1  12
 Mendenhall, William.
 2nd ed.  San Francisco, Calif. : Dellen Pub. Co. ; London : Collier Macmillan, c1988.
 Description
 Book — xvi, 1036 p. : ill. ; 25 cm.
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QA276 .M428 1988  Available 
 Mendenhall, William.
 4th ed.  Englewood Cliffs, N.J. : PrenticeHall, c1995.
 Description
 Book — xvii, 1182 p. : ill. ; 25 cm. + 1 computer disk (3 1/2 in.)
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QA276 .M429 1995  Unknown 
4. A first course in statistics [1995]
 McClave, James T.
 5th ed.  Englewood Cliffs, N.J. : Prentice Hall, c1995.
 Description
 Book — xv, 608 p. : ill. (some col.) ; 25 cm. + 1 computer disk (3 1/2 in.)
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QA276.12 .M3997 1995  Unknown 
QA276.12 .M3997 1995  Unknown 
QA276.12 .M3997 1995  Unknown 
QA276.12 .M3997 1995  Unknown 
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QA276.12 .M3997 1995  Available 
 Mendenhall, William.
 5th ed.  Upper Saddle River, N.J. : Pearson PrenticeHall, c2007.
 Description
 Book — xii, 1060 p. : ill. ; 26 cm.
 Summary

For engineering statistics courses in departments of Statistics and Engineering. This text is designed for a twosemester introductory course in statistics for students majoring in engineering or any of the physical sciences. Inevitalby, once these studenrts graduate and are employed, they will be involved in the collection and analysis of data and will be required to think critically about the results. Consequently, they need to acquire knowledge of the basic concepts of data description and statistical inference and familiarity with statistical methods they are required to use on the job.The text includes optional theoretical exercises allowing instructors who choose to emphasize theory to do so without requiring additional materials. The assumed mathematical background is a twosemester sequence in calculus  that is, the course could be taught to students of average mathematical talent and with a basic understanding of the principles of differential and integral calculus. Datasets and other resources (where applicable) for this book are available here.
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QA276 .M429 2007  Unknown 
6. A first course in statistics [1997]
 McClave, James T.
 6th ed.  Upper Saddle River, N.J. : Prentice Hall, c1997.
 Description
 Book — xix, 519 p. : ill. (some col.), col. map ; 26 cm.
 Summary

 1. Statistics, Data, and Statistical Thinking. The Science of Statistics. Types of Statistical Applications. Fundamental Elements of Statistics. Types of Data. Collecting Data. The Role of Statistics in Critical Thinking.
 2. Methods for Describing Sets of Data. Describing Qualitative Data. Graphic Methods for Describing Quantitative Data. Summation Notation. Numerical Measures of Central Tendency. Numerical Measures of Variability. Interpreting the Standard Deviation. Numerical Measures of Relative Standing. Quartiles and Box Plots (Optional). Distorting the Truth with Descriptive Techniques.
 3. Probability. Events, Sample Spaces, and Probability. Unions and Intersections. Complementary Events. The Additive Rule and Mutually Exclusive Events. Conditional Probability. The Multiplicative Rule and Independent Events. Probability and Statistics: An Example. Random Sampling.
 4. Random Variables and Probability Distributions. Two Types of Random Variables. Probability Distributions for Discrete Random Variables. The Binomial Distribution. Probability Distributions for Continuous Random Variables. The Normal Distribution. Sampling Distributions. Properties of Sampling Distributions: Unbiasedness and Minimum Variance (Optional). The Central Limit Theorem.
 5. Inferences Based on a Single Sample: Estimation with Confidence Intervals. LargeSample Confidence Interval for a Population Mean. Small Sample Confidence Interval for a Population Mean. LargeSample Confidence Interval for a Population Proportion. Determining the Sample Size.
 6. Inferences Based on a Single Sample: Tests of Hypothesis. The Elements of a Test of Hypothesis. LargeSample Test of Hypothesis About a Population Mean. Observed Significance Levels: p Values. SmallSample Test of Hypothesis About a Population Mean. LargeSample Test of Hypothesis About a Population Proportion. A Nonparametric Test About a Population Median (Optional).
