## Topic outline

•  ## Welcome to AP Statistics The purpose of Advanced Placement Statistics is to introduce students to the major concepts and tools for collecting, analyzing, and drawing conclusions from data. Students are exposed to four broad conceptual themes:

• Exploring Data: Observing patterns and departures from patterns (20%-30%)
• Planning and Conducting a Study: Deciding what and how to measure (10%-15%)
• Anticipating Patterns: Producing models using probability and simulation (20%-30%)
• Statistical Inference: Estimating parameters and testing hypotheses (30%-40%)
The percentages in parentheses for each content area indicate the coverage for that content area in the exam.
•  ### Daily Assignments

•  •  ### Chapter 6: Random Variables In Chapter 5, we learned that a "random phenomenon" was one that was unpredictable in the short term, but displayed a predictable pattern in the long run. In Statistics, we are often interested in numerical outcomes of random phenomena. In this chapter, we will learn to define random variables to describe numeric outcomes of random phenomena as well as how to calculate the means and variances of such random variables.

In practice, we often encounter situations where there are two outcomes of interest. These situations can be described by binomial and geometric distributions. We will use what we have learned about probability and random variables to complete our foundational study of inference.

•  ### Chapter 7: Sampling Distributions The inferential methods we will learn in the coming chapters will be based on using information from a sample to reach a conclusion about the population. In order to use this information, we must develop an understanding of how sampling information varies from sample to sample. In this chapter, we will explore the behavior of sample statistics in repeated sampling and learn one of the most important theorems in Statistics - The Central Limit Theorem.

•  ### Chapter 8: Estimating with Confidence When we select a sample, we want to infer some conclusion about the population that the sample represents. In this chapter, we will be introduced to the one of the most common types of formal statistical inference: Confidence Intervals. This type of inference is based on the sampling distributions of statistics. The purpose of this chapter is to describe the reasoning used when constructing confidence intervals.

•  ### Chapter 9: Testing a Claim In Chapter 8, we learned inferential procedures for constructing a confidence interval. In this chapter, we will introduce the basics of significance testing and then focus on one-sample tests for a population proportion and one-sample tests for a population mean.

•  ### Chapter 10: Comparing Two Populations or Groups In Chapter 9, we learned the logic behind inferential procedures. In this chapter, we will apply that logic to inference involving two groups samples or groups. We will learn how to build confidence intervals and perform significance tests for comparisons between two proportions and two means.

• This topic ### Chapter 11: Chi-Square Procedures In the previous chapters, we discussed inference procedures for means and proportions. In some cases, we want to examine the distribution of proportions for a population or determine whether the distribution of one variable has been influenced by another. Chi-square procedures help us in these situations.

•  •  •  ### Topic 20

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