introduction to data mining and soft computing
... ∑ Respond quickly to the customer demands ∑ Quick react to the market opportunities and threats ∑ Predict pulse of the customers ∑ Market analysis and financial forecasting. It is absolutely difficult to even attempt to achieve these goals, if the management can not aware about technical growth in t ...
... ∑ Respond quickly to the customer demands ∑ Quick react to the market opportunities and threats ∑ Predict pulse of the customers ∑ Market analysis and financial forecasting. It is absolutely difficult to even attempt to achieve these goals, if the management can not aware about technical growth in t ...
3.1 Measures of central tendency: mode, median, mean, midrange
... To capture the spread of the data we use a measure related to the average distance of the data from the mean. We call this the standard deviation. If we have a population, we report this ave ...
... To capture the spread of the data we use a measure related to the average distance of the data from the mean. We call this the standard deviation. If we have a population, we report this ave ...
File
... What’s the same and what’s different: inter-quartile range, median, mean, mode Convince me how to construct a cumulative frequency curve Always/Sometimes/Never: The median is greater than the interquartile range ...
... What’s the same and what’s different: inter-quartile range, median, mean, mode Convince me how to construct a cumulative frequency curve Always/Sometimes/Never: The median is greater than the interquartile range ...
Solution
... For all of the questions on this assignment, assume the data are a random sample from a normal population, that is X1 , . . . , Xn ∼ iid N (µ, σ 2 ). Depending on the specifics of the question, σ may be known or unknown. I’d recommend that you use R for any problems in which you need to calculate sa ...
... For all of the questions on this assignment, assume the data are a random sample from a normal population, that is X1 , . . . , Xn ∼ iid N (µ, σ 2 ). Depending on the specifics of the question, σ may be known or unknown. I’d recommend that you use R for any problems in which you need to calculate sa ...
Chapter 10: STATISTICAL INFERENCE FOR TWO SAMPLES Part 2
... • If n is large, we don’t need to check the normality of the differences (xdi values) because the central limit theorem will give us normality of a sample mean. • We often set-up the difference as the hypothesized larger mean minus the hypothesize smaller mean (in order to work with a positive test ...
... • If n is large, we don’t need to check the normality of the differences (xdi values) because the central limit theorem will give us normality of a sample mean. • We often set-up the difference as the hypothesized larger mean minus the hypothesize smaller mean (in order to work with a positive test ...
Document
... If students can’t make their own connections with a concept as simple as the slope of a line, they won’t be able to create meaningful interpretations and connections on their own for more sophisticated mathematical concepts. For instance, • What is the significance of the base (growth or decay facto ...
... If students can’t make their own connections with a concept as simple as the slope of a line, they won’t be able to create meaningful interpretations and connections on their own for more sophisticated mathematical concepts. For instance, • What is the significance of the base (growth or decay facto ...
Chapter 3 powerpoints only
... The overall pattern is fairly symmetrical except for 2 states clearly not belonging to the main trend. Alaska and Florida have unusual representation of the elderly in their population. A large gap in the distribution is typically a sign of an outlier. ...
... The overall pattern is fairly symmetrical except for 2 states clearly not belonging to the main trend. Alaska and Florida have unusual representation of the elderly in their population. A large gap in the distribution is typically a sign of an outlier. ...
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.).