Measures are a central concept in probability theory and integration theory. They are also widely used in physics, especially in the context of spectral measures and projection-valued measures.
To be of a certain measure is to be comparable in some way to something else: She was of good measure.
Definition
Measures are used to collect raw data and reduce uncertainty about the value of a quantity. They are used to track metrics and determine business performance. There are several different types of measures, but each one is designed for a specific purpose. For example, some measurements are used to calculate averages while others provide more detailed information about individual variables.
Each measurement process begins with an initial guess as to the value of a variable and then uses methods and instruments to reduce uncertainty. This process is often iterative and can be time consuming.
A measure space is a set of sets such that every measurable set contains a countable sum of finite measures. Alternatively, a measure space is Lebesgue measurable if it has a closed interval [ k, k + 1 ]
Purpose
The purpose of measurements is to translate a physical quantity or phenomenon into a set of representative numbers. It has many applications, including in education, science and engineering, construction, industry, and almost all daily activities. It is an important part of students’ mathematics learning and is central to workplace occupations such as opticians and lab technicians.
A key challenge for measurement systems is to ensure that the results are valid and reliable, independent of the context in which they were produced. This requires clear strategic thinking at the design phase of the measurement system and strong involvement of people in implementing it. It also requires that the measures are made under similar conditions so that they can be compared on an equal footing. These requirements are described by information-theoretic accounts of measurement.
Accuracy
The nearness of a measurement to its true value or accepted standard. Compare precision.
For example, if you measure the mass of an object and get values of 17.4, 17.1, 18.5, and 19.6, your scale is precise but not very accurate. A more accurate scale would give you values of 10.0, 10.5, and 11.5.
Accuracy is not the same as precision, though they are related. Accuracy measures how close a single measurement is to its accepted value, while precision reflects how closely the results of a series of measurements agree with each other. Think of it like aiming for the center of the bullseye in a dart game. An accurate throw hits the bullseye, while a precise throw will hit somewhere else on the board.
Variability
Variability is a measure of how spread out or dispersed the data values are in a dataset. It also measures how wide a data distribution is when graphed. The lower the variability, the closer together the data points are.
A higher variability means that there is more chance of observations outside the normal range (either very large or small). This can be a problem for classifiers since it can cause them to overfit and become biased towards the noise in the training set.
The most common measure of variability is the standard deviation. It is easy to calculate and uses the original units of the data, so it is a convenient measure of variation. However, it can be sensitive to outliers and is therefore not suitable for skewed distributions. For these cases, the interquartile range (IQR) is a better measure of variability.
Time-series
Time series measurements are data points collected at regular intervals over a period of time. These data points are often used to predict what will happen in the future. Time series analysis includes identifying trends, seasonality and autocorrelation in data.
Time-series data can be divided into stock and flow measurements. Stock series measures attributes at specific times and are like static snapshots of the information as it existed at that point. Flow series measures activity over the course of a time period, such as a survey of retail sales over several months.
Time-series analysis is central to statistics, signal processing, pattern recognition, econometrics, quantitative finance, meteorology, seismology and control engineering. It is also useful in a variety of domains for forecasting, including the economy, weather and healthcare.