Algebra level symbolic math

  • This journal article describes a set of experiments in which different methods of teaching Bayes' Theorem were compared to each other. The frequency representation of the rule was found to be easier to learn than the probability representation.
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  • This applet plots the survival function (1-F(t)) of the exponential distribution against the empirical survival function. The empirical survival function is one minus the empirical distribution function.

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  • This page contains course notes and homework assignments with solutions for a mathematical statistics class. The course covers statistical inference, probability, and estimation principles.
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  • This Electronic Statistics Textbook offers training in the understanding and application of statistics ... and covers a wide variety of applications, including laboratory research (biomedical, agricultural, etc.), business statistics and forecasting, social science statistics and survey research, data mining, engineering and quality control applications, and many others. Quoted from the index page of the text.
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  • This site provides definitions and examples for the following topics: Graphical displays (stemplots, histograms, boxplots, scatterplots), Numerical Summaries (mean, median, quantiles, variance, standard deviation), Normal Distributions (assessing normality, normal probability plots), Categorical Data (two-way tables, bar graphs, segmented bar graphs), Linear regression (least-squares, residuals, outliers and influential observations, extrapolation), Correlation (correlation coefficient, rŒ_), Inference in Linear Regression (confidence intervals for intercept and slope, significance tests, mean response and prediction intervals), Multiple Linear Regression (confidence intervals, tests of significance, squared multiple correlation), ANOVA for Regression (analysis of variance calculations for simple and multiple regression, F statistics), Experimental Design (experimentation, control, randomization, replication), Sampling (simple, stratified, and multistage random sampling), Sampling in Statistical Inference (sampling distributions, bias, variability), Probability Models (components of probability models, basic rules of probability), Conditional Probability (probabilities of intersections of events, Bayes' formula), Random variables (discrete, continuous, density function), Mean and Variance of Random Variables (definitions, properties), Binomial Distributions (counts, proportions, normal approximation), Sample Means (mean, variance, distribution, Central Limit Theorem), Confidence Intervals (inference about population mean, z and t critical values), Tests of Significance (null and alternative hypotheses for population mean, one-sided and two-sided z and t tests, levels of significance, matched pairs analysis), Comparison of Two Means (confidence intervals and significance tests, z and t statistics, pooled t procedures), Inference for Categorical Data (confidence intervals and significance tests for a single proportion, comparison of two proportions), Chi-square Goodness of Fit Test (chi-square test statistics, tests for discrete and continuous distributions), Two-Way tables and the Chi-Square test (categorical data analysis for two variables, tests of association).
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  • This site gives a definition and an example of stem and leaf plots.
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  • This site gives a definition and an example of histograms.
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  • This site give a definition and an example of boxplots. Topics include outliers, medians, as well as lower and upper quartiles.
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  • This site gives a definition and an example of scatterplots. Topics include positive and negative association.
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  • This site gives a definition and an example of numerical summaries. Topics include mean, median, quantiles, variance, and standard deviation.
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