According to EqualOceans, the largest funds raised by online education companies in China in the first half of 2020 ranged between 25 million and one billion yuan. Several online education companies, such as Yuanfudao, Zuoyebang, Wanxue or Yunxuetang, used the boom in online education due to the COVID-19 pandemic to raise new funds.

Some studies have shown that body mass index (BMI), weight (kg)/height (m)2, has a negative (or no) effect on wage. But BMI representing obesity is a tightly specified function of weight and height, and there is a room for weight given height (i.e. obesity given height) to better explain wage when the tight specification gets relaxed. In this paper, we address the question of weight effect on wage given height, employing two-wave panel data for white females and adopting a semi-linear model consisting of a nonparametric function of weight and height and a linear function of the other regressors. We find that there is no weight effect on wage up to the average weight, beyond which a large negative effect kicks in. Linear BMI models give the incorrect impression of the presence of a wage gain by becoming slimmer than the average and of a wage loss that is less than what it actually is when going above the average.

This dataset tracks annual distribution of students across grade levels in Increase Miller Elementary School

This dataset tracks annual reading and language arts proficiency from 2011 to 2022 for Increase Miller Elementary School vs. New York and Katonah-Lewisboro Union Free School District

From June 19 to October 31, 2013, the Together We Raise Tomorrow unified engagement took place on the Children’s Charter, Poverty Reduction Strategy and Early Childhood Development. Over 6,400 Albertans provided feedback through 302 community conversations, survey’s and on-line participation. In May 2014, the draft Children’s Charter was posted to childcharter.alberta.ca for Albertans to comment. In addition, discussions with members from the following Aboriginal partners took place across the province. Upon conclusion of the engagement, the notes from all conversations were reviewed and grouped into common themes: importance of language and identity, role of families and communities, acknowledgement of residential school experience; respect for the land and indigenous worldviews; educational inequities and issues of access.

In 2020, scarves registered a 215 percent increase in online orders compared to 2019. Gloves also recorded an increase of 151 percent.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Mitchells & Butlers (MAB): Raise a Glass to Expansion Plans?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This dataset tracks annual reduced-price lunch eligibility from 2010 to 2020 for Increase Miller Elementary School vs. New York and Katonah-Lewisboro Union Free School District

Data is a collation of local and global programs that address tertiary education attainment. It is in the form of a Microsoft Office Excel spreadsheet and is purely text.

Abstract

The objective of the project was to increase the consumption of fruits and vegetables in school children in the city of Guatemala, by implementing a system of fruit and vegetable provision sustained in the school environment, in order to establish healthy eating habits at an early age.

Geographic coverage

Sub-national coverage, only urban areas.

Analysis unit

Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The schools were selected by convenience. Then, 4 class sections were selected by simple random sampling in each school. The scholars authorized by their parents to participate in the study and voluntarily wanting to, were selected. From there, simple random sampling was applied to have the study sample.

Mode of data collection

Face-to-face paper [f2f]

Effect of KSX participation on MOOC completion rate.

BackgroundA superior family background often provides children with more educational resources and a better learning environment, while poor or educationally deficient families may put children at a disadvantage on the starting line of education. This difference is not only related to the fate of individuals, but also to the fairness and justice of the whole society. Therefore, this study explores the influence mechanism of family background on children’s education level and the moderating effect of socioeconomic status.MethodsBased on the data of China General Social Survey in 2021, a total of 4592 samples were selected. Principal component analysis model, hierarchical regression model and hierarchical linear model were used to verify the relationship between family background, socioeconomic status and children’s education level.Results(1) Family background has a significant positive impact on children’s education level (β=1.266; p 

The City of Chicago auctions surplus equipment, vehicles, furniture, office equipment, and other goods and materials online in order to raise funds. The below graph provides weekly updates on progress toward the fundraising target of $4.6 million.

Th e education and application of skills in it is an essential prerequisite for the growth and development of each national economy in the future. Investing in education and skills development are essential incentive for raising the growth and competitiveness of each country and its participation in the labor market. Th e skills are part of the educational capacity which have the aim to increase the productivity of labor and knowledge of production processes and technologies, to raise long-term growth and innovation, they transform the production to new values, stimulate competition for application of higher level skills, or with one word it shape the future of the labor market to the real needs of the working environment. Th e main task of this paper is to answer the question whether with the current method of education we can be a country of information society where the processes and programs are the foundation of the industrial model of education and the demand for individuality, creations and innovations for application awareness, humanity, the requirement of a model of education with more educational programs represent the future serious indicators and parameters for better quality economic growth and development. Key Words: education, skills, capacity, information, innovations. JEL Classifi cation: A20, A21, A22, A23, A29, I20, I21, I22, I23, I25, I26.

Since its launch in 2017, the furniture and lifestyle marketplace Faire has raised 1.8 billion U.S. dollars. Headquartered in San Francisco, the startup serves as online wholesale platform for more than 85,000 retail brands worldwide.

Japan Shunto: AWM: Wage Increase incl Regular Raise Rate (WIRR) data was reported at 5.370 % in 2025. This records an increase from the previous number of 5.100 % for 2024. Japan Shunto: AWM: Wage Increase incl Regular Raise Rate (WIRR) data is updated yearly, averaging 2.070 % from Mar 1989 (Median) to 2025, with 37 observations. The data reached an all-time high of 5.950 % in 1990 and a record low of 1.630 % in 2003. Japan Shunto: AWM: Wage Increase incl Regular Raise Rate (WIRR) data remains active status in CEIC and is reported by Japanese Trade Union Confederation. The data is categorized under Global Database’s Japan – Table JP.G166: Shunto: Spring Wage Negotiation.

India New Capital Raised: Public Company: Year to Date: Issues: Debentures data was reported at 11.000 Unit in Sep 2018. This records an increase from the previous number of 7.000 Unit for Aug 2018. India New Capital Raised: Public Company: Year to Date: Issues: Debentures data is updated monthly, averaging 5.000 Unit from Apr 1990 (Median) to Sep 2018, with 342 observations. The data reached an all-time high of 171.000 Unit in Mar 1993 and a record low of 0.000 Unit in Jun 2012. India New Capital Raised: Public Company: Year to Date: Issues: Debentures data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under Global Database’s India – Table IN.ZA037: New Capital Raised: Non Government Public Limited Companies.

State Government: Market Borrowings: Punjab: Net Amount Raised data was reported at 319,261.670 INR mn in 2025. This records an increase from the previous number of 295,174.390 INR mn for 2024. State Government: Market Borrowings: Punjab: Net Amount Raised data is updated yearly, averaging 184,700.000 INR mn from Mar 2017 (Median) to 2025, with 9 observations. The data reached an all-time high of 336,599.160 INR mn in 2023 and a record low of 121,435.240 INR mn in 2017. State Government: Market Borrowings: Punjab: Net Amount Raised data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Government and Public Finance – Table IN.FC009: State Government: Market Borrowings: Annual.