The data are qualitative data consisting of notes recorded during meetings, workshops, and other interactions with case study participants. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: The data cannot be accessed by anyone outside of the research team because of the potential to identify human participants. Format: The data are qualitative data contained in Microsoft Word documents. This dataset is associated with the following publication: Eisenhauer, E., K. Maxwell, B. Kiessling, S. Henson, M. Matsler, R. Nee, M. Shacklette, M. Fry, and S. Julius. Inclusive engagement for equitable resilience: community case study insights. Environmental Research Communications. IOP Publishing, BRISTOL, UK, 6: 125012, (2024).

This data collection provides comparable measures of state appellate and trial court caseloads by type of case for the 50 states, the District of Columbia, and Puerto Rico. Court caseloads are tabulated according to generic reporting categories developed by the Court Statistics Project Committee of the Conference of State Court Administrators. These categories describe differences in the unit of count and the point of count when compiling each court's caseload. Major areas of investigation include (1) case filings in state appellate and trial courts, (2) case processing and dispositions in state appellate and trial courts, and (3) appellate opinions. Within each of these areas of state government investigation, cases are separated by main case type, including civil cases, capital punishment cases, other criminal cases, juvenile cases, and administrative agency appeals.

Provide the product inspection bureau with case statistics for item inquiries.

Pen-and-paper homework and project-based learning are both commonly used instructional methods in introductory statistics courses. However, there have been few studies comparing these two methods exclusively. In this case study, each was used in two different sections of the same introductory statistics course at a regional state university. Students’ statistical literacy was measured by exam scores across the course, including the final. The comparison of the two instructional methods includes using descriptive statistics and two-sample t-tests, as well authors’ reflections on the instructional methods. Results indicated that there is no statistically discernible difference between the two instructional methods in the introductory statistics course.

The data contain records of defendants in criminal cases filed in United States District Court during fiscal year 2007. The data were constructed from the Administrative Office of the United States District Courts' (AOUSC) criminal file. Defendants in criminal cases may be either individuals or corporations. There is one record for each defendant in each case filed. Included in the records are data from court proceedings and offense codes for up to five offenses charged at the time the case was filed. (The most serious charge at termination may differ from the most serious charge at case filing, due to plea bargaining or action of the judge or jury.) In a case with multiple charges against the defendant, a "most serious" offense charge is determined by a hierarchy of offenses based on statutory maximum penalties associated with the charges. The data file contains variables from the original AOUSC files as well as additional analysis variables, or "SAF variables," that denote subsets of the data. These SAF variables are related to statistics reported in the Compendium of Federal Justice Statistics, Tables 4.1-4.5 and 5.1-5.6. Variables containing information (e.g., name, Social Security number) were replaced with blanks, and the day portions of date fields were also sanitized in order to protect the identities of individuals. These data are part of a series designed by the Urban Institute (Washington, DC) and the Bureau of Justice Statistics. Data and documentation were prepared by the Urban Institute.

The data contain records of defendants in criminal cases terminated in United States District Court during fiscal year 2002. The data were constructed from the Administrative Office of the United States District Courts' (AOUSC) criminal file. Defendants in criminal cases may be either individuals or corporations. There is one record for each defendant in each case filed. Included in the records are data from court proceedings and offense codes for up to five offenses charged at the time the case was filed. (The most serious charge at termination may differ from the most serious charge at case filing, due to plea bargaining or action of the judge or jury.) In a case with multiple charges against the defendant, a "most serious" offense charge is determined by a hierarchy of offenses based on statutory maximum penalties associated with the charges. The data file contains variables from the original AOUSC files as well as additional analysis variables, or "SAF" variables, that denote subsets of the data. These SAF variables are related to statistics reported in the Compendium of Federal Justice Statistics, Tables 4.1-4.5 and 5.1-5.6. Variables containing identifying information (e.g., name, Social Security number) were replaced with blanks, and the day portions of date fields were also sanitized in order to protect the identities of individuals. These data are part of a series designed by the Urban Institute (Washington, DC) and the Bureau of Justice Statistics. Data and documentation were prepared by the Urban Institute.

