AP Statistics Curriculum 2007 - Revision history

IvoDinov: Redirecting to Probability and statistics EBook

2008-11-08T02:59:40Z

Redirecting to Probability and statistics EBook

IvoDinov: Made a redirect to EBook

2008-11-08T02:51:41Z

Made a redirect to EBook

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PriscillaChui: /* Chapter VIII: Hypothesis Testing */

2008-05-01T16:35:46Z

‎Chapter VIII: Hypothesis Testing

PriscillaChui: /* Chapter IV: Probability Distributions */

2008-04-28T19:44:07Z

‎Chapter IV: Probability Distributions

PriscillaChui: /* Chapter VII: Point and Interval Estimates */

2008-04-28T19:42:15Z

‎Chapter VII: Point and Interval Estimates

PriscillaChui: /* Chapter VIII: Hypothesis Testing */

2008-04-28T19:40:44Z

‎Chapter VIII: Hypothesis Testing

PriscillaChui: /* Chapter IX: Inferences From Two Samples */

2008-04-28T19:38:22Z

‎Chapter IX: Inferences From Two Samples

PriscillaChui: /* Chapter X: Correlation and Regression */

2008-04-28T19:36:53Z

‎Chapter X: Correlation and Regression

PriscillaChui: /* Chapter XII: Non-Parametric Inference */

2008-04-28T19:34:42Z

‎Chapter XII: Non-Parametric Inference

IvoDinov: Added the EBook Logo Image

2008-04-17T17:46:28Z

Added the EBook Logo Image

← Older revision		Revision as of 02:59, 8 November 2008
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−	#REDIRECT [[EBook]]	+	#REDIRECT [[Probability and statistics EBook]]

@@ Line 154: / Line 154: @@
 ===[[AP_Statistics_Curriculum_2007_Hypothesis_S_Mean |Testing a Claim about a Mean: Small Samples]]===
-We continue with the discussion on inference for the population mean for small smaples.
+We continue with the discussion on inference for the population mean for small samples.
 ===[[AP_Statistics_Curriculum_2007_Hypothesis_Proportion |Testing a Claim about a Proportion]]===
@@ Line 160: / Line 160: @@
 ===[[AP_Statistics_Curriculum_2007_Hypothesis_Var |Testing a Claim about a Standard Deviation or Variance]]===
-The significance testing for the variation or the standard deviation of a process, a natural phenomenon or an experiment is of paramount importance in many fields. This chapter provides the details for formulating testable hypotheses, computation and inference on assessing variation.
+The significance testing for the variation or the standard deviation of a process, a natural phenomenon or an experiment is of paramount importance in many fields. This chapter provides the details for formulating testable hypotheses, computation, and inference on assessing variation.
 ==Chapter IX: Inferences From Two Samples==

@@ Line 78: / Line 78: @@
 ===[[AP_Statistics_Curriculum_2007_Distrib_Binomial |Bernoulli and Binomial Experiments]]===
-The Bernoulli and Binomial processes provide the simplest models for discrete random experiments.
+The '''Bernoulli''' and '''Binomial''' processes provide the simplest models for discrete random experiments.
 ===[[AP_Statistics_Curriculum_2007_Distrib_Multinomial |Multinomial Experiments]]===
-Multinomial processes extend the [[AP_Statistics_Curriculum_2007_Distrib_Binomial |Binomial experiments]] for the situation of multiple possible outcomes.
+'''Multinomial processes''' extend the [[AP_Statistics_Curriculum_2007_Distrib_Binomial |Binomial experiments]] for the situation of multiple possible outcomes.
 ===[[AP_Statistics_Curriculum_2007_Distrib_Dists |Geometric, Hypergeometric and Negative Binomial]]===
-The Geometric, Hypergeometric and Negative Binomial distributions provide computational models for calculating probabilities for a large number of experiment and random variables. This section presents the theoretical foundations and the applications of each of these discrete distributions.
+The '''Geometric, Hypergeometric and Negative Binomial distributions''' provide computational models for calculating probabilities for a large number of experiment and random variables. This section presents the theoretical foundations and the applications of each of these discrete distributions.
 ===[[AP_Statistics_Curriculum_2007_Distrib_Poisson |Poisson Distribution]]===
-The Poisson distribution models many different discrete processes where the probability of the observed phenomenon is constant in time or space. Poisson distribution may be used as an approximation to the Binomial distribution.
+The '''Poisson distribution''' models many different discrete processes where the probability of the observed phenomenon is constant in time or space. Poisson distribution may be used as an approximation to the Binomial distribution.
 ==Chapter V: Normal Probability Distribution==

@@ Line 136: / Line 136: @@
 ===[[AP_Statistics_Curriculum_2007_StudentsT |Student's T distribution]]===
-The Student's T-Distribution arises in the problem of estimating the mean of a normally distributed population when the sample size is small and the population variance is unknown.
+The '''Student's T-Distribution''' arises in the problem of estimating the mean of a normally distributed population when the sample size is small and the population variance is unknown.
 ===[[AP_Statistics_Curriculum_2007_Estim_Proportion |Estimating a Population Proportion]]===
-Normal Distribution is appropriate model for proportions, when the sample size is large enough. In this section we demonstrate how to obtain point and interval estimates for population proportion.
+'''Normal Distribution''' is appropriate model for proportions, when the sample size is large enough. In this section, we demonstrate how to obtain point and interval estimates for population proportion.
 ===[[AP_Statistics_Curriculum_2007_Estim_Var |Estimating a Population Variance]]===

