Regression ranks among the most popular statistical analysis methods across many research areas, including psychology. Typically, regression coefficients are displayed in tables. While this mode of presentation is information-dense, extensive tables can be cumbersome to read and difficult to interpret. Here, we introduce three novel visualizations for reporting regression results. Our methods allow researchers to arrange large numbers of regression models in a single plot. Using regression results from real-world as well as simulated data, we demonstrate the transformations which are necessary to produce the required data structure and how to subsequently plot the results. The proposed methods provide visually appealing ways to report regression results efficiently and intuitively. Potential applications range from visual screening in the model selection stage to formal reporting in research papers. The procedure is fully reproducible using the provided code and can be executed via free-of-charge, open-source software routines in R.

Australian and New Zealand journal of statistics Acceptance Rate - ResearchHelpDesk - The Australian & New Zealand Journal of Statistics is an international journal managed jointly by the Statistical Society of Australia and the New Zealand Statistical Association. Its purpose is to report significant and novel contributions in statistics, ranging across articles on statistical theory, methodology, applications and computing. The journal has a particular focus on statistical techniques that can be readily applied to real-world problems, and on application papers with an Australasian emphasis. Outstanding articles submitted to the journal may be selected as Discussion Papers, to be read at a meeting of either the Statistical Society of Australia or the New Zealand Statistical Association. The main body of the journal is divided into three sections. The Theory and Methods Section publishes papers containing original contributions to the theory and methodology of statistics, econometrics and probability, and seeks papers motivated by a real problem and which demonstrate the proposed theory or methodology in that situation. There is a strong preference for papers motivated by, and illustrated with, real data. The Applications Section publishes papers demonstrating applications of statistical techniques to problems faced by users of statistics in the sciences, government and industry. A particular focus is the application of newly developed statistical methodology to real data and the demonstration of better use of established statistical methodology in an area of application. It seeks to aid teachers of statistics by placing statistical methods in context. The Statistical Computing Section publishes papers containing new algorithms, code snippets, or software descriptions (for open source software only) which enhance the field through the application of computing. Preference is given to papers featuring publically available code and/or data, and to those motivated by statistical methods for practical problems. In addition, suitable review papers and articles of historical and general interest will be considered. The journal also publishes book reviews on a regular basis. Abstracting and Indexing Information Academic Search (EBSCO Publishing) Academic Search Alumni Edition (EBSCO Publishing) Academic Search Elite (EBSCO Publishing) Academic Search Premier (EBSCO Publishing) CompuMath Citation Index (Clarivate Analytics) Current Index to Statistics (ASA/IMS) Journal Citation Reports/Science Edition (Clarivate Analytics) Mathematical Reviews/MathSciNet/Current Mathematical Publications (AMS) RePEc: Research Papers in Economics Science Citation Index Expanded (Clarivate Analytics) SCOPUS (Elsevier) Statistical Theory & Method Abstracts (Zentralblatt MATH) ZBMATH (Zentralblatt MATH)

The statistic shows data on the average number of magazine issues read per person in the United States in 2015 and 2018, by format. During the 2018 survey, it was discovered that U.S. respondents read an average of 2.7 print magazine issues, and the same number of digital magazines.

Dataset Overview: This dataset contains survey responses collected from Undergraduate/Graduate and above level Professionals From all over India. The survey was conducted to gather information about People's Reading Habits, learning preferences, and other related factors.

Columns: The dataset consists of 8 columns representing different survey questions and responses. The columns include information such as gender, location, preferred study materials, average time spent for reading, reading preferences, education, and more.

Data Collection: The survey was shared with People and their responses were collected using a Google Form. Approximately 2k-3k studying/compled various courses, ranging from UG to Doctoral degree, participated in the survey.

Data Format: The dataset is provided in CSV format, with each row representing a survey response and each column representing a specific survey question.

Data Usage: This dataset can be used to gain insights into People's reading Material usage patterns, reading habits, and learning preferences. It can be used for exploratory data analysis, statistical analysis, and building predictive models related to Reading behavior, library services, or educational interventions.

Data Quality: The dataset has been cleaned and preprocessed to remove any identifiable personal information and ensure data privacy. However, it is always advisable to handle the data responsibly and in accordance with applicable data protection regulations.

Here's a column-wise description of the dataset:

Age Gender Location (country, city) Educational level (e.g., undergraduate, graduate) Stream(MEDIICAL,ENGINEERING,DESIGNING,etc.) Reading frequenc Average Reading Duration (minutes) Types of content (e.g., textbooks, research papers, fiction, technical articles) Reading preferences (e-books, PDFs, web articles)

