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Introductory statistics and analytics a resampling perspective by peter

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Introductory statistics and analytics a resampling perspective by peter Introductory statistics and analytics a resampling perspective by peter Introductory statistics and analytics a resampling perspective by peter Introductory statistics and analytics a resampling perspective by peter Introductory statistics and analytics a resampling perspective by peter Introductory statistics and analytics a resampling perspective by peter

ment variable, 29 median, 13 Mendel’s peas, 72 mode, 15 multicollinearity, 275 multistage sampling, 118 munging, 24 nonresponse bias, 119 normal distribution, 52, 80 normalization, 25 nuisance variable, 248 null hypothesis, 155 null model, see null hypothesis, 152, 155 observation, 113 observational study, outlier, 32 paired data, 13 parameter, 108 parametric bootstrap, 141 percentile, 17 permutation test, 46 pie charts, 61 PivotTable, 92 placebo effect, 12 point estimate, 125 population, 108 power, 165 practical significance, 55 prior probability, 96 probability, 73 probability distribution, 74, 78 pseudo-random number generator, p-value, 153 qualitative variable, see categorical variable, 29 R2 , 261 random assignment, INDEX random number, random number generator, 7, 141 random sample, 108 simple 107 random variable, 77 range, 16 regression, 209 regression line, 211 Relational Database, 25 relative frequencies, 34 replication, 225 resample, 113 resample with replacement, see sample with replacement, 114 residual, 211 RMSE, 262 root mean squared error, 262 sample, 108, 113 sample standard deviation, 19 sample variance, 19 sample with replacement, 111 sampling error, 118 sampling frame, 108, 118 sampling with replacement, 114 sampling without replacement, 114 self selection, 119 shuffling, 114 significance level, 153, 229 simple random sample (SRS), see random sample (simple) Simpson’s Paradox, 89 simulated population, 111, 144 simulation, 114 single simulation trial, 114 single-blind, 12 skew, 39 slope coefficients, 217 SQL, 26 squared residual error, 212 SRS, see random sample (simple), 107 standard deviation, 18 standard error, 132 statistic, 109 statistical significance, 55, 153 STDEV, 19 stem-and-leaf plot, 36 stratification, 247 stratified sampling, 117 Structured Query Language, 26 Student’s t, 131 survey, 104 systematic sampling, 118 t-distribution, 131 tail of the distribution, 39 test statistic, 22 treatment group, www.downloadslide.com 285 INDEX trend line, 210 triple-blind, 12 Tukey, 99 Type I error, 54 Type II error, 54 variability, 16 variable, 77 variance, 18 Venn Diagrams, 74 whiskers, 37 urn, see box, 111 z-interval, 130 VAR, 19 www.downloadslide.com www.downloadslide.com WILEY END USER LICENSE AGREEMENT Go to www.wiley.com/go/eula to access Wiley’s ebook EULA ...www.downloadslide.com 285 INDEX trend line, 210 triple-blind, 12 Tukey, 99 Type I error, 54 Type II error, 54 variability, 16 variable, 77 variance, 18 Venn Diagrams, 74 whiskers, 37... see box, 111 z-interval, 130 VAR, 19 www.downloadslide.com www.downloadslide.com WILEY END USER LICENSE AGREEMENT Go to www.wiley.com/go/eula to access Wiley’s ebook EULA

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    Chapter 1 Designing and Carrying Out a Statistical Study

    1.2 Is Chance Responsible? The Foundation of Hypothesis Testing

    1.5 What to Measure-Central Location

    1.7 What to Measure-Distance (Nearness)

    1.10 Variables and Their Flavors

    1.11 Examining and Displaying the Data

    1.12 Are we Sure we Made a Difference?

    2.3 How Odd is Odd?

    2.4 Statistical and Practical Significance

    2.5 When to use Hypothesis Tests

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