
Aineprogramm
... A typical data sample is distributed over a range of values, with some values occurring more frequently than others. Some of the variability may be the result of measurement error or sampling effects. For large random samples, however, the distribution of the data typically reflects the variability ...
... A typical data sample is distributed over a range of values, with some values occurring more frequently than others. Some of the variability may be the result of measurement error or sampling effects. For large random samples, however, the distribution of the data typically reflects the variability ...
ap stats review – organizing data
... different types of cereal from the shelves of a large grocery store. The student noticed that the side panels of some of the cereal boxes showed sugar content based on one-cup servings, while others showed sugar content based on threequarter-cup servings. Many of the cereal boxes with side panels th ...
... different types of cereal from the shelves of a large grocery store. The student noticed that the side panels of some of the cereal boxes showed sugar content based on one-cup servings, while others showed sugar content based on threequarter-cup servings. Many of the cereal boxes with side panels th ...
IQR
... Because the median is relatively unaffected by the presence of extreme values, it is said to be a resistance statistic. For this reason it is frequently used if the data is clearly skewed or is likely to contain and outlier. However, if a distribution is symmetric, both the mean and the median will ...
... Because the median is relatively unaffected by the presence of extreme values, it is said to be a resistance statistic. For this reason it is frequently used if the data is clearly skewed or is likely to contain and outlier. However, if a distribution is symmetric, both the mean and the median will ...
2.4-guided-notes - Bryant Middle School
... As a measure of variation, the range is very easy to compute. However, it only uses two values from the data set, and it’s possible that one, or both of the values, are _______________. Two measures of variation that use ______ the entries in a data set are the _________________ and the ____________ ...
... As a measure of variation, the range is very easy to compute. However, it only uses two values from the data set, and it’s possible that one, or both of the values, are _______________. Two measures of variation that use ______ the entries in a data set are the _________________ and the ____________ ...
Analysis of Quantitative Data
... Range – The range is a measure of dispersion, found by finding the highest score/number and taking away the lowest score giving the difference between the two. ...
... Range – The range is a measure of dispersion, found by finding the highest score/number and taking away the lowest score giving the difference between the two. ...
lesson 6: measures of spread or variation (section 3-3)
... µ , is presumably unknown, so we replace it with the sample mean, x . We also replace the population size, N, with n − 1 . But …. Note 2: Why n − 1 , not n? Why is s the square root of a “tilted” average of the squared deviations from the sample mean, x ? The appropriate reference point is still the ...
... µ , is presumably unknown, so we replace it with the sample mean, x . We also replace the population size, N, with n − 1 . But …. Note 2: Why n − 1 , not n? Why is s the square root of a “tilted” average of the squared deviations from the sample mean, x ? The appropriate reference point is still the ...
Data Mining: Concepts and Techniques
... smooth by fitting the data into regression functions A regression is a technique that conforms data values to a function. Linear regression involves finding the “best” line to fit two attributes (or variables) so that one attribute can be used to predict the other. ...
... smooth by fitting the data into regression functions A regression is a technique that conforms data values to a function. Linear regression involves finding the “best” line to fit two attributes (or variables) so that one attribute can be used to predict the other. ...
Time series

A time series is a sequence of data points, typically consisting of successive measurements made over a time interval. Examples of time series are ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average. Time series are very frequently plotted via line charts. Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, intelligent transport and trajectory forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and largely in any domain of applied science and engineering which involves temporal measurements.Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. While regression analysis is often employed in such a way as to test theories that the current values of one or more independent time series affect the current value of another time series, this type of analysis of time series is not called ""time series analysis"", which focuses on comparing values of a single time series or multiple dependent time series at different points in time.Time series data have a natural temporal ordering. This makes time series analysis distinct from cross-sectional studies, in which there is no natural ordering of the observations (e.g. explaining people's wages by reference to their respective education levels, where the individuals' data could be entered in any order). Time series analysis is also distinct from spatial data analysis where the observations typically relate to geographical locations (e.g. accounting for house prices by the location as well as the intrinsic characteristics of the houses). A stochastic model for a time series will generally reflect the fact that observations close together in time will be more closely related than observations further apart. In addition, time series models will often make use of the natural one-way ordering of time so that values for a given period will be expressed as deriving in some way from past values, rather than from future values (see time reversibility.)Time series analysis can be applied to real-valued, continuous data, discrete numeric data, or discrete symbolic data (i.e. sequences of characters, such as letters and words in the English language.).