 7. Comparing Population Means. Comparing Two Population Means: Independent Sampling. Comparing Two Population Means: Paired Difference Experiments. Determining the Sample Size. A Nonparametric Test for Comparing Two Populations: Independent Sampling (Optional). A Nonparametric Test for Comparing Two Populations: Paired Difference Experiments (Optional). Comparing Three or More Population Means: Analysis of Variance (Optional).
 8. Comparing Population Proportions. Comparing Two Population Proportions: Independent Sampling. Determining the Sample Size. Comparing Population Proportions: Multinomial Experiment (Optional). Contingency Table Analysis (Optional).
 9. Simple Linear Regression. Probabilistic Models. Fitting the Model: The Least Squares Approach. Model Assumptions. An Estimator of !
 s2. Assessing the Utility of the Model: Making Inferences About the Slope !
 b1. The Coefficient of Correlation. The Coefficient of Determination. Using the Model for Estimation and Prediction. Simple Linear Regression: An Example. A Nonparametric Test for Correlation (Optional). Appendix A. Tables. Appendix B. Data Sets. Appendix C. Calculation Formulas for Analysis of Variance: Independent Sampling.
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QA276 .M378 1997  Available 
7. Statistics [2003]
 McClave, James T.
 9th ed.  Upper Saddle River, NJ : Prentice Hall, c2003.
 Description
 Book — xxvi, 850 p. : ill. ; 27 cm + 1 computer optical disc (4 3/4 in.).
 Summary

 1. Statistics, Data, and Statistical Thinking. The Science of Statistics. Types of Statistical Applications. Fundamental Elements of Statistics. Types of Data. Collecting Data. The Role of Statistics in Critical Thinking.
 2. Methods for Describing Sets of Data. Describing Qualitative Data. Graphical Methods for Describing Quantitative Data. Summation Notation. Numerical Measures of Central Tendency. Numerical Measures of Variability. Interpreting the Standard Deviation. Numerical Measures of Relative Standing. Methods for Detecting Outliers (Optional). Graphing Bivariate Relationships (Optional). Distorting the Truth with Descriptive Techniques.
 3. Probability. Events, Sample Spaces, and Probability. Unions and Intersections. Complementary Events. The Additive Rule and Mutually Exclusive Events. Conditional Probability. The Multiplicative Rule and Independent Events. Random Sampling. Some Counting Rules (Optional).
 4. Discrete Random Variables. Two Types of Random Variables. Probability Distributions for Discrete Random Variables. Expected Values of Discrete Random Variables. The Binomial Random Variable. The Poisson Random Variable (Optional). The Hypergeometric Random Variable (Optional).
 5. Continuous Random Variables. Continuous Probability Distributions. The Uniform Distribution. The Normal Distribution. Descriptive Methods for Assessing Normality. Approximating a Binomial Distribution with a Normal Distribution (Optional). The Exponential Distribution (Optional).
 6. Sampling Distributions. What Is a Sampling Distribution? Properties of Sampling Distributions: Unbiasedness and Minimum Variance (Optional). The Central Limit Theorem.
 7. Inferences Based on a Single Sample: Estimation with Confidence Intervals. LargeSample Confidence Interval for a Population Mean. SmallSample Confidence Interval for a Population Mean. LargeSample Confidence Interval for a Population Proportion. Determining the Sample Size.
 8. Inferences Based on a Single Sample: Tests of Hypotheses. The Elements of a Test of Hypothesis. LargeSample Test of Hypothesis about a Population Mean. Observed Significance Levels: pValues. SmallSample Test of Hypothesis about a Population Mean. LargeSample Test of Hypothesis about a Population Proportion. Calculating Type II Error Probabilities: More about !b (Optional). Test of Hypothesis about a Population Proportion.
 9. Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses. Comparing Two Population Means: Independent Sampling. Comparing Two Population Means: Paired Difference Experiments. Comparing Two Population Proportions: Independent Sampling. Determining the Sample Size. Comparing Two Population Variances: Independent Sampling (Optional).
 10. Analysis of Variance: Comparing More Than Two Means. Elements of a Designed Experiment. The Completely Randomized Design. Multiple Comparisons of Means. The Randomized Block Design. Factorial Experiments.
 11. Simple Linear Regression. Probabalistic Models. Fitting the Model: The Least Squares Approach. Model Assumptions. An Estimator of !
 s2. Assessing the Utility of the Model: Making Inferences about the Slope !
 b1. The Coefficient of Correlation. The Coefficient of Determination. Using the Model for Estimation and Prediction. A Complete Example.