View less Usability 10.00

Australian and New Zealand journal of statistics Impact Factor 2024-2025 - ResearchHelpDesk - The Australian & New Zealand Journal of Statistics is an international journal managed jointly by the Statistical Society of Australia and the New Zealand Statistical Association. Its purpose is to report significant and novel contributions in statistics, ranging across articles on statistical theory, methodology, applications and computing. The journal has a particular focus on statistical techniques that can be readily applied to real-world problems, and on application papers with an Australasian emphasis. Outstanding articles submitted to the journal may be selected as Discussion Papers, to be read at a meeting of either the Statistical Society of Australia or the New Zealand Statistical Association. The main body of the journal is divided into three sections. The Theory and Methods Section publishes papers containing original contributions to the theory and methodology of statistics, econometrics and probability, and seeks papers motivated by a real problem and which demonstrate the proposed theory or methodology in that situation. There is a strong preference for papers motivated by, and illustrated with, real data. The Applications Section publishes papers demonstrating applications of statistical techniques to problems faced by users of statistics in the sciences, government and industry. A particular focus is the application of newly developed statistical methodology to real data and the demonstration of better use of established statistical methodology in an area of application. It seeks to aid teachers of statistics by placing statistical methods in context. The Statistical Computing Section publishes papers containing new algorithms, code snippets, or software descriptions (for open source software only) which enhance the field through the application of computing. Preference is given to papers featuring publically available code and/or data, and to those motivated by statistical methods for practical problems. In addition, suitable review papers and articles of historical and general interest will be considered. The journal also publishes book reviews on a regular basis. Abstracting and Indexing Information Academic Search (EBSCO Publishing) Academic Search Alumni Edition (EBSCO Publishing) Academic Search Elite (EBSCO Publishing) Academic Search Premier (EBSCO Publishing) CompuMath Citation Index (Clarivate Analytics) Current Index to Statistics (ASA/IMS) Journal Citation Reports/Science Edition (Clarivate Analytics) Mathematical Reviews/MathSciNet/Current Mathematical Publications (AMS) RePEc: Research Papers in Economics Science Citation Index Expanded (Clarivate Analytics) SCOPUS (Elsevier) Statistical Theory & Method Abstracts (Zentralblatt MATH) ZBMATH (Zentralblatt MATH)

The statistic shows data on the share of kids who have read a Harry Potter book in the United States as of October 2016, by age group. Overall, ** percent of kids in the U.S. have read a Harry Potter book. According to the source, ** percent of kids aged 15 to 17 have read a Harry Potter book.

Dataset Overview: This dataset contains survey responses collected from students in a college located in Satara, Maharashtra, India. The survey was conducted to gather information about students' library usage, reading habits, learning preferences, and other related factors.

Columns: The dataset consists of 29 columns representing different survey questions and responses. The columns include information such as gender, faculty, location, preferred study materials, library visit frequency, average time spent in college, preferred learning language, reading preferences, COVID-19 pandemic impact, book purchasing behavior, parents' occupation and education, and more.

Data Collection: The survey was shared with students in the college library, and their responses were collected using a Google Form. Approximately 10-15k students studying in various courses, ranging from 11th grade to master's degree, participated in the survey.

Data Format: The dataset is provided in CSV format, with each row representing a student's survey response and each column representing a specific survey question.

Data Usage: This dataset can be used to gain insights into students' library usage patterns, reading habits, and learning preferences. It can be used for exploratory data analysis, statistical analysis, and building predictive models related to student behavior, library services, or educational interventions.

Data Quality: The dataset has been cleaned and preprocessed to remove any identifiable personal information and ensure data privacy. However, it is always advisable to handle the data responsibly and in accordance with applicable data protection regulations.

Here's a column-wise description of the dataset:

gender: Gender of the student. faculty: Faculty or department of the student. Enter Your Location: Location of the student. kind of books preferred for study: Preferred type of books for studying. How Frequently do you visit library: Frequency of visiting the library. For what Purposes do you visit library: Purposes for visiting the library. Average Time spent in college: Average time spent in college. What is general Purposes: General purposes of the student. Which one is your Preferred location: Preferred location. What is your preferred time?: Preferred time for activities. Preferred language for Learning: Preferred language for learning. Preferred type for reading: Preferred type of reading material. Do you enjoy the Reading: Enjoyment of reading. Which mode of learning: Preferred mode of learning. Dose Covid Pandemic Ch: Impact of the Covid pandemic on learning. How do you study before collage: Study habits before college. How do you study after Collage: Study habits after college. Do you aware about Nati: Awareness about National Digital Library. Do you Using National di: Usage of National Digital Library. Dose Covid 19 Pandemic Affected Your Reading Habits: Impact of the Covid-19 pandemic on reading habits. Do you purchase Books from store: Book purchasing behavior from physical stores. Average Expenditure on books: Average expenditure on books. Occupation Of Father: Occupation of the student's father. Parents Education: Education level of the student's parents. Select your Faculty: Select faculty or department. Enter your Location: Enter location. Preferred Language for Learning: Preferred language for learning. Do you Using National dig: Usage of National Digital Library. Occupation of Father: Occupation of the student's father.

In 2024, adults in the United States spent more time reading on weekends than weekdays, according to recent data. The average time spent reading in the U.S. amounted to *** hours (18 minutes) on weekends and holidays, while daily time spent reading on weekdays in 2024 remained belowpre-pandemic levels at just over a ******* of an hour.

