Description: Volume 1. Chapter 1: A First Look at Statistics and MINITAB Why Study Statistics? What is Statistics? An Introduction to MINITAB. Chapter 2: Describing Data: Graphical and Numerical Methods Graphical Methods for Quantitative Data: Frequency Distributions and Histograms. Graphical Methods for Quantitative Data: Dotplots and Stem-and-Leaf Displays. Graphical Methods for Qualitative Data: Bar and Pie Charts. Numerical Measures of Central Location. Numerical Measures of Variability. Understanding the Significance of Standard Deviation. Measures of Relative Position. Chapter 3: Modeling Bivariate Data: Describing the Relationship Between Two Variables Straight Lines and the SS( ) Notation. Scatter Diagrams. Linear Regression and the Method of Least Squares. Correlation: Measuring the Usefulness of the Model. Chapter 4: Probability: Measuring Uncertainty Probability, Sample Spaces and Events. Basic Techniques for Counting Sample Points. The Addition Rule of Probability. The Multiplication and Conditional Rules of Probability. The Complement Rule of Probability. Combining the Rules of Probability. Chapter 5: Random Variables and Discrete Probability Random Variables. Discrete Probability Distributions. The Mean of a Discrete Random Variable. Measuring the Variability of a Random Variable. Chapter 6: Special Discrete Probability Distributions Binomial Experiments. The Binomial Probability Distribution. The Mean and the Standard Deviation of a Binomial Random Variable. The Hypergeometric Probability Distribution (Optional). The Poisson Probability Distribution (Optional). Chapter 7: Normal Probability Distributions Probability Distributions of Continuous Random Variables. Normal Probability Distributions. Applications of Normal Distributions. Using Normal Distributions to Approximate Binomial Distributions. Chapter 8: Sampling Methods and Sampling Distributions Random Sampling. Other Scientific Sampling Methods. Sampling Distributions. The Central Limit Theorem. Applications of the Central Limit Theorem. Chapter 9: Estimating Means, Proportions and Variances: Single Sample Basic Concepts of Estimating Population Parameters. Confidence Interval for a Mean: Large Sample Student''s Probability Distributions. Confidence Interval for a Mean: Small Sample. Confidence Interval for a Proportion: Large Sample. Determining the Required Sample Size. Chi-Square Probability Distributions Confidence Interval for a Variance. Chapter 10: Tests of Hypotheses: Single Sample Basic Concepts of Testing Statistical Hypotheses. Hypothesis Test for a Mean: Large Sample. Hypothesis Test for a Mean: Small Sample. Hypothesis Test for a Proportion: Large Sample. Hypothesis Test for a Variance. Using p-Values to Report Test Results. Chapter 11: Tests of Hypotheses and Estimation: Two Samples Large-Sample Inferences for Two Means: Independent Samples. Small-Sample Inferences for Two Means: Independent Samples and Equal Variances. Small-Sample Inferences for Two Means: Independent Samples and Unequal Variances (Optional). Inferences for Two Means: Paired Samples. Large Sample Inferences for Two Proportions. Determining Required Sample Sizes. F Probability Distributions. Inferences for Two Variances. Chapter 12: Chi-Square Tests for Analyzing Count Data Count Data and the Chi-Square Statistic. Hypothesis Test for a Multinomial Experiment. Hypothesis Test for a Contingency Table. Hypothesis Test for Two or More Proportions. Volume II. Chapter 13: Analysis of Variance The Analysis of Variance Technique. The Analysis of Variance Table and Computing Formulas. Applications of the One-Way Analysis of Variance. Estimation of Means. Randomized Block Designs and the Analysis of Variance. Chapter 14: Linear Regression Analysis The Simple Linear Model and Related Assumptions. Fitting the Model by the Method of Least Squares. Inferences for the Slope to Assess the Usefulness of the Model. The Coefficients of Correlation and Determination to Measure the Usefulness of the Model. Us

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