List of Figures

Chapter 3: Summarizing Data

Figure 3.1: A typical histogram showing normality.

Figure 3.2: A typical histogram showing a positive skew distribution.

Figure 3.3: A typical histogram showing a negative skew distribution.

Figure 3.4: A typical histogram showing a bimodal distribution.

Chapter 4: Working with the Normal Distribution

Figure 4.1: The normal distribution.

Figure 4.2: Comparison of actual data with a superimposed distribution curve.

Figure 4.3: The sampling distribution of means.

Figure 4.4: The sample mean 1.5 standard errors above the population mean.

Figure 4.5: The sample mean 1 standard error below the population mean.

Figure 4.6: The distribution of mean for sample size 25.

Chapter 5: Testing Hypotheses About Two Independent Means

Figure 5.1: Theoretical distribution of differences of means.

Chapter 6: Testing Hypotheses About Two Dependent Means

Figure 6.1: Impact of sample size on power for various alpha levels (.01, .05, .10).

Chapter 7: Comparing Several Means

Figure 7.1: Typical graphical analysis of residuals.

Chapter 8: Measuring Association

Figure 8.1: Five types of relationships.

Figure 8.2: A relationship with points scattered around a straight line.

Figure 8.3: Scatterplot matrix of metric variables .

Figure 8.4: Strong relationship but very low correlation.

Chapter 9: Calculating Regression Lines

Figure 9.1: Regression assumptions.

Figure 9.2: Scatterplot with possible linear fit superimposed.

Figure 9.3: Fitted values and residuals.

Figure 9.4: Typical residuals in a standardized format.

Figure 9.5: Outlier with large residual .

Figure 9.6: Outlier that tilts the regression line.

Figure 9.7: Outliers outside pattern of explanatory variables.

Figure 9.8: Graphical illustration of two- group discriminant analysis.

Figure 9.9: Optimal cutting score with equal sample sizes.

Figure 9.10: Optimal cutting score with unequal sample sizes.

Figure 9.11: Territorial map and rotated discriminant Z scores.

Figure 9.12: Graphical portrayals of the hierarchical clustering process (a) nested groupings, (b) dendogram.

Chapter 10: Common Miscellaneous Statistical Tests

Figure 10.1: t Distributions with 1, 8, and 25 df.

Figure 10.2: The t and standard normal distributions.

Chapter 11: Advanced Topics in Statistics

Figure 11.1: Univariate representation of discriminant Z scores.

Figure 11.2: Normal probability plots and corresponding univariate distributions.

Figure 11.3: Scatterplots of homoscedastic and heteroscedastic relationships.

Figure 11.4: A typical comparison of side-by-side boxplots .

Figure 11.5: Representing nonlinear relationships with polynomials .

Figure 11.6: Proportions of unique and shared variance by levels of multicollinearity.

Chapter 12: Time Series and Forecasting

Figure 12.1: Time series plots.

Figure 12.2: Lags and autocorrelation for product X (sales).

Figure 12.3: A typical correlogram.

Chapter 14: Set Theory

Figure 14.1: Parallel components .

Chapter 16: Discrete and Continuous Random Variables

Figure 16.1: Discrete probability density function.

Figure 16.2: A bar chart and a histogram of two tosses of a coin.

Figure 16.3: Cumulative distribution of two tosses of a coin.

Figure 16.4: Probability density function.

Figure 16.5: Cumulative probability function.

Figure 16.6: The probability (left) and cumulative (right) functions.

Figure 16.7: The normal distribution.

Figure 16.8: Uniform probability density for a die.

Figure 16.9: A generic uniform distribution.

Figure 16.10: A comparison of the uniform distribution and its C.D.F.

Figure 16.11: The sound level in a room.

Figure 16.12: A typical normal curve.

Figure 16.13: Probability density function for random variable x.

Figure 16.14: Probability density function with different means and same standard deviation.

Figure 16.15: Probability density with different means and/or standard deviation.

Figure 16.16: Cumulative distribution function.

Figure 16.17: Standardized and unstandardized normal function (a) unstandardized distribution, (b) standardized distribution.

Figure 16.18: Cumulative distribution function area of interval.

Figure 16.19: Tabulated cumulative distribution function leading tail.

Figure 16.20: Tabulated cumulative distribution function area of specific interval.

Figure 16.21: Standardized normal distribution with trailing tail.

Figure 16.22: Electronic components in a symmetrical format of the distribution.

Figure 16.23: Area of interval cumulative distribution function.

Figure 16.24: The graphical progression in figuring out the components of "meeting specifications."

Figure 16.25: Percent area under the SND curve.

Figure 16.26: A typical binomial distribution.

Figure 16.27: Normal distribution approximation .

Figure 16.28: Mean of the means.

Figure 16.29: Binomial distribution histogram six tosses of a coin.

Figure 16.30: Histogram in percent of B(x;n,p).

Figure 16.31: Binomial distribution for square rod.

Figure 16.32: Poisson distribution for the four failures.

Appendix B: The Simplex Method in Two Dimensions

Figure B.1: A typical geometry of linear programming.

Figure B.2: The simplex notation for corner points.

Appendix E: Optimization

Figure E.1: Strategies for R and C.

Appendix H: Monte Carlo Simulation

Figure H.1: Monte Carlo simulation.