 12. Multiple Regression and Model Building. Multiple Regression Models. The FirstOrder Model: Estimating and Interpreting the !b Parameters. Model Assumptions. Inferences About the Individual !b Parameters. Checking the Overall Utility of a Model. Using the Model for Estimation and Prediction. Model Building: Interaction Models. Model Building: Quadratic and Other HigherOrder Models. Model Building: Qualitative (Dummy) Variable Models. Model Building: Models with Both Quantitative and Qualitative Variables. Model Building: Comparing Nested Models. Model Building: Stepwise Regression. Residual Analysis: Checking the Regression Assumptions. Some Pitfalls: Estimability, Multicollinearity, and Extrapolation.
 13. Categorical Data Analysis. Categorical Data and the Multinomial Distribution. Testing Categorical Probabilities: OneWay Table. Testing Categorical Probabilities: TwoWay (Contingency) Table. A Word of Caution about ChiSquare Tests.
 14. Nonparametric Statistics. Introduction: DistributionFree Tests. Single Population Inferences: The Sign Test. Comparing Two Populations: The Wilcoxon Rank Sum Test for Independent Samples. Comparing Two Populations: The Wilcoxon Signed Rank Test for the Paired Difference Experiment. The KruskalWallis HTest for a Completely Randomized Design. The Friedman F rTest for a Randomized Block Design. Spearman's Rank Correlation Coefficient. Appendix A. Tables. Random Numbers. Binomial Probabilities. Poisson Probabilities. Normal Curve Areas. Exponentials. Critical Values of t. Critical Values of !
 c2. Percentage Points of the F Distribution, !a=
 .10. Percentage Points of the F Distribution, !a=
 .05. Percentage Points of the F Distribution, !a=
 .025. Percentage Points of the F Distribution, !a=
 .01. Critical Values of TL and TU for the Wilcoxon Rank Sum Test: Independent Samples. Critical Values of TO in the Wilcoxon Paired Difference Signed Rank Test. Critical Values of Spearman's Rank Correlation Coefficient. Appendix B. Data Sets. Coronary Artery Patients' Blood Loss Data. Car & Driver Data. Starting Salaries of USF Graduates. Sealed Milk Bids Data. Federal Trade Commission Rankings of Domestic Cigarette Brands. Appendix C. Calculation Formulas for Analysis of Variance. Short Answers to Selected OddNumbered Exercises. Index.
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QA276.12 .M4 2003  Available 
8. Statistics [2013]
 McClave, James T.
 12th ed.  Boston : Pearson, c2013.
 Description
 Book — xxvi, 814 p. : ill. (some col.) ; 29 cm. + 1 CDROM (4 3/4 in.)
 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 1.6 The Role of Statistics in Critical Thinking Statistics in Action: Social Media Networks and the Millennial Generation Using Technology: Creating 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 Summation Notation 2.4 Numerical Measures of Central Tendency 2.5 Numerical Measures of Variability 2.6 Interpreting the Standard Deviation 2.7 Numerical Measures of Relative Standing 2.8 Methods for Detecting Outliers: Box Plots and zScores 2.9 Graphing Bivariate Relationships (Optional) 2.10 Distorting the Truth with Descriptive Techniques Statistics In Action: Body Image Dissatisfaction: Real or Imagined? Using Technology: 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 Random Sampling 3.8 Some Additional Counting Rules (Optional) 3.9 Bayes' Rule (Optional) Statistics In Action: Lotto Buster! Can You Improve Your Chances of Winning the Lottery? Using Technology: Generating a Random Sample 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: Discrete 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 Development
 Is the Hit Ratio Optimized? Using Technology: Continuous Random Variables, Probabilities, and Normal Probability Plots 6. Sampling Distributions 6.1 What is 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 Statistics in Action: The Insomnia PillWill It Take Less Time to Fall Asleep? Using Technology: 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: 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 Test of Hypothesis About a Population Mean: Normal (z) Statistic 8.4 Observed Significance Levels: pValues 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 ss (Optional) 8.8 Test of Hypothesis About a Population Variance (Optional) Statistics in Action: Diary of a Kleenex UserHow Many Tissues in a Box? Using Technology: Tests of Hypothesis 9. Inferences Based on a 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. vs. Visa USA Inc.A Libel Case Using Technology: TwoSample 