AP Statistics

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Select the number of each type of objective: (Selecting Random will randomly generate all subtypes) Click on any title to see the free sample worksheet. (only the first few samples are free) (Random) 1.1 Introducing Statistics: What Can We Learn from Data? a. Descriptive vs. Inferential b. Qualitative vs. Quantitative c. Sample vs. Population d. Gathering Data Vocab (Data/Datum/Population/Sample) (Random) 1.2 Variables a. Parameter vs. Statistic b. Statistical Terms Given Situations (Random) 1.3 Tabular Representation and Summary Statistics for One Categorical Variable a. Measuring Mean for Frequency Distributions b. Measuring Standard Deviation for Frequency Distributions c. Frequency and Relative Frequency Graphs (Random) 1.4 Graphical Representations for One Categorical Variable a. Circle Graphs (Pie Charts) b. Bar Graphs (Random) 1.5 Graphical Representations for One Quantitative Variable a. Dotplots b. Stem and Leaf Display c. Histograms (Random) 1.6 Descriptions for One Quantitative Variable Distributions a. Center and Spread b. Clusters and Gaps c. Outliers and other Unusual Features d. Shape e. Situational Problems (Random) 1.7 Summary Statistics for One Quantitative Variable a. Measuring Center: Median, Mean b. Measuring Spread: Range, Interquartile Range, Standard Deviation c. Measuring Position: Quartiles, Percentiles, Standardized scores (z-scores) d. The Effect of Changing Units on Summary Measures e. Changing Units of Measurement for Mean and Median (Random) 1.8 Graphical Representations of Summary Statistics for One Quantitative Variable a. Using Boxplots (Random) 1.9 Comparisons of the Distributions for One Quantitative Variable a. Comparing Center and Spread b. Comparing Clusters and Gaps c. Comparing Outliers and other Unusual Features d. Comparing Shapes (Random) 1.10 The Investigative Question Revisited and Data Collection a. Census b. Sample Survey c. Experiment d. Observational Study (Random) 1.11 Random Sampling a. Sampling methods, including Simple Random Sampling, Stratified Random Sampling, and Cluster Sampling (Random) 1.12 Potential Problems with Sampling a. Characteristics of Well-Designed and Well-Conducted surveys b. Populations, Samples, and Random Selection c. Sources of bias in Sampling and Surveys (Random) 1.13 Experimental Design a. Characteristics of Well-Designed and Well-Conducted Experiments b. Treatments, Control Groups, Experimental Units, Random Assignments, and Replication c. Sources of Bias and Confounding, including Placebo Effect and Blinding d. Completely Random Design e. Randomized Block Design, Including Matched Pairs Design (Random) 2.1 Tabular and Graphical Representations for the Distributions of Two Categorical Variables a. Contingency Tables b. Comparing Distributions Using Bar Charts c. Interpreting Probability, including Long-Run Relative Frequency Interpretation (Random) 2.2 Summary Statistics for Two Categorical Variables a. Marginal and Joint Frequencies for two-way Tables b. Conditional Relative Frequencies and Association (and Simpson's Paradox) (Random) 2.3 Estimating Probabilities Using Simulation a. "Law of Large Numbers" concept b. Simulation of Random Behavior and Probability Distributions (Random) 2.4 Introduction to Probability a. Probability Distributions b. Applying Uniform Distributions for Probability c. Finding Percentiles in a Uniform Distribution (Random) 2.5 Mutually Exclusive Events a. Mutually Exclusive and Basic Probabilities (Random) 2.6 Conditional Probability a. Finding Probability of Dependent Events (Random) 2.7 Independent Events and Unions of Events a. Finding Probability of Independent Events (Random) 2.8 Introduction to Random Variables and Probability Distributions a. Discrete vs. Continuous Random Variables (Random) 2.9 Parameters of Random Variables a. Finding Mean and Standard deviation of a Uniform Distribution b. Mean of a Discrete Random Variable c. Variance and Standard Deviation of a Discrete Random Variable	(Random) 2.10 The Binomial Distribution a. Definition of a Binomial Probability Distribution b. Mean and Standard Deviation of Binomial Distributions (Random) 2.11 The Normal Distribution a. Properties of Normal and Standard Normal Distributions b. Z-Scores c. Using Tables of the Normal Distribution (given z-score) d. Using Tables of the Normal Distribution (given a sample piece of data) e. Using Tables of the Normal Distribution (given probability) f. Finding