A First Course in Statistics
6th Edition
Published on 24. June 1997
Book
Hardback
519 pages
978-0-13-579277-3 (ISBN)
Description
For the one semester general statistics course, this text has been revised to emphasize statistical thinking. Topics of data collection including observations, experiments, and surveys are introduced in Chapter 1. Over 60% of examples and exercises have been updated. Cases are all new and stress evaluation of statistical studies. Clear explanations, detailed examples.
More details
Edition
6th edition
Language
English
Place of publication
United States
Publishing group
Pearson Education (US)
Target group
Professional and scholarly
Dimensions
Height: 260 mm
Width: 210 mm
Thickness: 23 mm
Weight
1139 gr
ISBN-13
978-0-13-579277-3 (9780135792773)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Other editions
Previous edition
James T. McClave | Frank H. Dietrich | Terry T. Sincich
A First Course in Statistics
Book
04/1995
5th Edition
Prentice Hall
€59.60
Article exhausted; check for reprint
Content
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.
Large-Sample Confidence Interval for a Population Mean. Small- Sample Confidence Interval for a Population Mean. Large-Sample 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. Large-Sample Test of Hypothesis About a Population Mean. Observed Significance Levels: p- Values. Small-Sample Test of Hypothesis About a Population Mean. Large-Sample 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.
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.
Large-Sample Confidence Interval for a Population Mean. Small- Sample Confidence Interval for a Population Mean. Large-Sample 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. Large-Sample Test of Hypothesis About a Population Mean. Observed Significance Levels: p- Values. Small-Sample Test of Hypothesis About a Population Mean. Large-Sample 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.