1. A first course in statistics [2017]
 McClave, James T., author.
 Twelfth edition.  Boston : Pearson, [2017]
 Description
 Book — xxvi, 613 pages : color illustrations ; 28 cm
 Summary

 1. Statistics, Data, and Statistical Thinking 1.1 The Science of Statistics 1.2 Types of Statistical Applications 1.3 Fundamental Elements of Statistics 1.4 Types of Data 1.5 Collecting Data: Sampling and Related Issues 1.6 The Role of Statistics in Critical Thinking and Ethics Statistics in Action: Social Media Network UsageAre You Linked In? Using Technology: MINITAB: Accessing and Listing Data
 2. Methods for Describing Sets of Data 2.1 Describing Qualitative Data 2.2 Graphical Methods for Describing Quantitative Data 2.3 Numerical Measures of Central Tendency 2.4 Numerical Measures of Variability 2.5 Using the Mean and Standard Deviation to Describe Data 2.6 Numerical Measures of Relative Standing 2.7 Methods for Detecting Outliers: Box Plots and zScores 2.8 Graphing Bivariate Relationships (Optional) 2.9 Distorting the Truth with Descriptive Statistics Statistics in Action: Body Image Dissatisfaction: Real or Imagined? Using Technology: MINITAB: Describing Data TI83/TI84 Plus Graphing Calculator: Describing Data
 3. Probability 3.1 Events, Sample Spaces, and Probability 3.2 Unions and Intersections 3.3 Complementary Events 3.4 The Additive Rule and Mutually Exclusive Events 3.5 Conditional Probability 3.6 The Multiplicative Rule and Independent Events 3.7 Some Additional Counting Rules (Optional) 3.8 Bayes's Rule (Optional) Statistics in Action: Lotto Buster! Can You Improve Your Chance of Winning? Using Technology: TI83/TI84 Plus Graphing Calculator: Combinations and Permutations
 4. Discrete Random Variables 4.1 Two Types of Random Variables 4.2 Probability Distributions for Discrete Random Variables 4.3 Expected Values of Discrete Random Variables 4.4 The Binomial Random Variable 4.5 The Poisson Random Variable (Optional) 4.6 The Hypergeometric Random Variable (Optional) Statistics in Action: Probability in a Reverse Cocaine Sting: Was Cocaine Really Sold? Using Technology: MINITAB: Discrete Probabilities TI83/TI84 Plus Graphing Calculator: Discrete Random Variables and Probabilities
 5. Continuous Random Variables 5.1 Continuous Probability Distributions 5.2 The Uniform Distribution 5.3 The Normal Distribution 5.4 Descriptive Methods for Assessing Normality 5.5 Approximating a Binomial Distribution with a Normal Distribution (Optional) 5.6 The Exponential Distribution (Optional) Statistics in Action: Super Weapons DevelopmentIs the Hit Ratio Optimized? Using Technology: MINITAB: Continuous Random Variable Probabilities and Normal Probability Plots TI83/TI84 Plus Graphing Calculator: Normal Random Variable and Normal Probability Plots
 6. Sampling Distributions 6.1 The Concept of a Sampling Distribution 6.2 Properties of Sampling Distributions: Unbiasedness and Minimum Variance 6.3 The Sampling Distribution of (xbar) and the Central Limit Theorem 6.4 The Sampling Distribution of the Sample Proportion Statistics in Action: The Insomnia Pill: Is It Effective? Using Technology: MINITAB: Simulating a Sampling Distribution
 7. Inferences Based on a Single Sample: Estimation with Confidence Intervals 7.1 Identifying and Estimating the Target Parameter 7.2 Confidence Interval for a Population Mean: Normal (z) Statistic 7.3 Confidence Interval for a Population Mean: Student's tStatistic 7.4 LargeSample Confidence Interval for a Population Proportion 7.5 Determining the Sample Size 7.6 Confidence Interval for a Population Variance (Optional) Statistics in Action: Medicare Fraud Investigations Using Technology: MINITAB: Confidence Intervals TI83/TI84 Plus Graphing Calculator: Confidence Intervals