@@ Line 145: / Line 145: @@
 ==Chapter VIII: Hypothesis Testing==
-Hypothesis Testing is a statistical technique for decision making regarding populations or processes based on experimental data. It quantitatively answers the possibility that chance alone might be responsible for the observed discrepancy between a theoretical model and the empirical observations.
+'''Hypothesis Testing''' is a statistical technique for decision making regarding populations or processes based on experimental data. It quantitatively answers the possibility that chance alone might be responsible for the observed discrepancy between a theoretical model and the empirical observations.
 ===[[AP_Statistics_Curriculum_2007_Hypothesis_Basics |Fundamentals of Hypothesis Testing]]===
@@ Line 160: / Line 160: @@
 ===[[AP_Statistics_Curriculum_2007_Hypothesis_Var |Testing a Claim about a Standard Deviation or Variance]]===
-Significance Testing for the variation or the standard deviation of a process, natural phenomenon or an experiment is of paramount importance in many fields. This chapter provides the details for formulating testable hypotheses, computation and inference on assessing variation.
+The significance testing for the variation or the standard deviation of a process, a natural phenomenon or an experiment is of paramount importance in many fields. This chapter provides the details for formulating testable hypotheses, computation and inference on assessing variation.
 ==Chapter IX: Inferences From Two Samples==

@@ Line 169: / Line 169: @@
 ===[[AP_Statistics_Curriculum_2007_Infer_2Means_Indep |Inferences About Two Means: Independent Samples]]===
-Independent Samples designs refer to experiments or observations where all measurements are individually independent from each other within their groups and the groups are independent. In this section, we discuss inference based on independent samples.
+'''Independent''' Samples designs refer to experiments or observations where all measurements are individually independent from each other within their groups and the groups are independent. In this section, we discuss inference based on independent samples.
 ===[[AP_Statistics_Curriculum_2007_Infer_BiVar |Comparing Two Variances]]===
-In this section, we compare variances (or standard deviations) of two populations using randomly sampled data.
+In this section, we compare '''variances (or standard deviations)''' of two populations using randomly sampled data.
 ===[[AP_Statistics_Curriculum_2007_Infer_2Proportions |Inferences about Two Proportions]]===
-This section presents the significance testing and inference on equality of proportions from two independent populations.
+This section presents the '''significance testing''' and '''inference on equality''' of proportions from two independent populations.
 ==Chapter X: Correlation and Regression==

@@ Line 181: / Line 181: @@
 ===[[AP_Statistics_Curriculum_2007_GLM_Corr |Correlation]]===
-The correlation between X and Y represents the first bivariate model of association which may be used to make predictions.
+The '''Correlation''' between X and Y represents the first bivariate model of association which may be used to make predictions.
 ===[[AP_Statistics_Curriculum_2007_GLM_Regress |Regression]]===
-We are now ready to discuss the modeling of linear relations between two variables using regression analysis. This section demonstrates this methodology for the SOCR California Earthquake dataset.
+We are now ready to discuss the modeling of linear relations between two variables using '''Regression Analysis'''. This section demonstrates this methodology for the SOCR California Earthquake dataset.
 ===[[AP_Statistics_Curriculum_2007_GLM_Predict |Variation and Prediction Intervals]]===
@@ Line 190: / Line 190: @@
 ===[[AP_Statistics_Curriculum_2007_GLM_MultLin |Multiple Regression]]===
-Now we are interested in determining linear regressions, multilinear models, of the relationships between one dependent variable Y and many independent variables <math>X_i</math>.
+Now, we are interested in determining linear regressions and multilinear models of the relationships between one dependent variable Y and many independent variables <math>X_i</math>.
 ==Chapter XI: Analysis of Variance (ANOVA)==

@@ Line 206: / Line 206: @@
 ===[[AP_Statistics_Curriculum_2007_NonParam_2MedianIndep | Differences of Medians (Centers) of Two Independent Samples]]===
-The Wilcoxon-Mann-Whitney (WMW) Test (also known as Mann-Whitney U Test, Mann-Whitney-Wilcoxon Test, or Wilcoxon rank-sum Test) is a ''non-parametric'' test for assessing whether two samples come from the same distribution.
+The '''Wilcoxon-Mann-Whitney (WMW) Test''' (also known as Mann-Whitney U Test, Mann-Whitney-Wilcoxon Test, or Wilcoxon rank-sum Test) is a ''non-parametric'' test for assessing whether two samples come from the same distribution.
 ===[[AP_Statistics_Curriculum_2007_NonParam_2PropIndep | Differences of Proportions of Two Samples]]===
@@ Line 215: / Line 215: @@
 ===[[AP_Statistics_Curriculum_2007_NonParam_VarIndep | Differences of Variances of Independent Samples (Variance Homogeneity)]]===
-There are several tests for variance equality in k samples. These tests are commonly known as tests for homogeneity of variances.
+There are several tests for variance equality in ''k'' samples. These tests are commonly known as tests for '''Homogeneity of Variances'''.
 ==Chapter XIII: Multinomial Experiments and Contingency Tables==

← Older revision		Revision as of 17:46, 17 April 2008
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−	This is a General Advanced-Placement (AP) ~~Statistics Curriculum E-Book~~	+	This is a General Statistics Curriculum E-Book, including Advanced-Placement (AP) materials.
		+	[[Image:EBook_Logo.jpg\|150px\|thumbnail\|right\| [[EBook]] ]]

	==[[AP_Statistics_Curriculum_2007_Preface\| Preface]]==		==[[AP_Statistics_Curriculum_2007_Preface\| Preface]]==