It takes a real teacher, such as Professor Sally Caldwell, to write a statistics textbook that allows students to truly understand and connect with the subject matter. STATISTICS UNPLUGGED comes into its Second Edition with the same goals as in the First Edition: to help students overcome their apprehension of statistics. Caldwell does this by emphasizing the logic behind statistical analysis, and by helping students gain an intuitive understanding of statistics not just by focusing on formulas and equations. Student feedback about the book repeatedly points out Caldwell's student-friendly language and how she writes in a way that makes the material understandable. Brief and affordable, STATISTICS UNPLUGGED succinctly alleviates student's fears of formulas, and helps them to understand the relevance of statistics to their lives.
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Methods Material and Moments to Remember
The What and How of Statistics
Describing Data and Distributions
The Shape of Distributions
The Normal Curve
Four Fundamental Concepts
Hypothesis Testing With a Single Sample Mean
Hypothesis Testing With Two Samples Mean Difference and Difference of Means
Beyond the Null Hypothesis
05 level alternative or research ANOVA Answer Application Questions/Problems appropriate null hypothesis association Assuming calculated test statistic Central Limit Theorem chance chi-square test concepts confidence interval contingency table critical value degrees of freedom difference of means distribution of sample equal estimate of variance example extreme F ratio fail to reject Figure flextime workers formula hypothesis testing inferential statistics involving LEARNING CHECK Question level of measurement level of significance logic mean difference Mean of Sample median normal distribution notion number of degrees population mean procedure random sample raw score region regression reject the null research hypothesis research situation sample means sampling distribution sampling error scatter plot Score/Value Score/Value scores or values square root standard deviation standard error standardized normal curve statistical analysis statisticians sum of squares test scores there’s tion two-tailed test Type I error understand variables variation What’s you’re Z scores