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From Wikipedia, the free encyclopedia

Nominal level is the operating level at which an electronic signal processing device is designed to operate. The electronic circuits that make up such equipment are limited in the maximum signal they can handle and the low-level internally generated electronic noise they add to the signal. The difference between the internal noise and the maximum level is the device's dynamic range. The nominal level is the level that these devices were designed to operate at, for best dynamic range and adequate headroom. When a signal is chained with improper gain staging through many devices, clipping may occur or the system may operate with reduced dynamic range.

In audio, a related measurement, signal-to-noise ratio, is usually defined as the difference between the nominal level and the noise floor, leaving the headroom as the difference between nominal and maximum output.[1][2] It is important to realize that the measured level is a time average, meaning that the peaks of audio signals regularly exceed the measured average level. The headroom measurement defines how far the peak levels can stray from the nominal measured level before clipping. The difference between the peaks and the average for a given signal is the crest factor.

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  • Types of Data: Nominal, Ordinal, Interval/Ratio - Statistics Help
  • Nominal, ordinal, interval and ratio data: How to Remember the differences
  • Introduction to Levels of Measurement: Nominal, Ordinal, Interval, Ratio

Transcription

Types of data: Nominal Ordinal Interval/Ratio Data is central to statistical analysis When we wish to find out more about a phenomenon or process we collect data. Usually we collect several measures on each person or thing of interest. Each thing we collect data about is called an observation. If we are interested in how people respond, then each observation will be a person. OR an observation could be a business or a product, or a period in time, such as a week. Variables record the measurements we are interested in. Age, sex and chocolate preference can all be stored as variables. For each observation we record a score or value for each of the variables. When we store this data in a spreadsheet or database, each row corresponds to a single observation and each column is a variable. Level of measurement The level of measurement used for a variable determines which summary statistics, graphs and analysis are possible and sensible. The Nominal level is the most basic level of measurement. Nominal is also known as categorical or qualitative. Examples of nominal variables are sex, preferred type of chocolate and colour. These are descriptions or labels with no sense of order. Nominal values can be stored as a word or text or given a numerical code. However, the numbers do not imply order. To summarise nominal data we use a frequency or percentage. You can not calculate a mean or average value for nominal data. The next level of measurement is ordinal. Examples of ordinal variables are rank, satisfaction, and fanciness! Ordinal variables have a meaningful order, but the intervals between the values in the scale may not be equal. For example the gap between first and second runners in a race may be small, whereas there is a bigger gap between second and third. Similarly there may be a big difference between satisfied and unsatisfied, but a smaller difference between unsatisfied and very unsatisfied. Like Nominal data, ordinal data can be given as frequencies. Some people state that you should never calculate a mean or average for ordinal data. However it is quite common practice, particularly in research regarding people's behaviour to find mean values for ordinal data. You should be careful if you do this to think about what it means and if it is justifiable. The most precise level of measurement is interval/ratio. This label includes things that can be measured rather than classified or ordered, such as number of customers weight, age and size. Interval ratio data is also known as scale, quantitative or parametric. Interval/Ratio data can be discrete, with whole numbers or continuous, with fractional numbers. Interval/Ratio data is very mathematically versatile. The most common summary measures are the mean, the median and the standard deviation. The way data should be represented in a graph or chart depends on the level of measurement. Nominal data can be displayed as a pie chart, column or bar chart or stacked column or bar chart. In most cases the best choice for a single set of nominal data is a column chart. Ordinal data must not be represented as a pie chart, but is best shown as a column or bar chart. Interval/ratio data is best represented as a bar chart or a histogram. For these the data is grouped. Box plots illustrate the summary statistics for a variable in a neat way. Data which occurs over time is best displayed as a line chart. Here is an example using different types of data. Helen sells choconutties. Helen is interested in developing a new product to add to her line of choconutties. She develops a questionnaire and asks a random sample of 50 of her customers to fill it out. She asks them their age and sex, how much they spend on groceries each week, how many chocolate bars they buy in a week, and which they like best out of dark, milk and white chocolate. She asks them how satisfied they are with choconutties: very satisfied, satisfied, not satisfied, very unsatisfied. And she asks them how likely they are to buy a whole box of 10 packets of choconutties. Helen enters the data in a spreadsheet. Each row has responses from one customer. Each column contains the measurements or scores for one variable. The type of chocolate preferred is nominal data. This can be shown in a pie chart or bar chart. We can summarise by saying that 46% of customers prefer Dark chocolate, 40% prefer milk chocolate, and 14% prefer white chocolate. The measures of satisfaction and likelihood are ordinal level data. These should not be shown in a pie chart. The values should be put in a logical order in a column chart. We could say that 32% are very satisfied with choconutties and 72% of people are satisfied or very satisfied. and 72% of people are satisfied or very satisfied. The average satisfaction score comes to 2.06, which could be interpreted as satisfied. However it is debatable whether it is sensible to calculate a mean satisfaction score. Age, amount spent on groceries and number of chocolate bars are all interval/ratio data. These can be displayed on bar charts or histograms. We can say that for the customers in the sample, the mean age is 38 years, the mean amount spent on groceries is $192, and the mean number of chocolate bars bought per week is 3.3. These are all meaningful summary statistics. The type of analysis that is sensible for a given dataset depends on the level of measurement. You can find out more about this in the video, "Choosing the test".

Standards

VU meters are designed to represent the perceived loudness of a passage of music, or other audio content, measuring in volume units. Devices are designed so that the best signal quality is obtained when the meter rarely goes above nominal. The markings are often in dB instead of "VU", and the reference level should be defined in the device's manual. In most professional recording and sound reinforcement equipment, the nominal level is 0 VU = +4 dBu. In semi-professional and domestic equipment, the nominal level is usually −10 dBV.[3] This difference is due to the cost required to create larger power supplies and output higher levels.[4]

In broadcasting equipment, this is termed the Maximum Permitted Level, which is defined by European Broadcasting Union standards. These devices use peak programme meters instead of VU meters, which gives the reading a different meaning.

"Mic level" is sometimes defined as −60 dBV, though levels from microphones vary widely.[5]

In video systems, nominal levels are 1 VP-P for synched systems, such as baseband composite video, and 0.7 VP-P for systems without sync.[5][6][7] Note that these levels are measured peak-to-peak, while audio levels are time averages.

See also

References

  1. ^ AES Pro Audio Reference definition of signal-to-noise ratio
  2. ^ AES Pro Audio Reference definition of headroom
  3. ^ Robjohns, Hugh (October 2003). "Sound Advice > Q. Are all Decibels equal?". www.soundonsound.com. Retrieved 12 February 2020.
  4. ^ Winer, Ethan (2013). The Audio Expert: Everything You Need to Know About Audio. Focal Press. p. 107. ISBN 978-0-240-82100-9.
  5. ^ a b Extron Electronics Glossary of Terms definition of nominal level
  6. ^ TRV900 Technical Measurements - "The nominal baseband composite video signal has an amplitude of 1.0 Vpp."
  7. ^ Video Signal Standards and Conversion Page - "The Y signal has a nominal level of 1Vpp and C signal a level of around 0.5V.", "The nominal signal level is 1Vpp on a 75 ohm terminated line."

External links

This page was last edited on 9 January 2022, at 18:24
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