This article describes the new RPIJ measure of Consumer Price Inflation. RPIJ is a Retail Prices Index (RPI) based measure that will use a geometric (Jevons) formula in place of one type of arithmetic formula (Carli). It is being launched in response to the National Statistician's conclusion that the RPI does not meet international standards due to the use of the Carli formula in its calculation. The accompanying Excel file includes a back series for RPIJ from 1997 to 2012. Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: New RPIJ measure of Consumer Price Inflation

Measures of monthly UK inflation data including CPIH, CPI and RPI. These tables complement the consumer price inflation time series dataset.

There are a number of differences between the Consumer Prices Index (CPI) and Retail Prices Index (RPI), including their coverage, population base, commodity measurement and methods of construction. Combined, these differences have meant that, for most of its history, the CPI has been lower than the RPI. One of the main reasons to this difference is the method of construction at the lowest level, where different formulae are used in the CPI and RPI to combine individual prices. This difference is usually referred to as the formula effect. This article will investigate similar formula effects present in the inflation measures of other countries, and where necessary will attempt to explain why the magnitude of the formula effect experienced by other countries differs from that of the UK. Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: International Comparison

Retail Price Index in the United Kingdom decreased to 4.30 percent in October from 4.50 percent in September of 2025. This dataset provides - United Kingdom Retail Price Index YoY- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Representative items within the Consumer Prices Index including owner occupiers' housing costs, Consumer Prices Index and Retail Prices Index for the basket of goods and services.

The Regional Price Index contrasts the cost of a common basket of goods and services at a number of regional locations to the Perth metropolitan area. The RPIs were commissioned to assist with the calculation of the Western Australian State Government’s regional district allowance, and it has been used to assist in policy decision-making. Show full description

Definition of Hydrographic Unit based on the Sander: Environmental variable for calculating the RPI (River Fish Index) and relating to a territorial area that has been demarcated according to faunistic criteria. The perimeter concerns 17 municipalities, i.e. 24 680 ha.

Construction Output Price Indices (OPIs) from January 2014 to September 2025, UK. Summary

Definition of Hydrographic Unit based on the Sander: environmental variable allowing the calculation of the RPI and relating to a territorial area which has been demarcated according to faunistic criteria.

Inflation Rate in the United Arab Emirates decreased to 0.72 percent in August from 1.17 percent in July of 2025. This dataset provides the latest reported value for - United Arab Emirates Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Inflation Rate in Ireland increased to 2.90 percent in October from 2.70 percent in September of 2025. This dataset provides the latest reported value for - Ireland Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Context

A team's mean seasons statistics can be used as predictors for their performance in future games. However, these statistics gain additional meaning when placed in the context of their opponents' (and opponents' opponents') performance. This dataset provides this context for each team. Furthermore, predicting games based on post-season stats causes data leakage, which from experience can be significant in this context (15-20% loss in accuracy). Thus, this dataset provides each of these statistics prior to each game of the regular season, preventing any source of data leakage.

Content

All data is derived from the March Madness competition data. Each original column was renamed to "A" and "B" instead of "W" and "L," and the mirrored to represent both orderings of opponents. Each team's mean stats are computed (both their stats, and the mean "allowed" or "forced" statistics by their opponents). To compute the mean opponents' stats, we analyze the games played by each opponent (excluding games played against the team in question), and compute the mean statistics for those games. We then compute the mean of these mean statistics, weighted by the number of times the team in question played each opponent. The opponents' opponent's stats are computed as a weighted average of the opponents' average. This results in statistics similar to those used to compute strength of schedule or RPI, just that they go beyond win percentages (See: https://en.wikipedia.org/wiki/Rating_percentage_index)

The per game statistics are computed by pretending we don't have any of the data on or after the day in question.

Next Steps

Currently, the data isn't computed particularly efficiently. Computing the per game averages for every day of the season is necessary to compute fully accurate opponents' opponents' average, but takes about 90 minutes to obtain. It is probably possible to parallelize this, and the per-game averages involve a lot of repeated computation (basically computing the final averages over and over again for each day). Speeding this up will make it more convenient to make changes to the dataset.

I would like to transform these statistics to be per-possession, add shooting percentages, pace, and number of games played (to give an idea of the amount uncertainty that exists in the per-game averages). Some of these can be approximated with the given data (but the results won't be exact), while others will need to be computed from scratch.

Data relating to the price of houses sold in the Glasgow Area from the years 1991 - 2013.Some elements of the dataset are derived from information produced by Registers of ScotlandCLASS Administrative Classification onlySTNO Street NumberSTnu Street NumberFLATPOSN Flat PositionSTNAME Street NamePOSTCODE Post CodeMONTH OF SALE Month of SaleYEAR OF SALE (CALENDAR) YEAR OF SALE (CALENDAR)YEAR OF SALE (BUSINESS) YEAR OF SALE (BUSINESS)MONTH AND YEAR MONTH AND YEARQUARTER_(CALENDAR) QUARTER_(CALENDAR)ACTUAL PRICE AT POINT OF SALE Actual Price RPI Retail Price Index - Published every month and available for the last 20 yearsDEFLATOR Figure used to to determine change in house prices over time - calculated fromthe Retail Price Index and other dataPRICE CONSTANT AT July 2013 Actual Price multiplied by the Deflator. This is the price if RPI is applied to original sale price - How much would the property be valued at now. ORIGINOFBUY Council area or Country where the buyer comes fromOMIT OR USE Oroginal data also included retail and commercial data. - Not reproduced hereNEWBUILD OR RESALE Is it a newbuild house or a resaleLHF Local Housing Forum Area