 8. Inferences Based on a Single Sample: Tests of Hypothesis 8.1 The Elements of a Test of Hypothesis 8.2 Formulating Hypotheses and Setting Up the Rejection Region 8.3 Observed Significance Levels: pValues 8.4 Test of Hypothesis about a Population Mean: Normal (z) Statistic 8.5 Test of Hypothesis about a Population Mean: Student's tStatistic 8.6 LargeSample Test of Hypothesis about a Population Proportion 8.7 Calculating Type II Error Probabilities: More about beta (Optional) 8.8 Test of Hypothesis about a Population Variance (Optional) Statistics in Action: Diary of a KLEENEX(R) UserHow Many Tissues in a Box? Using Technology: MINITAB: Tests of Hypotheses TI83/TI84 Plus Graphing Calculator: Tests of Hypotheses
 9. Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses 9.1 Identifying the Target Parameter 9.2 Comparing Two Population Means: Independent Sampling 9.3 Comparing Two Population Means: Paired Difference Experiments 9.4 Comparing Two Population Proportions: Independent Sampling 9.5 Determining the Sample Size 9.6 Comparing Two Population Variances: Independent Sampling (Optional) Statistics in Action: ZixIt Corp. v. Visa USA Inc.A Libel Case Using Technology: MINITAB: TwoSample Inferences TI83/TI84 Plus Graphing Calculator: Two Sample Inferences
 10. Analysis of Variance: Comparing More than Two Means 10.1 Elements of a Designed Study 10.2 The Completely Randomized Design: Single Factor 10.3 Multiple Comparisons of Means 10.4 The Randomized Block Design 10.5 Factorial Experiments: Two Factors Statistics in Action: Voice versus Face RecognitionDoes One Follow the Other? Using Technology: MINITAB: Analysis of Variance TI83/TI84 Plus Graphing Calculator: Analysis of Variance
 11. Simple Linear Regression 11.1 Probabilistic Models 11.2 Fitting the Model: The Least Squares Approach 11.3 Model Assumptions 11.4 Assessing the Utility of the Model: Making Inferences about the Slope beta1 11.5 The Coefficients of Correlation and Determination 11.6 Using the Model for Estimation and Prediction 11.7 A Complete Example Statistics in Action: Can "Dowsers" Really Detect Water? Using Technology: MINITAB: Simple Linear Regression TI83/TI84 Plus Graphing Calculator: Simple Linear Regression
 12. Multiple Regression and Model Building 12.1 MultipleRegression Models PART I: FirstOrder Models with Quantitative Independent Variables 12.2 Estimating and Making Inferences about the beta Parameters 12.3 Evaluating Overall Model Utility 12.4 Using the Model for Estimation and Prediction PART II: Model Building in Multiple Regression 12.5 Interaction Models 12.6 Quadratic and Other Higher Order Models 12.7 Qualitative (Dummy) Variable Models 12.8 Models with Both Quantitative and Qualitative Variables (Optional) 12.9 Comparing Nested Models (Optional) 12.10 Stepwise Regression (Optional) PART III: Multiple Regression Diagnostics 12.11 Residual Analysis: Checking the Regression Assumptions 12.12 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation Statistics in Action: Modeling Condominium Sales: What Factors Affect Auction Price? Using Technology: MINITAB: Multiple Regression TI83/TI84 Plus Graphing Calculator: Multiple Regression
 13. Categorical Data Analysis 13.1 Categorical Data and the Multinomial Experiment 13.2 Testing Categorical Probabilities: OneWay Table 13.3 Testing Categorical Probabilities: TwoWay (Contingency) Table 13.4 A Word of Caution about ChiSquare Tests Statistics in Action: The Case of the Ghoulish Transplant Tissue Using Technology: MINITAB: ChiSquare Analyses TI83/TI84 Plus Graphing Calculator: ChiSquare Analyses
 14. Nonparametric Statistics (available online) 14.1 Introduction: DistributionFree Tests 14.2 SinglePopulation Inferences 14.3 Comparing Two Populations: Independent Samples 14.4 Comparing Two Populations: Paired Difference Experiment 14.5 Comparing Three or More Populations: Completely Randomized Design 14.6 Comparing Three or More Populations: Randomized Block Design 14.7 Rank Correlation 1448 Statistics in Action: Pollutants at a Housing Development: A Case of Mishandling Small Samples 142 Using Technology: MINITAB: Nonparametric Tests 1465.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
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Science Library (Li and Ma)
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QA276 .M378 2017  Unknown 