Making History Count

• Part I. Elementary Statistical Analysis: 1. Introduction; 2. Descriptive statistics; 3. Correlation; 4. Simple linear regression; Part II. Samples and Inductive Statistics: 5. Standard errors and confidence intervals; 6. Hypothesis testing; 7. Non-parametric tests; Part III. Multiple Linear Regression: 8. Multiple relationships; 9. The classical linear regression model; 10. Dummy variables and lagged values; Part IV. Further Topics in Regression Analysis: 11. Violating the assumptions of the classical model; 12. Non-linear models and functional forms; 13. Logit, probit, and tobit models; Part V. Specifying and Interpreting Models: Four Case Studies: 14. Case studies 1 and 2: unemployment in Britain and emigration from Ireland; 15. Case studies 3 and 4: the Old Poor Law in England and leaving home in the United States, 1850–60; Appendix A. The four data sets; Appendix B. Index numbers; Index.

'This is an excellent book which serves two purposes. It fills a much needed gap in the literature for the historian who isn't particularly happy in handling numerical data. it also benefits other students who require a passing knowledge of statistics. nothing to my knowledge, has come on the market since Maroney's Facts from Figures in the 1950s provides such an extensive insight into statistical methodology.' Open History