.

Anterior chamber depth (ACD) is a quantitative trait associated with primary angle closure glaucoma (PACG). Although ACD is highly heritable, known genetic variations explain a small fraction of the phenotypic variability. The purpose of this study was to identify additional ACD-influencing loci using strains of mice. Cohorts of 86 N2 and 111 F2 mice were generated from crosses between recombinant inbred BXD24/TyJ and wild-derived CAST/EiJ mice. Using anterior chamber optical coherence tomography, mice were phenotyped at 10–12 weeks of age, genotyped based on 93 genome-wide SNPs, and subjected to quantitative trait locus (QTL) analysis. In an analysis of ACD among all mice, six loci passed the significance threshold of p = 0.05 and persisted after multiple regression analysis. These were on chromosomes 6, 7, 11, 12, 15 and 17 (named Acdq6, Acdq7, Acdq11, Acdq12, Acdq15, and Acdq17, respectively). Our findings demonstrate a quantitative multi-genic pattern of ACD inheritance in mice and identify six previously unrecognized ACD-influencing loci. We have taken a unique approach to studying the anterior chamber depth phenotype by using mice as genetic tool to examine this continuously distributed trait.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along open-ocean sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

1. Dataset: House pricing dataset containing 21 columns and 21613 rows.
2. Programming Language : R
3. Objective : To predict house prices by creating a model
4. Steps : A) Import the dataset B) Install and run libraries C) Data Cleaning - Remove Null Values , Change Data Types , Dropping of Columns which are not important D) Data Analysis - (i)Linear Regression Model was used to establish the relationship between the dependent variable (price) and other independent variable (ii) Outliers were identified and removed (iii) Regression model was run once again after removing the outliers (iv) Multiple R- squared was calculated which indicated the independent variables can explain 73% change/ variation in the dependent variable (v) P value was less than that of alpha 0.05 which shows it is statistically significant. (vi) Interpreting the meaning of the results of the coefficients (vii) Checked the assumption of multicollinearity (viii) VIF(Variance inflation factor) was calculated for all the independent variables and their absolute value was found to be less than 5. Hence, there is not threat of multicollinearity and that we can proceed with the independent variables specified.

This data release contains input data and programs (scripts) used to estimate monthly water demand for retail customers of Providence Water, located in Providence, Rhode Island. Explanatory data and model outputs are from July 2014 through June 2021. Models of per capita (for single-family residential customers) or per connection (for multi-family residential, commercial, and industrial customers) water use were developed using multiple linear regression. The dependent variables, provided by Providence Water, are the monthly number of connections and gallons of water delivered to single- and multi-family residential, commercial, and industrial connections. Potential independent variables (from online sources) are climate variables (temperature and precipitation), economic statistics, and a drought statistic. Not all independent variables were used in all of the models. The data are provided in data tables and model files. The data table RIWaterUseVariableExplanation.csv describes the explanatory variables and their data sources. The data table ProvModelInputData.csv provides the monthly water-use data that are the independent variables and the monthly climatic and economic data that are the dependent variables. The data table DroughtInputData.csv provides the weekly U.S. drought monitor index values that were processed to formulate a potential independent variable. The R script model_water_use.R runs the models that predict water use. The other two R scripts (load_preprocess_input_data.R and model_water_use_functions.R) are not run explicitly but are called from the primary script model_water_use.R. Regression equations produced by the models can be used to predict water demand throughout Rhode Island.

This repository contains supplementary materials related to the study "𝐒𝐮𝐫𝐯𝐞𝐲 𝐨𝐧 𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐫𝐞𝐬𝐮𝐥𝐭𝐬 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐢𝐧 𝐁𝐫𝐚𝐳𝐢𝐥𝐢𝐚𝐧 𝐜𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐨𝐫𝐢𝐞𝐬: 𝐏𝐫𝐨𝐟𝐢𝐥𝐢𝐧𝐠 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐦𝐮𝐥𝐭𝐢𝐯𝐚𝐫𝐢𝐚𝐭𝐞 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐚𝐧𝐝 𝐚 '𝐍𝐞𝐰 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬' 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡". The dataset, figures, exported results, and analysis scripts are included to ensure full transparency and reproducibility of the research findings.

Multiple regression models through stepwise addition of independent environmental variables.

This dataset contains a collection of 100 randomly generated data points representing the relationship between the number of hours a student spends studying and their corresponding performance, measured as a score. The data has been generated to simulate a real-world scenario where study hours are assumed to influence academic outcomes, making it an excellent resource for linear regression analysis and other machine learning tasks.

Each row in the dataset consists of:

Hours: The number of hours a student dedicates to studying, ranging between 0 and 10 hours. Scores: The student's performance score, represented as a percentage, ranging from 0 to 100. Use Cases: This dataset is particularly useful for:

Linear Regression: Exploring how study hours influence student performance, fitting a regression line to predict scores based on study time. Data Science & Machine Learning: Practicing regression analysis, training models, and applying other predictive algorithms. Educational Research: Simulating data-driven insights into student behavior and performance metrics. Features: 100 rows of data. Continuous numerical variables suitable for regression tasks. Generated for educational purposes, making it ideal for students, teachers, and beginners in machine learning and data science. Potential Applications: Build a linear regression model to predict student scores. Investigate the correlation between study time and performance. Apply data visualization techniques to better understand the data. Use the dataset to experiment with model evaluation metrics like Mean Squared Error (MSE) and R-squared.

STAD-R is a set of R programs that performs descriptive statistics, in order to make boxplots and histograms. STAD-R was designed because is necessary before than the thing, check if the dataset have the same number of repetitions, blocks, genotypes, environments, if we have missing values, where and how many, review the distributions and outliers, because is important to be sure that the dataset is complete and have the correct structure for do and other kind of analysis.

*Standardized units.r-squared = 0.94.A multiple linear regression of the US News and World Report Score and its contributors.

We reported the R code used to study the relationship between variables using a simple linear regression model in the software R (R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. Accessed 24/09/2021).

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along open-ocean sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

Examples of adjusted R2 linear regression values obtained for the other subject who presented 9 good ICA components for beta activity.

Multiple regression model summary of market groups and time periods.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along open-ocean sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.