Online AP Statistics Preparation and training
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About AP Statistics
The Advanced Placement (AP) program offers high school students college-level curricula and exams to showcase proficiency in specific subjects before college enrollment. A high score on an AP test may earn students course credit and advanced placement in college.
Unit 1: Exploring One-Variable Data
Topics may include:
1. Variation in categorical and quantitative variables
2. Representing data using tables or graphs
3. Calculating and interpreting statistics
4. Describing and comparing distributions of data
5. The normal distribution
1. Variation in categorical and quantitative variables
2. Representing data using tables or graphs
3. Calculating and interpreting statistics
4. Describing and comparing distributions of data
5. The normal distribution
Unit 2: Exploring Two-Variable Data
Topics may include:
1. Comparing representations of 2 categorical variables
2. Calculating statistics for 2 categorical variables
3. Representing bivariate quantitative data using scatter plots
4. Describing associations in bivariate data and interpreting correlation
5. Linear regression models
6. Residuals and residual plots
7. Departures from linearity
1. Comparing representations of 2 categorical variables
2. Calculating statistics for 2 categorical variables
3. Representing bivariate quantitative data using scatter plots
4. Describing associations in bivariate data and interpreting correlation
5. Linear regression models
6. Residuals and residual plots
7. Departures from linearity
Unit 3: Collecting Data
Topics may include:
1. Planning a study
2. Sampling methods
3. Sources of bias in sampling methods
4. Designing an experiment
5. Interpreting the results of an experiment
1. Planning a study
2. Sampling methods
3. Sources of bias in sampling methods
4. Designing an experiment
5. Interpreting the results of an experiment
Unit 4: Probability, Random Variables, and Probability Distributions
Topics may include:
1. Using simulation to estimate probabilities
2. Calculating the probability of a random event
3. Random variables and probability distributions
4. The binomial distribution
5. The geometric distribution
1. Using simulation to estimate probabilities
2. Calculating the probability of a random event
3. Random variables and probability distributions
4. The binomial distribution
5. The geometric distribution
Unit 5: Sampling Distributions
Topics may include:
1. Variation in statistics for samples collected from the same population
2. The central limit theorem
3. Biased and unbiased point estimates
4. Sampling distributions for sample proportions
5. Sampling distributions for sample means
1. Variation in statistics for samples collected from the same population
2. The central limit theorem
3. Biased and unbiased point estimates
4. Sampling distributions for sample proportions
5. Sampling distributions for sample means
Unit 6: Inference for Categorical Data: Proportions
Topics may include:
1. Constructing and interpreting a confidence interval for a population proportion
2. Setting up and carrying out a test for a population proportion
3. Interpreting a p-value and justifying a claim about a population proportion
4. Type I and Type II errors in significance testing
5. Confidence intervals and tests for the difference of 2 proportions
1. Constructing and interpreting a confidence interval for a population proportion
2. Setting up and carrying out a test for a population proportion
3. Interpreting a p-value and justifying a claim about a population proportion
4. Type I and Type II errors in significance testing
5. Confidence intervals and tests for the difference of 2 proportions
Unit 6: Integration and Accumulation of Change
Topics may include:
1. Using definite integrals to determine accumulated change over an interval
2. Approximating integrals using Riemann Sums
3. Accumulation functions, the Fundamental Theorem of Calculus, and definite integrals
4. Antiderivatives and indefinite integrals
5. Properties of integrals and integration techniques
1. Using definite integrals to determine accumulated change over an interval
2. Approximating integrals using Riemann Sums
3. Accumulation functions, the Fundamental Theorem of Calculus, and definite integrals
4. Antiderivatives and indefinite integrals
5. Properties of integrals and integration techniques
Unit 7: Inference for Quantitative Data: Means
Topics may include:
1. Constructing and interpreting a confidence interval for a population mean
2. Setting up and carrying out a test for a population mean
3. Interpreting a p-value and justifying a claim about a population mean
4. Confidence intervals and tests for the difference of 2 population means
1. Constructing and interpreting a confidence interval for a population mean
2. Setting up and carrying out a test for a population mean
3. Interpreting a p-value and justifying a claim about a population mean
4. Confidence intervals and tests for the difference of 2 population means
Unit 8: Inference for Categorical Data: Chi-Square
Topics may include:
1. The chi-square test for goodness of fit
2. The chi-square test for homogeneity
3. The chi-square test for independence
4. Selecting an appropriate inference procedure for categorical data
1. The chi-square test for goodness of fit
2. The chi-square test for homogeneity
3. The chi-square test for independence
4. Selecting an appropriate inference procedure for categorical data
Unit 9: nference for Quantitative Data: Slopes
Topics may include:
1. Confidence intervals for the slope of a regression model
2. Setting up and carrying out a test for the slope of a regression model
3. Selecting an appropriate inference procedure
1. Confidence intervals for the slope of a regression model
2. Setting up and carrying out a test for the slope of a regression model
3. Selecting an appropriate inference procedure
Advanced Placement
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Exam strategy
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01. Decision tree
02. Answer elimination
03. Passage mapping
