## We are happy to share these links to help you get started in analytics

"Do Baseball Players Regress Toward the Mean?” http://economics-files.pomona.edu/GarySmith/BBregress/baseball.html

An example of Median income:

http://www.census.gov/content/dam/Census/library/publications/2015/demo/p60-252.pdf

The Online Stats Book has a chapter on descriptive statistics here:

http://onlinestatbook.com/2/introduction/descriptive.html

The Social Research Methods web page also has a chapter on descriptive statistics: http://www.socialresearchmethods.net/kb/statdesc.php

To get into more detail on exploratory statistics, take a look at the Engineering Statistics Handbook:

http://www.itl.nist.gov/div898/handbook/eda/eda.htm

Frederick Hartwig and Brian E. Dearing wrote what looks like a dull book on the outside on exploratory statistics, called Exploratory Data Analysis (Quantitative Applications in the Social Sciences). You’ll find in-depth exercises, including stem and leaf plots, skewness, and outliers. Significance testing is also covered.

Article on how our use of data is expanding and how to keep it all simple:

http://www.analyticbridge.com/profiles/blogs/new-aberdeen-group-research-simple-analytics-is-good-for-business

Here is a case study on data preparation:

http://www.datasciencecentral.com/forum/topics/data-preparation-case-study-preparing-child-mortality-data-for

An article on avoiding data prep mistakes:

http://www.datasciencecentral.com/profiles/blogs/five-data-preparation-mistakes

There actually is a book on data preparation:

See here for explanations of t and p values:

http://blog.minitab.com/blog/adventures-in-statistics/understanding-hypothesis-tests%3A-significance-levels-alpha-and-p-values-in-statistics

Read about tests of significance here: http://www.stat.yale.edu/Courses/1997-98/101/sigtest.htm

Here’s a good overview of significance: http://researchrundowns.com/quantitative-methods/significance-testing/

t test information: http://docs.statwing.com/examples-and-definitions/t-test/statistical-significance/

Really specific significance test suggestions can be found here: http://www.ats.ucla.edu/stat/stata/whatstat/whatstat.htm

This chart not only shows you all the tests you can run but also how to do it in 4 different programs, including SPSS: http://www.ats.ucla.edu/stat/mult_pkg/whatstat/

Here is that chart again, restated by the original author: http://bama.ua.edu/~jleeper/627/choosestat.html

Here’s some explanation of sum of squares: https://en.wikipedia.org/wiki/Total_sum_of_squares

Here is explanation of SPSS’s output for ANOVA and other parts of regression: http://www.ats.ucla.edu/stat/spss/output/reg_spss.htm

Here is a white paper on endogeneity or confounding variables: http://people.bu.edu/tsimcoe/code/Endog-PDW.pdf

David Kremelberg wrote the book,

Kevin MacDonell and Peter Wylie wrote the book,

Peter Wylie wrote the very first book on analytics for our profession:

Joshua Birkholz wrote this excellent book on understanding how analytics fits into a fundraising shop:

An article on data visualization and preparation for it: “SIX Questions and SIX Steps to Through-the-Roof, Human-Centric BI and Metrics-Driven Results” through DataScience Central:

http://www.analyticbridge.com/profiles/blogs/six-questions-and-six-steps-to-through-the-roof-human-centric-bi

An example of Median income:

http://www.census.gov/content/dam/Census/library/publications/2015/demo/p60-252.pdf

The Online Stats Book has a chapter on descriptive statistics here:

http://onlinestatbook.com/2/introduction/descriptive.html

The Social Research Methods web page also has a chapter on descriptive statistics: http://www.socialresearchmethods.net/kb/statdesc.php

To get into more detail on exploratory statistics, take a look at the Engineering Statistics Handbook:

http://www.itl.nist.gov/div898/handbook/eda/eda.htm

Frederick Hartwig and Brian E. Dearing wrote what looks like a dull book on the outside on exploratory statistics, called Exploratory Data Analysis (Quantitative Applications in the Social Sciences). You’ll find in-depth exercises, including stem and leaf plots, skewness, and outliers. Significance testing is also covered.

Article on how our use of data is expanding and how to keep it all simple:

http://www.analyticbridge.com/profiles/blogs/new-aberdeen-group-research-simple-analytics-is-good-for-business

Here is a case study on data preparation:

http://www.datasciencecentral.com/forum/topics/data-preparation-case-study-preparing-child-mortality-data-for

An article on avoiding data prep mistakes:

http://www.datasciencecentral.com/profiles/blogs/five-data-preparation-mistakes

There actually is a book on data preparation:

*Data Preparation for Data Mining (The Morgan Kaufmann Series in Data Management Systems)*by Dorian PyleSee here for explanations of t and p values:

http://blog.minitab.com/blog/adventures-in-statistics/understanding-hypothesis-tests%3A-significance-levels-alpha-and-p-values-in-statistics

Read about tests of significance here: http://www.stat.yale.edu/Courses/1997-98/101/sigtest.htm

Here’s a good overview of significance: http://researchrundowns.com/quantitative-methods/significance-testing/

t test information: http://docs.statwing.com/examples-and-definitions/t-test/statistical-significance/

Really specific significance test suggestions can be found here: http://www.ats.ucla.edu/stat/stata/whatstat/whatstat.htm

This chart not only shows you all the tests you can run but also how to do it in 4 different programs, including SPSS: http://www.ats.ucla.edu/stat/mult_pkg/whatstat/

Here is that chart again, restated by the original author: http://bama.ua.edu/~jleeper/627/choosestat.html

Here’s some explanation of sum of squares: https://en.wikipedia.org/wiki/Total_sum_of_squares

Here is explanation of SPSS’s output for ANOVA and other parts of regression: http://www.ats.ucla.edu/stat/spss/output/reg_spss.htm

Here is a white paper on endogeneity or confounding variables: http://people.bu.edu/tsimcoe/code/Endog-PDW.pdf

David Kremelberg wrote the book,

*Practical Statistics: A Quick and Easy Guide to IBM® SPSS® Statistics, STATA, and Other Statistical Software*.Kevin MacDonell and Peter Wylie wrote the book,

*Score!*The price on Amazon is too high — find it at the CASE store. It covers fundraising uses of modeling.Peter Wylie wrote the very first book on analytics for our profession:

*Data Mining for Fundraisers*.Joshua Birkholz wrote this excellent book on understanding how analytics fits into a fundraising shop:

*Fundraising Analytics: Using Data to Guide Strategy*An article on data visualization and preparation for it: “SIX Questions and SIX Steps to Through-the-Roof, Human-Centric BI and Metrics-Driven Results” through DataScience Central:

http://www.analyticbridge.com/profiles/blogs/six-questions-and-six-steps-to-through-the-roof-human-centric-bi