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ACE2046 Quantitative Techniques Statistical Computing
ACE2046 Quantitative Techniques Statistical Computing

... inferences from the sample data to the population from which the sample has been taken. It allows us to distinguish real differences from random variation. • Random variables: The quantities measured in a study. A particular outcome is an observation. • Data: Several different observations collected ...
Chapters 1-2 course notes
Chapters 1-2 course notes

Module 3 Test.tst
Module 3 Test.tst

... 20) Find the z-score corresponding to the given value and use the z-score to determine whether the value is unusual. Consider a score to be unusual if it is at least three standard deviations above or below the mean. Round to the z-score to two decimal places, if necessary. A department store, on a ...
Data Objects
Data Objects

... It shows what proportion of cases fall into each of several categories ...
Chapter 11 Summary
Chapter 11 Summary

slides in pdf - Università degli Studi di Milano
slides in pdf - Università degli Studi di Milano

... the area of the bar that denotes the value, not the height as in bar charts, a crucial distinction when the categories are not of uniform width ...
Data Transforms: Natural Logarithms and Square Roots
Data Transforms: Natural Logarithms and Square Roots

... former are based on ratio level data (real values) whereas the latter are based on ranked or ordinal level data. Of course, non-parametrics are extremely useful as sometimes our data is highly non-normal, meaning that comparing the means is often highly misleading, and can lead to erroneous results. ...
CSCE590/822 Data Mining Principles and Applications
CSCE590/822 Data Mining Principles and Applications

...  Different considerations between the time when the data was collected and when it is analyzed.  Human/hardware/software problems  Noisy data (incorrect values) may come from  Faulty data collection instruments  Human or computer error at data entry  Errors in data transmission  Inconsistent ...
Unit 6 Faculty Guide
Unit 6 Faculty Guide

... Activity Description The purpose of this activity is to help students visualize the spread of the data by focusing on the concept of deviations from the mean. Students can work on this activity either individually or in groups. In questions 1 and 2, students make dotplots of the data and then draw h ...
Statistics and Statistical Graphs
Statistics and Statistical Graphs

... INT ...
REVIEW ON DATA MINING
REVIEW ON DATA MINING

... computerized methods applied to find information among these large repositories of data available to organizations whether it was online or offline. Data mining was conceptualized in the 1990s as a means of addressing the problem of analyzing the vast repositories of data that are available to manki ...
Measures of Variation PowerPoint
Measures of Variation PowerPoint

Week 2, Lecture 3, Descriptive measures for grouped data
Week 2, Lecture 3, Descriptive measures for grouped data

... incorrectly recorded. If so, it needs to be corrected before further analysis. ...
Modeling Lifetime Value in the Insurance Industry
Modeling Lifetime Value in the Insurance Industry

TextVis: An Integrated Visual Environment for Text Mining*
TextVis: An Integrated Visual Environment for Text Mining*

Exercise3
Exercise3

... This exercise will be done using Excel. The exercise was designed using the 2007 version of Excel. To accomplish the exercise the Data Analysis Toolpak must be used. To use this tool it must first be activated. The directions for this can be found in the attached appendix. The original data for this ...
Report for Data Mining
Report for Data Mining

Chapter 3 Numerically Summarizing Data
Chapter 3 Numerically Summarizing Data

Chapter 4
Chapter 4

... earthquake Magnitudes looks like this: ...
p - Claudia Wagner
p - Claudia Wagner

...  Minimum sample size increases with decreasing effect size ...
Describing the Graphs
Describing the Graphs

Mean - BCI-Calculus45
Mean - BCI-Calculus45

... The average of the squared differences from the Mean. To calculate the variance follow these steps: ...
Student Notes – Prep Session Topic: Exploring Data
Student Notes – Prep Session Topic: Exploring Data

... increase of between $500 and $2,000 based on a performance review by the mayor's staff. Some employees are members of the mayor's political party, and the rest are not. Students at the local high school form two lists, A and B, one for the raises granted to employees who are in the mayor's party, an ...
Atlantic Canada Grades 4 to 12 Curriculum Links, 2005
Atlantic Canada Grades 4 to 12 Curriculum Links, 2005

... change in one quantity affects a related quantity F1 communicate through example the distinction between biased and unbiased sampling, and first- and second-hand data F2 formulate questions for investigation from relevant contexts F3 select, defend, and use appropriate data collection methods and ev ...
Graphically Assessing Normality
Graphically Assessing Normality

... show that normality is plausible and that data that approximately follow the 6895-99.7 rule and show a linear pattern in the normal quantile plot may not be normally distributed. These check procedures can only show that you have no evidence to think the data are not normal. Discuss with the student ...
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Data mining

Data mining (the analysis step of the ""Knowledge Discovery in Databases"" process, or KDD), an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets (""big data"") involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amount of data, not the extraction of data itself.It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The popular book ""Data mining: Practical machine learning tools and techniques with Java"" (which covers mostly machine learning material) was originally to be named just ""Practical machine learning"", and the term ""data mining"" was only added for marketing reasons. Often the more general terms ""(large scale) data analysis"", or ""analytics"" – or when referring to actual methods, artificial intelligence and machine learning – are more appropriate.The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting are part of the data mining step, but do belong to the overall KDD process as additional steps.The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.
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