Comprehending behavioral statistics
While this book provides the same material found in most introductory statistics texts, it goes beyond the standard by teaching students how to estimate statistics before computations are performed. The ESTAT software helps students build this skill by allowing them to learn to make accurate eyeball-estimates before ever touching a calculator. These estimation techniques are provided for both descriptive and inferential statistics. The ESTAT software is optional with the text, students can learn estimation from information in the book alone if you so choose.
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Mathematical and Probability Concepts
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alternative hypotheses ANOVA answers in Appendix assuming the null Basic exercises continued cell Chapter column computational formula Compute the standard confidence interval correlation coefficient critical value data set degrees of freedom denominator dependent-sample equal Equation errors of prediction estimate example eyeball Eyeball-estimate the standard F ratio frequency polygon histogram hypothesis is true interaction interval/ratio level of measurement level of significance main effect Mann-Whitney measure median mode normal distribution null hypothesis observed value one-tailed outcome parameter Pearson point-estimate points pooled variance population mean pounds probability procedure random sample range method real distribution regression line Reject H0 reject the null rejection region result sample means scatter diagram Section shown in Figure shows Sketch the distribution specified squared deviation standard deviation standard error standardized score sum of squares Suppose tail test statistic therapy type two-tailed Type I error variable wishes to know X-axis zero