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: On the Trail of the Cockroach: Do Roaches Travel at Random? Using Technology: 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 ss1 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: Simple Linear Regression 12. Multiple Regression and Model Building 12.1 Multiple Regression Models 12.2 The FirstOrder Model: Inferences About the Individual ssParameters 12.3 Evaluating the Overall Utility of a Model 12.4 Using the Model for Estimation and Prediction 12.5 Model Building: Interaction Models 12.6 Model Building: Quadratic and other HigherOrder Models 12.7 Model Building: Qualitative (Dummy) Variable Models 12.8 Model Building: Models with both Quantitative and Qualitative Variables 12.9 Model Building: Comparing Nested Models (Optional) 12.10 Model Building: Stepwise Regression (Optional) 12.11 Residual Analysis: Checking the Regression Assumptions 12.12 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation Statistics in Action: Modeling Condo Sales: Are There Differences in Auction Prices? Using Technology: Multiple Regression 13. Categorical Data Analysis 13.1 Categorical Data and the Multinomial Distribution 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: College Students and AlcoholIs Drinking Frequency Related to Amount? Using Technology: ChiSquare Analyses 14. Nonparametric Statistics* 14.1 Introduction: DistributionFree Tests 14.2 Single Population 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 Statistics in Action: How Vulnerable are Wells to Groundwater Contamination? Using Technology: Nonparametric Analyses Appendix A. Tables Table I. Random Numbers Table II. Binomial Probabilities Table III. Poisson Probabilities Table IV. Normal Curve Areas Table V. Exponentials Table VI. Critical Values of t Table VII. Critical Values of x2 Table VIII. Percentage Points of the F Distribution, a=.10 Table IX. Percentage Points of the F Distribution, a=.05 Table X. Percentage Points of the F Distribution, a=.025 Table XI. Percentage Points of the F Distribution, a=.01 Table XII. Critical Values of TL and TU for the Wilcoxon Rank Sum Test Table XIII. Critical Values of T0 in the Wilcoxon Signed Rank Test Table XIV. Critical Values of Spearman's Rank Correlation Coefficient Appendix B. Calculation Formulas for Analysis of Variance Short Answers to Selected OddNumbered Exercises *This chapter is included on the CDROM that comes with the textbook.
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QA276.12 .M4 2013  Available 
9. 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.
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QA276 .M378 2017  Unknown 
10. Statistics [2017]
 McClave, James T., author.
 Thirteenth edition.  Boston : Pearson, [2017]
 Description
 Book — xxviii, 864 pages : illustrations (some color) ; 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.
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Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA276.12 .M4 2017  Unknown 
 Mendenhall, William.
 5th ed.  Upper Saddle River, N.J. : Prentice Hall, c1996.
 Description
 Book — xxi, 899 p. : ill ; 24 cm.
 Summary

 1. Review of Basic Concepts (Optional).
 2. Introduction to Regression Analysis.
 3. Simple Linear Regression.
 4. Multiple Regression.
 5. Model Building.
 6. Some Regression Pitfalls.
 7. Residual Analysis.
 8. Special Topics in Regression (Optional).
 9. Time Series Modeling and Forecasting.
 10. Principles of Experimental Design.
 11. The Analysis of Variance for Designed Experiments. (Note: the following chapters are case studies.)
 12. Modeling the Sale Prices of Residential Properties in Four Neighborhoods.
 13. An Analysis in Rain Levels in California.
 14. Reluctance to Transmit Bad News: The MUM Effect.
 15. An Investigation of Factors Affecting the Sale Price of Condominium Units Sold at Public Auction.
 16. Modeling Daily Peak Electricity Demands. Appendix A: The Mechanics of a Multiple Regression Analysis. Appendix B: A Procedure for Inverting a Matrix. Appendix C: Useful Statistical Tables. Appendix D: SAS Tutorial. Appendix E: SPSS Tutorial. Appendix F: MINITAB Tutorial. Appendix G: ASP Tutorial Data Sets. Answers to OddNumbered Exercises. Index.
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Business Library
Business Library  Status 

Offsite stacks  Request (opens in new tab) 
HF1017 .M46 1996  Available 
12. Statistics for business and economics [2001]
 McClave, James T.
 8th ed.  Upper Saddle River, NJ : Prentice Hall, 2001.