Integrated Postsecondary Education Data System (IPEDS) Complete Data Files from 1980 to 2023. Includes data file, STATA data file, SPSS program, SAS program, STATA program, and dictionary. All years compressed into one .zip file due to storage limitations.Updated on 2/14/2025 to add Microsoft Access Database files.From IPEDS Complete Data File Help Page (https://nces.ed.gov/Ipeds/help/complete-data-files):Choose the file to download by reading the description in the available titles. Then, click on the link in that row corresponding to the column header of the type of file/information desired to download.To download and view the survey files in basic CSV format use the main download link in the Data File column.For files compatible with the Stata statistical software package, use the alternate download link in the Stata Data File column.To download files with the SPSS, SAS, or STATA (.do) file extension for use with statistical software packages, use the download link in the Programs column.To download the data Dictionary for the selected file, click on the corresponding link in the far right column of the screen. The data dictionary serves as a reference for using and interpreting the data within a particular survey file. This includes the names, definitions, and formatting conventions for each table, field, and data element within the file, important business rules, and information on any relationships to other IPEDS data.For statistical read programs to work properly, both the data file and the corresponding read program file must be downloaded to the same subdirectory on the computer’s hard drive. Download the data file first; then click on the corresponding link in the Programs column to download the desired read program file to the same subdirectory.When viewing downloaded survey files, categorical variables are identified using codes instead of labels. Labels for these variables are available in both the data read program files and data dictionary for each file; however, for files that automatically incorporate this information you will need to select the Custom Data Files option.

Data sets to reproduce the results in A Redemption Song for Statistical Significance at https://www.qeios.com/read/3QQSNC.5

This resource contains a Jupyter Notebook that is used to introduce hydrologic data analysis and conservation laws. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) calculate the residence time of water in land and rivers for the global hydrologic cycle; (2) quantify the relative and absolute uncertainties in components of the water balance; (3) navigate public websites and databases, extract key watershed attributes, and perform basic hydrologic data analysis for a watershed of interest; (4) assess, compare, and interpret hydrologic trends in the context of a specific watershed.

Please note that in problems 3-8, the user is asked to use an R package (i.e., dataRetrieval) and select a U.S. Geological Survey (USGS) streamflow gage to retrieve streamflow data and then apply the hydrological data analysis to the watershed of interest. We acknowledge that the material relies on USGS data that are only available within the U.S. If running for other watersheds of interest outside the U.S. or wishing to work with other datasets, the user must take some further steps and develop codes to prepare the streamflow dataset. Once a streamflow time series dataset is obtained for an international catchment of interest, the user would need to read that file into the workspace before working through subsequent analyses.

The DNA Read, Write, and Edit market is rapidly evolving, driven by the need for precision in genetic research, diagnostics, and therapeutics. As of 2023, the market has demonstrated significant growth, reflecting a burgeoning landscape valued at several billion dollars, with projections suggesting a double-digit co

The Programmable Read-Only Memory (PROM) market has witnessed significant evolution and expansion in recent years, driven by the increasing demand for reliable storage solutions across various industries, including consumer electronics, automotive, telecommunications, and industrial applications. PROM, a type of non

Female Color Morph CountsThe file reports the counts in female color morphs of damselflies (I. elegans) in 12 ponds of southern Sweden, for 12 consecutive years (2000 to 2011). The number of females of each morph (A, I, and O) is reported in three distinct tables (years as lines, populations as columns). The file is formatted such that it can be read by our statistical program.data.datfinal_scriptsThis ZIP file contains all scripts and code to reproduce the statistical analyses of our paper. Programs R and ADMB should be installed on the computer. Scripts were run on a Linux/Ubuntu operating system. Directory 'analysis': scripts to generate the figures (there could be minor differences compared to the published version). Directory 'data' contains the data (same file as already published on Dryad). Directory 'model' contains the ADMB code to run the model (should be compiled with ADMB). Directory 'suppl' contains the material necessary to run the supplementary analyses, including simulat...

The EEPROM (Electrically Erasable Programmable Read-Only Memory) chips market has been witnessing significant growth, driven by the increasing demand for non-volatile memory solutions across various industries. These chips are critical components in electronic devices, enabling the storage of data even when the powe

The Long-Read Sequencing market has emerged as a pivotal segment of genomic research, revolutionizing our ability to analyze complex genetic materials with remarkable accuracy. Unlike traditional short-read sequencing methods, long-read sequencing techniques, such as those offered by PacBio and Oxford Nanopore, enab

The statistic shows the average number of books read per year by consumers in the United States as of August 2018, broken down by income. The data shows that Americans earning more than ******* a year were more likely to have read more books than those on lower salaries, with ** percent of respondents in this category saying that they read between ** and ** books per year compared to ** percent of Americans with a salary below *******.

In 2024, ** percent of French people had read 20 books or more on print format, while ** percent read non. As a matter of comparison, only *** percent of consumers read 20 or more digital books that same year.