Statistical Analysis: Microsoft Excel 2010
“Excel has become the standard platform for quantitative analysis. Carlberg has become a world-class guide for Excel users wanting to do quantitative analysis. The combination makes Statistical Analysis: Microsoft Excel 2010 a must-have addition to the library of those who want to get the job done and done right.”
—Gene V Glass, Regents’ Professor Emeritus, Arizona State University
Use Excel 2010’s statistical tools to transform your data into knowledge
Use Excel 2010’s powerful statistical
tools to gain a deeper understanding of your data,
make more accurate and reliable inferences, and solve problems in fields ranging from business to health sciences.
Top Excel guru Conrad Carlberg shows how to use Excel 2010 to perform the core statistical tasks every business professional, student, and researcher should master. Using real-world examples, Carlberg helps you choose the right technique for each problem and get the most out of Excel’s statistical features, including its new consistency functions. Along the way, you discover the most effective ways to use correlation and regression and analysis of variance and covariance. You see how to use Excel to test statistical hypotheses using the normal, binomial, t and F distributions.
Becoming an expert with Excel statistics has never been easier! You’ll find crystal-clear instructions, insider insights, and complete step-by-step projects—all complemented by an extensive set of web-based resources.
• Master Excel’s most useful descriptive and inferential statistical tools
• Tell the truth with statistics, and recognize when others don’t
• Accurately summarize sets of values
• View how values cluster and disperse
• Infer a population’s characteristics from a sample’s frequency distribution
• Explore correlation and regression to learn how variables move in tandem
• Understand Excel’s new consistency functions
• Test differences
between two means using z tests, t tests, and Excel’s
Data Analysis Add-in
• Use ANOVA and ANCOVA to test differences between more than two means
• Explore statistical power by manipulating mean differences, standard errors, directionality, and alpha