 Description
 Book — xxiii, 1028 p. ; 26 cm.
 Summary

 1. Statistics, Data, and Statistical Thinking. The Science of Statistics. Types of Statistical Applications in Business. Fundamental Elements of Statistics. Processes (Optional). Types of Data. Statistics in Action 1
 .1: Quality Improvement: U.S. Firms Respond to the Challenge from Japan. Collecting Data. The Role of Statistics in Managerial DecisionMaking. Statistics in Action 1
 .2: A 20/20 View of Survey ResultsFact or Fiction?
 2. Methods for Describing Sets of Data. Describing Qualitative Data. Statistics in Action 2
 .1: Pareto Analysis. Graphical Methods for Describing Quantitative Data. Summation Notation. Numerical Measures of Central Tendency. Numerical Measures of Variability. Interpreting the Standard Deviation. Numerical Measures of Relative Standing. Methods for Detecting Outliers (Optional). Graphing Bivariate Relationships. (Optional). The Time Series Plot (Optional). Distorting the Truth with Descriptive Techniques. Statistics in Action 2
 .2: Car and Driver's "Road Test Digest." Real World Case: The Kentucky Milk Case, Part I. (A Case Covering Chapters 1 and
 2.
 3. Probability. Events, Sample Spaces, and Probability. Statistics in Action 3
 .1: Game Show Strategy: To Switch or Not to Switch? Unions and Intersections. Complementary Events. The Additive Rule and Mutually Exclusive Events. Conditional Probability. The Multiplicative Rule and Independent Events. Random Sampling. Statistics in Action 3
 .2: Lottery Buster.
 4. Discrete Random Variables. Two Types of Random Variables. Probability Distributions for Discrete Random Variables. Expected Values of Discrete Random Variables. Statistics in Action 4
 .1: Portfolio Selection. The Binomial Random Variable. Statistics in Action 4
 .2: The Space Shuttle Challenger: Catastrophe in Space. The Poisson Random Variable (Optional).
 5. Continuous Random Variables. Continuous Probability Distributions. The Uniform Distribution (Optional). The Normal Distribution. Statistics in Action 5
 .1: IQ, Economic Mobility, and the Bell Curve. Descriptive Methods for Assessing Normality. Approximating a Binomial Distribution with a Normal Distribution. The Exponential Distribution (Optional). Statistics in Action 5
 .2: Queueing Theory.
 6. Sampling Distributions. The Concept of Sampling Distributions. Properties of Sampling Distributions: Unbiasedness and Minimum Variance (Optional). Statistics in Action 6
 .1: Reducing Investment Risk through Diversification. The Central Limit Theorem. Statistics in Action 6
 .2: The Insomnia Pill. Real World Case: The Furniture Fire Case (A Case Covering Chapters 36).
 7. Inferences Based on a Single Sample: Estimation with Confidence Intervals. LargeSample Confidence Interval for a Population Mean. SmallSample Confidence Interval for a Population Mean. Statistics in Action 7
 .1: Scallops, Sampling, and the Law. LargeSample Confidence Interval for a Population Proportion. Determining the Sample Size. Finite Population Correction for Simple Random Sampling (Optional). Sample Survey Designs (Optional). Statistics in Action 7
 .2: Sampling Error Versus Nonsampling Error.
 8. Inferences Based on a Single Sample: Tests of Hypothesis. The Elements of a Test of Hypothesis. LargeSample Test of Hypothesis about a Population Mean. Statistics in Action 8
 .1: Statistical Quality Control. Observed Significance Levels: pValues. SmallSample Test of Hypothesis about a Population Mean. LargeSample Test of Hypothesis about a Population Proportion. Calculation Type II Error Probabilities: More about ...b (Optional). Test of Hypothesis about a Population Variance (Optional). Statistics in Action 8
 .2: March MadnessHandicapping the NCAA Basketball Tourney.
 9. Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses. Comparing Two Population Means: Independents Sampling. Statistics in Action 9
 .1: The Effect of SelfManaged Work Teams on Family Life. Comparing Two Population Means: Paired Difference Experiments. Comparing Two Population Proportions: Independent Sampling. Determining the Sample Size. Statistics in Action 9
 .2: Unpaid Overtime and the Fair Labor Standards Act. Comparing Two Population Variances: Independent Sampling (Optional). Real World Case: The Kentucky Milk Case, Part II (A Case Covering Chapters 79).
