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(2 classifications) (13 resources)

Data Collection -- Data Management and Organization

Statistical Topic Classifications
Missing data (1)
Outliers (4)

View Resource Cartoon: Data Management Work

A cartoon to teach about data management issues. Cartoon by John Landers ( based on an idea from Dennis Pearl (The Ohio State University). Free to use in the classroom and on course...
View Resource Cartoon: Spreadsheet Experience

A cartoon to teach about the use of spreadsheet programs. Cartoon by John Landers ( based on an idea from Dennis Pearl (The Ohio State University). Free to use in the classroom and...
View Resource Case Studies in Process Monitoring (Engineering Statistics Handbook)

This part of the NIST Engineering Statistics handbook contains case studies for the process or product monitoring and control chapter.
View Resource Data Management, EDA, and Regression Analysis with 1969-2000 Major League Baseball Attendance

This article describes a dataset containing Major League Baseball data from seasons 1969 through 2000 and illustrates how this data can be used as a course long project covering basic data management...
View Resource DataFerrett

DataFerrett is a unique data analysis and extraction tool?with recoding capabilities?to customize federal, state, and local data to suit your requirements. Using DataFerrett, you can develop an...
View Resource Exploratory Data Analysis (Engineering Statistics Handbook)

This chapter of the NIST Engineering Statistics handbook describes Exploratory Data Analysis with an introduction, a discussion of the assumptions, a description of the techniques used, and a set of...
View Resource General FAQ #25: Handling missing or incomplete data

Answers how to handle missing or incomplete data.
View Resource Getting Started with SPSS for Macintosh

This tutorial introduces the statistical software SPSS for Macintosh users. Topics covered include: Preparing Your Data, Descriptive Analysis, SPSS Output, Correlation, Simple Linear Regression,...
View Resource Manual Part VII: Multiple Regression: Using Categorical Variables in Multiple Regression

Describes dummy coding for categorical variables. Addresses types of coding schemes and ANOVA.
View Resource Plain English on Data Quality: Mistakes to Avoid for DW Data Quality

Article discussing two mistakes businesses make with their data and how if affects the business.