Introductory statistics

2.2 1.1: definitions of statistics, probability, and key terms
2.3 1.2: data, sampling, and variation in data and sampling
2.4 1.3: frequency, frequency tables, and levels of measurement
2.5 1.4: experimental design and ethics
2.6 2.1: stem-and leaf graphs (stemplots), line graphs, and bar graphs
2.7 2.2: histograms, frequency polygons, and time series graphs
2.8 2.3: measures of the location of the data
2.9 2.4: box plots
2.10 2.5: measures of the center of the data
2.11 2.6: skewness and the mean, median, and mode
2.12 2.7: measures of the spread of the data
2.13 3.1: terminology
2.14 3.2: independent and mutually exclusive events
2.15 3.3: two basic rules of probability
2.16 3.4: contingency tables
2.17 3.5: tree and venn diagrams
2.19 1.1: definitions of statistics, probability, and key terms
2.20 1.2: data, sampling, and variation in data and sampling
2.21 1.3: frequency, frequency tables, and levels of measurement
2.22 1.4: experimental design and ethics
2.23 2.1: stem-and leaf graphs (stemplots), line graphs, and bar graphs
2.24 2.2: histograms, frequency polygons, and time series graphs
2.25 2.3: measures of the location of the data
2.26 2.4: box plots
2.27 2.5: measures of the center of the data
2.28 2.6: skewness and the mean, median, and mode
2.29 2.7: measures of the spread of the data
2.30 3.1: terminology
2.31 3.2: independent and mutually exclusive events
2.32 3.3: two basic rules of probability
2.33 3.4: contingency tables
2.34 3.5: tree and venn diagrams
2.36 4.1: probability distribution function (pdf) for a discrete random
2.37 4.2: mean or expected value and standard deviation
2.38 4.3: binomial distribution
2.39 4.4: geometric distribution
2.40 4.5: hypergeometric distribution
2.41 4.6: poisson distribution
2.42 5.1: continuous probability functions
2.43 5.2: the uniform distribution
2.44 5.3: the exponential distribution
2.45 6.1: the standard normal distribution
2.46 6.2: using the normal distribution
2.47 7.1: the central limit theorem for sample means (averages)
2.48 7.2: the central limit theorem for sums
2.49 7.3: using the central limit theorem
2.51 Probability distribution function (pdf) for a discrete random variable
2.54 4.4: geometric distribution
2.55 4.5: hypergeometric distribution
2.56 4.6: poisson distribution
2.57 5.1: continuous probability functions
2.58 5.2: the uniform distribution
2.59 5.3: the exponential distribution
2.60 6.1: the standard normal distribution
2.61 6.2: using the normal distribution
2.62 7.1: the central limit theorem for sample means (averages)
2.63 7.2: the central limit theorem for sums
2.64 7.3: using the central limit theorem
2.66 8.1: confidence interval, single population mean, population standard
2.67 8.2: confidence interval, single population mean, standard deviation
2.68 8.3: confidence interval for a population proportion
2.69 9.1: null and alternate hypotheses
2.70 9.2: outcomes and the type i and type ii errors
2.71 9.3: distribution needed for hypothesis testing
2.72 9.4: rare events, the sample, decision, and conclusion
2.73 9.5: additional information and full hypothesis test examples
2.74 10.1: comparing two independent population means with unknown population
2.75 10.2: comparing two independent population means with known population
2.76 10.3: comparing two independent population proportions
2.77 10.4: matched or paired samples
2.78 11.1: facts about the chi-square distribution
2.79 11.2: goodness-of-fit test
2.80 11.3: test of independence
2.81 11.4: test of homogeneity
2.82 11.5: comparison summary of the chi-square tests: goodness-of-fit,
2.83 11.6: test of a single variance
2.85 8.1: confidence interval, single population mean, population standard
2.86 8.2: confidence interval, single population mean, standard deviation
2.87 8.3: confidence interval for a population proportion
2.88 9.1: null and alternate hypotheses
2.89 9.2: outcomes and the type i and type ii errors
2.90 9.3: distribution needed for hypothesis testing
2.91 9.4: rare events, the sample, decision, and conclusion
2.92 9.5: additional information and full hypothesis test examples
2.93 10.1: comparing two independent population means with unknown population
2.94 10.2: comparing two independent population means with known population
2.95 10.3: comparing two independent population proportions
2.96 10.4: matched or paired samples
2.97 11.1: facts about the chi-square distribution
2.98 11.2: goodness-of-fit test
2.99 11.3: test of independence
2.100 11.4: test of homogeneity
2.101 11.5: comparison summary of the chi-square tests: goodness-of-fit,
2.102 11.6: test of a single variance
2.105 12.2: slope and Y -intercept of a linear equation
2.106 12.3: scatter plots
2.107 12.4: the regression equation
2.108 12.5: correlation coefficient and coefficient of determination
2.109 12.6: testing the significance of the correlation coefficient
2.110 12.7: prediction
2.111 12.8: outliers
2.112 13.1: one-way anova
2.113 13.2: the F Distribution
2.114 13.3: facts about the F Distribution
2.115 13.4: test of two variances
2.118 12.2: slope and y-intercept of a linear equation
2.119 12.3: scatter plots
2.120 12.4: the regression equation
2.121 12.5: correlation coefficient and coefficient of determination
2.122 12.6: testing the significance of the correlation coefficient
2.123 12.7: prediction
2.124 12.8: outliers
2.125 13.1: one-way anova
2.126 13.2: the F Distribution
2.127 13.3: facts about the F Distribution
2.128 13.4: test of two variances
4.8 Part i: sampling
4.9 Part ii: possible distributions
4.10 Part iii: clt experiments
4.12 Hypothesis testing-article
4.16 Bivariate data, linear regression, and univariate data
4.19 Part i: bivariate data
4.20 Part ii: univariate data
7.6 Ti-83, 83+, 84, 84+ instructions for distributions and tests
2.2 Percentages that add to more (or less) than 100%
2.4 Pie charts: no missing data
3.2 A formula for finding the percentile of a value in a data set
3.3 Interpreting percentiles, quartiles, and median
5.2 Sampling distributions and statistic of a sampling distribution
7.9 Standard deviation of grouped frequency tables
3.1 Notation for the binomial: B = binomial probability distribution function
4.1 Notation for the geometric: g = geometric probability distribution
6.1 Notation for the poisson: p = poisson probability distribution function
1.1 Z -scores
1.3 Finding the z -score for the stated confidence level
1.4 Calculating the error bound ( EBM )
1.7 Changing the confidence level or sample size
3.1 “plus four” confidence interval for p
1.1 Slope and Y -intercept of a linear equation
4.2 Method 1: using a p -value to make a decision
4.3 Method 2: using a table of critical values to make a decision
4.4 Third-exam vs final-exam example: critical value method
4.5 Assumptions in testing the significance of the correlation coefficient
6.4 Numerical identification of outliers: calculating s And finding outliers
6.5 95% critical values of the sample correlation coefficient table

Source:  OpenStax, Introductory statistics. OpenStax CNX. May 06, 2016 Download for free at http://legacy.cnx.org/content/col11562/1.18
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