 10. Simple Linear Regression. Probabilistic Models. Fitting the Model: The Least Squares Approach. Model Assumptions. An Estimator of ...
 s2. Assessing the Utility of the Model: Making Inferences about the Slope ...
 b1. The Coefficient of Correlation. The Coefficient of Determination. Using the Model for Estimation and Prediction. Statistics in Action 10
 .1: Statistical Assessment of Damage to Bronx Bricks. Simple Linear Regression: A Complete Example. Statistics in Acton 10
 .2: Can "Dowsers" Really Detect Water?
 11. Multiple Regression and Model Building. Multiple Regression Models. The FirstOrder Model: Estimation and Interpreting the ...bParameters. Model Assumptions. Inferences about the ...b Parameters. Checking the Overall Utility of a Model. Statistics in Action 11
 .1: Prediction the Price of Vintage Red Bordeaux Wine. Using the Model for Estimation and Prediction. Using the Model for Estimation and Prediction. Model Building: Interaction Models. Model Building: Quadratic and Other HigherOrder Models. Model Building: Qualitative (Dummy) Variable Models. Model Building: Models with Both Quantitative and Qualitative Variables (Optional). Model Building: Comparing Nested Models (Optional). Model Building: Stepwise Regression (Optional). Residual Analysis: Checking the Regression Assumptions. Some Pitfalls: Estimability, Multicollinearity, and Extrapolation. Statistics in Action 11
 .2: "Wringing"The Bell Curve.
 12. Methods for Quality Improvement. Quality, Processes, and Systems. Statistics in Action
 12.
 1: Deming's 14 Points. Statistical Control. The Logic of Control Charts. A Control Chart for Monitoring the Mean of a Process: The xChart. A Control Chart for Monitoring the Variation of a Process: The RChart. A Control Chart for Monitoring the Proportion of Defectives Generated by a Process: The pChart. Diagnosing the Causes of Variation (Optional). Statistics in Action 12
 .2: Quality Control in a Service Operation. Capability Analysis (Optional).
 13. Time Series: Descriptive Analyses, Models, and Forecasting. Descriptive Analysis: Index Numbers. Statistics in Action 13
 .1: The Consumer Price Index: CPIU and CPIW. Descriptive Analysis: Exponential Smoothing. Time Series Components. Forecasting: Exponential Smoothing. Forecasting Trends: The HoltWinters Forecasting Model (Optional). Measuring Forecast Accuracy: MAD and RMSE. Forecasting Trends: Simple Linear Regression. Seasonal Regression Models. Statistics in Action 13
 .2: Forecasting the Demand for Emergency Room Services. Autocorrelation and the DurbinWatson Test. Real World Case: The Gasket Manufacturing Case (A Case Covering Chapters 12 and 13).
 14. Design of Experiments and Analysis of Variance. Elements of a Designed Experiment. The Completely Randomized Design: Single Factor. Multiple Comparisons of Means. Statistics in Action 14
 .1: Is Therapy the New Diet Pill for Binge Eaters? Factorial Experiments. Statistics in Action 14
 .2: On the Trail of the Cockroach. Using Regression Analysis for ANOVA (Optional).
 15. Nonparametric Statistics. Introduction: DistributionFree Tests. Single Population Inferences: The Sign Test. Comparing Two Populations: The Wilcoxon Rank Sum Test for Independent Samples. Comparing Two Populations: The Wilcoxon Signed Rank Test for the Paired Difference Experiment. Statistics in Action 15
 .1: Reanalyzing the Scallop Weight Data. The KruskalWallis HTest for a Completely Randomized Design. Statistics in Action 15
 .2: Taxpayers Versus the IRS: Selecting the Trial Court. Spearman's Rank Correlation Coefficient.
 16. Categorical Data Analysis. Categorical Data and the Multinomial Experiment. Testing Category Probabilities: OneWay Table. Testing Category Probabilities: TwoWay (Contingency) Table. Statistics in Action 16
 .1: Ethics in Computer Technology and Use. A Word of Caution about ChiSquare Tests. Real World Case: Discrimination in the Workplace (A Case Covering Chapters 1416). Appendices. Basic Counting Rules. Tables. Calculation Formulas for Analysis of Variance.
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