BUSINESS PROBLEM-1 BACKGROUND: The Lending Club is a peer-to-peer lending site where members make loans to each other. The site makes anonymized data on loans and borrowers publicly available. BUSINESS PROBLEM: Using lending club loans data, the team would like to test below hypothesis on how different factors effecing each other (Hint: You may leverage hypothesis testing using statistical tests) a. Intrest rate is varied for different loan amounts (Less intrest charged for high loan amounts) b. Loan length is directly effecting intrest rate. c. Inrest rate varies for different purpose of loans d. There is relationship between FICO scores and Home Ownership. It means that, People with owning home will have high FICO scores. DATA AVAILABLE:  LoansData.csv The data have the following variables (with data type and explanation of meaning)  Amount.Requested - numeric. The amount (in dollars) requested in the loan application.  Amount.Funded.By.Investors - numeric. The amount (in dollars) loaned to the individual.  Interest.rate – character. The lending interest rate charged to the borrower.  Loan.length - character. The length of time (in months) of the loan.  Loan.Purpose – categorical variable. The purpose of the loan as stated by the applicant.  Debt.to.Income.Ratio – character. The % of consumer’s gross income going toward paying debts.  State - character. The abbreviation for the U.S. state of residence of the loan applicant.  Home.ownership - character. Indicates whether the applicant owns, rents, or has a mortgage.  Monthly.income - categorical. The monthly income of the applicant (in dollars).  FICO.range – categorical (expressed as a string label e.g. “650-655”). A range indicating the applicants FICO score.  Open.CREDIT.Lines - numeric. The number of open lines of credit at the time of application.  Revolving.CREDIT.Balance - numeric. The total amount outstanding all lines of credit.  Inquiries.in.the.Last.6.Months - numeric. Number of credit inquiries in the previous 6 months.  Employment.Length - character. Length of time employed at current job.

BUSINESS PROBLEM - 2 BACKGROUND: When an order is placed by a customer of a small manufacturing company, a price quote must be developed for that order. Because each order is unique, quotes must be established on an order-by-order basis by a pricing expert. The price quote process is laborintensive, as prices depend on many factors such as the part number, customer, geographic location, market, and order volume. The sales department manager is concerned that the pricing process is too complex, and that there might be too much variability in the quoted prices. An improvement team is tasked with studying and improving the pricing process. After interviewing experts to develop a better understanding of the current process, the team designed a study to determine if there is variability between pricing experts. That is, do different pricing experts provide different price quotes? Two randomly selected pricing experts, Mary and Barry, were asked to independently provide prices for twelve randomly selected orders. Each expert provided one price for each of the twelve orders. BUSINESS PROBLEM: We would like to assess if there is any difference in the average price quotes provided by Mary and Barry. DATA AVAILABLE:  Price_Quotes.csv The data set contains the order number, 1 through 12, and the price quotes by Mary and Barry for each order. Each row in the data set is the same order. Thus, Mary and Barry produced quotes for the same orders. BUSINESS PROBLEM-3: BACKGROUND: The New Life Residential Treatment Facility is a NGO that treatsteenagers who have shown signs of mental illness. It provides housing and supervision of teenagers who are making the transition from psychiatric hospitals back into the community. Because many of the teenagers were severely abused as children and have been involved with the juvenile justice system, behavioral problems are common at New Life. Employee pay is low and staff turnover (attrition) is high. A reengineering program wasinstituted at New Life with the goals of lowering behavioral problems of the kids and decreasing employee turnover rates. As a part of this effort, the following changes were made:  Employee shifts were shortened from 10 hours to 8 hours each day.  Employees were motivated to become more involved in patient treatments. This included encouraging staff to run varioustherapeutic treatment sessions and allowing staff to have more say in program changes.  The activities budget wasincreased.  A facility-wide performance evaluation system was putinto place that rewarded staff participation andinnovation.  Management and staff instituted a program designed to raise expectations about appropriate behavior from the kids. Thisincluded strict compliance with reporting of behavioral violations, insistence o...

