The index for household furnishings and operations increased 0. Also rising in September were the indexes for airline fares, for tobacco, and for education.

The index for used cars and trucks declined in September, falling 1. The apparel index fell 0.

Also declining in September were the indexes for new vehicles, for communication, for alcoholic beverages, and for personal care. The recreation index was unchanged in September after rising in August. Indexes with larger increases included shelter and medical care both 3. Indexes with smaller increases over the past 12 months included recreation 1.

Indexes with declines over the past year included communication For the month, the index increased 0. Please note that the indexes for the past 10 to 12 months are subject to revision.

### Ratings and reviews

The CPI reflects spending patterns for each of two population groups: all urban consumers and urban wage earners and clerical workers. The all urban consumer group represents about 93 percent of the total U. It is based on the expenditures of almost all residents of urban or metropolitan areas, including professionals, the self-employed, the poor, the unemployed, and retired people, as well as urban wage earners and clerical workers. Not included in the CPI are the spending patterns of people living in rural nonmetropolitan areas, farming families, people in the Armed Forces, and those in institutions, such as prisons and mental hospitals.

The Consumer Price Index for Urban Wage Earners and Clerical Workers CPI-W is based on the expenditures of households included in the CPI-U definition that meet two requirements: more than one-half of the household's income must come from clerical or wage occupations, and at least one of the household's earners must have been employed for at least 37 weeks during the previous 12 months. Prices are collected each month in 75 urban areas across the country from about 6, housing units and approximately 22, retail establishments department stores, supermarkets, hospitals, filling stations, and other types of stores and service establishments.

All taxes directly associated with the purchase and use of items are included in the index.

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Prices of fuels and a few other items are obtained every month in all 75 locations. Prices of most other commodities and services are collected every month in the three largest geographic areas and every other month in other areas. In calculating the index, price changes for the various items in each location are aggregated using weights, which represent their importance in the spending of the appropriate population group. Local data are then combined to obtain a U. For the CPI-U and CPI-W, separate indexes are also published by size of city, by region of the country, for cross-classifications of regions and population-size classes, and for 23 selected local areas.

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Area indexes do not measure differences in the level of prices among cities; they only measure the average change in prices for each area since the base period. The index measures price change from a designed reference date. An increase of 7 percent from the reference base, for example, is shown as Sampling Error in the CPI The CPI is a statistical estimate that is subject to sampling error because it is based upon a sample of retail prices and not the complete universe of all prices.

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BLS calculates and publishes estimates of the 1-month, 2-month, 6-month, and month percent change standard errors annually for the CPI-U. These standard error estimates can be used to construct confidence intervals for hypothesis testing. For example, the estimated standard error of the 1-month percent change is 0. This means that if we repeatedly sample from the universe of all retail prices using the same methodology, and estimate a percentage change for each sample, then 95 percent of these estimates will be within 0.

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For example, for a 1-month change of 0. Calculating Index Changes Movements of the indexes from 1 month to another are usually expressed as percent changes rather than changes in index points, because index point changes are affected by the level of the index in relation to its base period, while percent changes are not. These factors are updated each February, and the new factors are used to revise the previous 5 years of seasonally adjusted data. The factors are available at www. For more information on data revision scheduling, please see the Factsheet on Seasonal Adjustment at www.

This allows data users to focus on changes that are not typical for the time of year. The unadjusted data are of primary interest to consumers concerned about the prices they actually pay.

### Consumer Price Index

Unadjusted data are also used extensively for escalation purposes. Many collective bargaining contract agreements and pension plans, for example, tie compensation changes to the Consumer Price Index before adjustment for seasonal variation. BLS advises against the use of seasonally adjusted data in escalation agreements because seasonally adjusted series are revised annually. Sometimes extreme values or sharp movements can distort the underlying seasonal pattern of price change.

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Intervention analysis seasonal adjustment is a process by which the distortions caused by such unusual events are estimated and removed from the data prior to calculation of seasonal factors. The resulting seasonal factors, which more accurately represent the seasonal pattern, are then applied to the unadjusted data. For example, this procedure was used for the motor fuel series to offset the effects of the return to normal pricing after the worldwide economic downturn in Retaining this outlier data during seasonal factor calculation would distort the computation of the seasonal portion of the time series data for motor fuel, so it was estimated and removed from the data prior to seasonal adjustment.

These seasonal factors represent a clearer picture of the seasonal pattern in the data. The last step is for motor fuel seasonal factors to be applied to the unadjusted data. For the seasonal factors introduced for January , BLS adjusted 51 series using intervention analysis seasonal adjustment, including selected food and beverage items, motor fuels, electricity, and vehicles. Every year, economists in the CPI calculate new seasonal factors for seasonally adjusted series and apply them to the last 5 years of data. Seasonally adjusted indexes beyond the last 5 years of data are considered to be final and not subject to revision.

For January , revised seasonal factors and seasonally adjusted indexes for to were calculated and published. Series which are indirectly seasonally adjusted by summing seasonally adjusted component series have seasonal factors which are derived and are therefore not available in advance.

Determining Seasonal Status Each year the seasonal status of every series is reevaluated based upon certain statistical criteria. The average is simply the sum of the numbers in a given problem, divided by the number of numbers added together. For example, if four number are added together their sum is divided by four to find the average or arithmetic mean.

Average or arithmetic mean is sometimes confused with two other concepts: mode and median. The mode is the most frequent value in a set of numbers, while the median is the number in the middle of the range of a given set. It's important to know how to calculate the mean or average of a set of numbers. Among other things, this will allow you to calculate your grade point average. However, you'll need to calculate the mean for several other situations, too. The concept of an average allows statisticians, demographers, economists, biologists, and other researchers to better understand the most common situations.

For example, by determining the average income of an American family and comparing it to the average cost of a home, it's possible to better understand the magnitude of economic challenges facing most American families.

Similarly, by looking at the average temperature in a particular area at a particular time of year, it's possible to predict the probable weather and make a wide range of decisions appropriately. While averages can be very useful tools, they can also be misleading for a variety of reasons.

In particular, averages can obscure the information contained in data sets. Here are a few examples of how averages can be misleading:. In general, you calculate the mean or average of a set of numbers by adding them all up and dividing by how many numbers you have. This can be defined as follows:. To do this, add up the numbers and divide by how many numbers you have 5 of them, in this case. How many numbers are there?