Using Microsoft Excel for Probability and Statistics
... Using Microsoft Excel for Probability and Statistics Introduction Despite having been set up with the business user in mind, Microsoft Excel is rather poor at handling precisely those aspects of statistics which might be most useful in the business context, namely informative displays of data. Calcu ...
... Using Microsoft Excel for Probability and Statistics Introduction Despite having been set up with the business user in mind, Microsoft Excel is rather poor at handling precisely those aspects of statistics which might be most useful in the business context, namely informative displays of data. Calcu ...
Worksheet 4
... A natural goal is to try to use the distance of a destination to predict the airfare for flying there, and the simplest model for this prediction is to assume that a straight line summarizes the relationship between distance and airfare. 1. Place a straightedge over the scatterplot above so that the ...
... A natural goal is to try to use the distance of a destination to predict the airfare for flying there, and the simplest model for this prediction is to assume that a straight line summarizes the relationship between distance and airfare. 1. Place a straightedge over the scatterplot above so that the ...
Cluster Analysis of Heterogeneous Rank Data
... rankings that are prototypical representatives for each group. Our approach is probabilistic: A probability model is defined capable of representing an individual group. A mixture of such models is then fitted to the data by an alternating estimation procedure. We will first introduce the standard p ...
... rankings that are prototypical representatives for each group. Our approach is probabilistic: A probability model is defined capable of representing an individual group. A mixture of such models is then fitted to the data by an alternating estimation procedure. We will first introduce the standard p ...
A Case Study: Improve Classification of Rare Events
... The first step is to identify a rare case’s K nearest neighbors. As illustrated in Figure 6, for rare case M, we find k=5 other rare cases close to it based on their distances. Next, randomly select a point between two points and generate a new case. For example, we create point m1(c1,c2) between M( ...
... The first step is to identify a rare case’s K nearest neighbors. As illustrated in Figure 6, for rare case M, we find k=5 other rare cases close to it based on their distances. Next, randomly select a point between two points and generate a new case. For example, we create point m1(c1,c2) between M( ...
Powerpoint
... for the set of questions for each student. These sums (or measures of the constructs) can then be used as new variables as the basis of further statistical analysis. There are more sophisticated ways to aggregate the indicator scores into a construct score (e.g, using item response theory models). ...
... for the set of questions for each student. These sums (or measures of the constructs) can then be used as new variables as the basis of further statistical analysis. There are more sophisticated ways to aggregate the indicator scores into a construct score (e.g, using item response theory models). ...
12.2.1 Transforming with Powers and Roots When you visit a pizza
... Populations of living things tend to grow exponentially if not restrained by outside limits such as lack of food or space. More pleasantly (unless we’re talking about credit card debt!), money also displays exponential growth when interest is compounded each time period. Compounding means that las ...
... Populations of living things tend to grow exponentially if not restrained by outside limits such as lack of food or space. More pleasantly (unless we’re talking about credit card debt!), money also displays exponential growth when interest is compounded each time period. Compounding means that las ...
Sources and magnitude of random errors in Acoustic
... Data analyses In the analysis the integrator readings are treated as a time series, the ‘time’ unit being 1 nautical mile. To each observation was attributed a mile figure between 1 and 160. The actual length of a survey was approximately 160 miles and the at tributed figure was chosen such that obs ...
... Data analyses In the analysis the integrator readings are treated as a time series, the ‘time’ unit being 1 nautical mile. To each observation was attributed a mile figure between 1 and 160. The actual length of a survey was approximately 160 miles and the at tributed figure was chosen such that obs ...
Analysis of Process Capability
... Probability Plotting - example In this example, the points on Weibull probability paper fall in a fairly linear fashion, indicating that the Weibull distribution provides a good fit to the data. If the points did not seem to follow a straight line, we might want to consider using another probabilit ...
... Probability Plotting - example In this example, the points on Weibull probability paper fall in a fairly linear fashion, indicating that the Weibull distribution provides a good fit to the data. If the points did not seem to follow a straight line, we might want to consider using another probabilit ...
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.).