|
|
Product Description
Many researchers jump from data collection directly into testing hypothesis without realizing these tests can go profoundly wrong without clean data. This book provides a clear, accessible, step-by-step process of important best practices in preparing for data collection, testing assumptions, and examining and cleaning data in order to decrease error rates and increase both the power and replicability of results.Jason W. Osborne, author of the handbook Best Practices in Quantitative Methods (SAGE, 2008) provides easily-implemented suggestions that are evidence-based and will motivate change in practice by empirically demonstrating―for each topic―the benefits of following best practices and the potential consequences of not following these guidelines.
Features
- Used Book in Good Condition
Customers Who Bought This Item Also Bought
- Introducing Python: Modern Computing in Simple Packages
- Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
- Introduction to Machine Learning with Python: A Guide for Data Scientists
- Spreadsheet Modeling & Decision Analysis: A Practical Introduction to Business Analytics
- Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
- Regression Analysis by Example
- Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
- Competing on Analytics: The New Science of Winning; With a New Introduction
- R in Action: Data Analysis and Graphics with R
- Database Systems: A Practical Approach to Design, Implementation, and Management (6th Edition)
*If this is not the "Best Practices in Data Cleaning: A Complete Guide to Everything You Need to Do Before and After Coll" product you were looking for, you can check the other results by clicking this link
.
Details were last updated on Dec 25, 2024 01:20 +08.