z(_probability) g. The Normal Distribution as a Model for Measures h. Empirical Rule (Random) 2.12 Sampling Distributions and the Central Limit Theorem a. Sampling Distributions b. Central Limit Theorem (Random) 3.1 Estimators a. Estimating Population Parameters and Margins of Error b. Properties of Point Estimators, including Unbiased and Variability (Random) 3.2 Sampling Distributions for Sample Proportions a. Sampling Distribution of a Sample Proportion (Random) 3.3 Constructing a Confidence Interval for a Population Proportion a. Large Sample Confidence Interval for a Proportion (Random) 3.4 Justifying a Claim Based on a Confidence Interval for a Population Proportion a. Logic of Confidence Intervals, Meaning of Confidence Level and Confidence Intervals, and Properties of Confidence Intervals. (Random) 3.5 Setting Up a Test for a Population Proportion a. Setting up a Sample test for a Proportion (Random) 3.6 p-Values a. p-value of a Hypothesis test for a population proportion (Random) 3.7 Carrying Out a Test for a Population Proportion a. Large Sample Test for a Proportion (Random) 3.8 Potential Errors When Performing Tests a. Type I Vs. Type II Error (Random) 3.9 Sampling Distributions for the Difference Between Sample Proportions a. Sampling Distribution of a difference between two Independent Sample Proportions b. Sampling Distribution for the Difference between two Proportions (Random) 3.10 Constructing a Confidence Interval for the Difference Between Two Population Proportions a. Large Sample Confidence Interval for a difference between two Proportions (Random) 3.11 Justifying a Claim Based on a Confidence Interval for the Difference Between Two Population Proportions a. Justifying from a Confidence Interval for the Difference Between Two Population Proportions (Random) 3.12 Setting Up a Test for the Difference Between Two Population Proportions a. Setting up a Test for the Difference Between two Proportions (Random) 3.13 Carrying Out a Test for the Difference Between Two Population Proportions a. Large Sample Test for a Difference between two Proportions (Random) 3.14 Setting Up a Chi-Square Test for Homogeneity or Independence a. Chi-Square Distribution (Random) 3.15 Carrying Out a Chi-Square Test for Homogeneity or Independence a. Chi-Square test for Goodness of Fit, homogeneity of proportions, and independence (one- and two-way tables) (Random) 4.1 Sampling Distributions for Sample Means a. Sampling Distribution of a Sample Mean (Random) 4.2 Constructing a Confidence Interval for a Population Mean or Population Mean Difference a. t-Distribution b. Confidence Interval for a Mean (Random) 4.3 Justifying a Claim Based on a Confidence Interval for a Population Mean or Population Mean Difference a. Finding a Confidence Interval for μ (σ known) b. Confidence Interval for a Paired difference Between two Means (Random) 4.4 Setting Up a Test for a Population Mean or Population Mean Difference a. Determining Sample Size needed b. Hypothesis Testing Vocabulary c. Steps for doing a Hypothesis Test (Random) 4.5 Carrying Out a Test for a Population Mean or Population Mean Difference a. Doing a Hypothesis Test of μ (σ known) b. Test for a Mean (Random) 4.6 Sampling Distributions for the Difference Between Two Sample Means a. Sampling Distribution for the Difference between two Means (Random) 4.7 Constructing a Confidence Interval for the Difference Between Two Population Means a. Confidence Interval for a unpaired difference Between two Means (Random) 4.8 Justifying a Claim Based on a Confidence Interval for the Difference Between Two Population Means a. Justifying a Claim based on a Confidence Interval for the Difference Betwen two Population Means (Random) 4.9 Setting Up a Test for the Difference Between Two Population Means a. Setting Up a Test for the Difference Between Two Population Means (Random) 4.10 Carrying Out a Test for the Difference Between Two Population Means a. Test for a Difference between two Means (paired and unpaired) b. Testing for the Difference Between Two Population Means (Random) 5.1 Graphical Representations Between Two Quantitative Variables a. Scatterplots (scatter diagrams) b. Analyzing patterns in scatterplots (Random) 5.2 Correlation a. Correlation and Linearity (Random) 5.3 Linear Regression Models a. Linear Regression (Random) 5.4 Residuals a. Residual Plots, Outliers, and Influential Points (Random) 5.5 Least-Squares Regression a. Least Squares Regression Line
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