We covered Ch. 6-6 Assessing Normality including histograms, outliers, normal quantile plot, normal distribution, non-normal distribution, normal, uniform, and skewed, 6-7 Normal Distribution as Approximation to Binomial including binomial distributions, continuity correction for normal/continuous and binomial/discrete distributions, 7-1 Estimates and Sample Sizes, 7-2 Estimating Population Proportion including point estimate, sample proportion, confidence interval, sample size, interpreting a confidence interval, and critical values..
We covered Ch. 6-3 Applications of the Normal Distribution including population mean, population standard deviation, sample mean, and z-score, 6-4 Sampling Distributions and Estimations including sampling means, sampling variances, sampling proportions, and skewness, and 6-5 The Central Limit Theorem including the sample size n>30 and the original population can have any distribution (uniform, skew, etc.) and the sample mean x-bar can be approximated by a normal distribution if n>30 with mean mu and standard deviation equal sigma divided by the square root of n.
We covered Ch. 5-4 Binomial Distribution including mean, variance, standard deviation, minimum range, and maximum range, and 5-5 Poisson Probability Distribution including its characteristics random, independent, uniform, interval, 6-1 Normal Distribution, and 6-2 Standard Normal Distribution including bell shape, mean=0, SD=1 and z-score.
We covered Ch. 5-2 Probability Distributions including expected value, 5-3 Binomial Probability Distrubutions, and 5-4 Parameters of Binomial Distributions including mean, variance, and standard deviation and their formulas.
We covered Ch. 5-1 Review and Preview of Discrete Probability Distributions, 5-2 Probability Distributions, and 5-3 Binomial Probability Distributions.
We covered and looked at problems in Ch. 4-6 Counting including the Factorial Rule, The Fundamental Counting Rule, the Permutations Rule when all items are different, the Permutations Rule when some items are identical, and the Combinations Rule. Ch. 5-1 Discrete Probabilities and Preview including using real sample data, using probabilities to find expected results, and probability distributions.
We reviewed Ch. 4-4 Multiplication Rule: Basics, 4-5 Multiplication Rule: Complements and Conditional Probability. We covered Ch. 4-6 Counting including permutations, combinations, factorials, the Fundamental Counting Rule, the Factorial Rule, the Permutations Rule (All different), the Permutations Rule (Some the same), and the Combinations Rule.
We covered Ch. 4-4 Multiplication Rule: Basics including independence and dependence and Ch. 4-5 Multiplication Rule: Complements and Conditional Probability including probability of at least one.
We reviewed Ch. 3 Statistics for Describing and Comparing Data and 4 Probability including rule of complementary events, formal addition rule, compound event, actual odds in favor, actual odds against, pay of odds, probability distribution, unlikely, unusually low number, unusually high number, complement, law of large numbers, event, simple event, sample space, rare event rule for inferential statistics; 5-number summary, quartiles, percentiles, z-score (standard value), unbiased estimator, coefficients of variation, Chebyshev's Theorem, empirical rule, standard deviation, mean absolute deviation, unbiased estimator, variance, range, weighted mean, mean, median, midrange, mode, measures of center.