This database supports Earnings Corrections.

These quarterly reports show the number of receipts, dispositions and pending New Court Cases (NCCs) during the defined period. The data shown is by month with quarterly and fiscal year (FY) summaries through the most recently completed quarter.

The Montana COVID-19 Case and Test Data web service hosts COVID-19 statistics for the state of Montana by county. The data was derived from local health officials at the county level who reported cases to the Montana Department of Health and Human Services. DPHHS tabulated case data and then gave the data to the Montana State Library to publish through this web service. The daily updates were managed by the Disaster and Emergency Service State Emergency Coordination Center. The feature service is comprised of Montana's county geography with attributes that summarize Total COVID-19 cases by age (10-year groups), by sex (M/F/U), new cases, total deaths, hospitalization count, total recovered and the number of total active cases. The two tables store various stats that include the total number of tests completed, and the number of new tests completed for individual test dates; and individual case data which includes age group, sex, county or residence and recovery status. Montana public health agencies and the Governor's Coronavirus task Force actively worked to limit the spread of novel coronavirus in Montana. The Montana State Library aided this effort by geo-enabling public health information and emergency response data to help decision-makers, State Emergency Coordination Center and the Governor's Coronavirus Task Force understand the spread of the disease. This data and feature service is no longer maintained and the final update to this data was made on 05/05/2023.

The agency's annual archive case statistics.......

In the way of my journey to earn the google data analytics certificate I will practice real world example by following the steps of the data analysis process: ask, prepare, process, analyze, share, and act. Picking the Bellabeat example.

The first case of the coronavirus (COVID-19) in Denmark was confirmed on February 27, 2020. By January 16, 2023, the number of confirmed cases in the country reached a total of 3,397,071. The number of daily cases in the country reached a new peak in winter 2021/22.

The worldwide number of confirmed cases of coronavirus was over 668 million as of January 9, 2023. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

The single largest use case in terms of number of Internet of Things (IoT) connected devices is consumer internet and media devices, accounting for a third of all devices worldwide in 2034. The other two largest use cases are smart grid (e.g. smart meters) and inventory management & monitoring.

The purpose of this publication, on episode based acute hospital inpatient and day case activity, is to complement data contained in the Northern Ireland Hospital Statistics: Inpatient and Day Case Activity Statistics. The Episode Based Acute Hospital Inpatient and Day Case Activity Data shows detailed analysis at diagnostic and procedure level, within the Acute Programme of Care (PoC 1).

Source agency: Health, Social Service and Public Safety (Northern Ireland)

Designation: Official Statistics not designated as National Statistics

Language: English

Alternative title: Episode Based Acute Hospital Inpatient and Day Case Activity

The first two cases of the new coronavirus (COVID-19) in Italy were recorded between the end of January and the beginning of February 2020. Since then, the total number of cases in Italy increased steadily and the country faced five harsh waves of contagion. The total number of cases reached 26,964,654 as of January 8, 2025. The region mostly hit by the virus in the country was Lombardy, counting almost than 4.4 million cases. The virus originated in Wuhan, a Chinese city populated by millions and located in the province of Hubei. More statistics and facts about the virus in Italy are available here.For a global overview, visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

Statistics on the nature of the cases filed by the people against the first-level government agencies

Statistics of refusal cases with breakdown by specific exemptions and by the handling public agencies

This dataset provides the average number of days to first appearance by court for serious and pending cases.

Youth courts, type of case by offence, age and sex of accused and median elapsed time in days, Canada, provinces, territories, ten jurisdictions and eight jurisdictions, five years of data.