IT-Enabled Process Improvement
... Be sure you have the right amount of statistical power. Be sure to use the best measurement tools available. Beware of multiple comparisons. Keep clear in your mind what you're trying to discover - look at ...
... Be sure you have the right amount of statistical power. Be sure to use the best measurement tools available. Beware of multiple comparisons. Keep clear in your mind what you're trying to discover - look at ...
Examining Residuals in Simple Linear Regression
... with mean 0 and constant standard deviation. In a real problem we don’t get to observe the errors e1 , . . . , en ; but we do observe the residuals ê1 , . . . , ên . If the residuals appear to be roughly independent and normally distributed with mean 0 and constant standard deviation, we assume th ...
... with mean 0 and constant standard deviation. In a real problem we don’t get to observe the errors e1 , . . . , en ; but we do observe the residuals ê1 , . . . , ên . If the residuals appear to be roughly independent and normally distributed with mean 0 and constant standard deviation, we assume th ...
Measures of Position
... Can be used to compare data values from different data sets by “converting” raw data to a standardized scale Calculation involves the mean and standard deviation of the data set Represents the number of standard deviations that a data value is from the mean for a specific distribution We will use ex ...
... Can be used to compare data values from different data sets by “converting” raw data to a standardized scale Calculation involves the mean and standard deviation of the data set Represents the number of standard deviations that a data value is from the mean for a specific distribution We will use ex ...
Chapter 1
... Measures of Center Explain what a “measure of center” attempts to describe about a data set. o One number that represents the whole data set. Name four measures of center (ans. Mean, Median, Mode, Midrange). Explain the difference between each of them. What are their relative strengths and weaknesse ...
... Measures of Center Explain what a “measure of center” attempts to describe about a data set. o One number that represents the whole data set. Name four measures of center (ans. Mean, Median, Mode, Midrange). Explain the difference between each of them. What are their relative strengths and weaknesse ...
7.3 Measures of Central Tendency and Measures of Dispersion
... Measures of Central Tendency and Measures of Dispersion ...
... Measures of Central Tendency and Measures of Dispersion ...
slides
... Each eigenvector ui is associated with some variance associated by i Uncorrelated features with higher variance (represented by i) contain more information Idea: ...
... Each eigenvector ui is associated with some variance associated by i Uncorrelated features with higher variance (represented by i) contain more information Idea: ...
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... step of a times series decomposition is to remove seasonal effects in the data. Without deseasonalizing the data, we may, for example, incorrectly infer that recent increase patterns will continue indefinitely; i.e., a growth trend is present, when actually the increase is 'just because it is that t ...
... step of a times series decomposition is to remove seasonal effects in the data. Without deseasonalizing the data, we may, for example, incorrectly infer that recent increase patterns will continue indefinitely; i.e., a growth trend is present, when actually the increase is 'just because it is that t ...
CH 4 Summary Statistics: Measures of Location and Dispersion
... Note: does not always exist/can be more than one Unstable (What happens if a 3 is changed in our example) can be used with qualitative data Example Average hair color Data (black, brown, black, red, blonde, brown, brown) ...
... Note: does not always exist/can be more than one Unstable (What happens if a 3 is changed in our example) can be used with qualitative data Example Average hair color Data (black, brown, black, red, blonde, brown, brown) ...
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.).