STATCRUNCH
Course Overview
AP Statistics is a collegelevel course that introduces students to the principles and methods of statistical analysis. The course covers a wide range of topics, including data collection and organization, probability theory, hypothesis testing, and data interpretation. Students will develop the skills necessary to analyze and interpret data, make informed decisions, and communicate statistical information effectively.
22 High Quality
lesson
19 practical Task
20 hours of video
Certificate of Achievement
Lesson You 'II Learn
Topic Covered
AP Statistics
Data Collection
Data Analysis
Theory

A solid foundation in statistical concepts and techniques.

Develop ability to collect, organize, and analyze data using appropriate statistical methods.

Enhance understanding of probability theory and its applications in realworld scenarios.

Skills to conduct hypothesis tests, make inferences, and draw conclusions based on sample data.

To foster critical thinking and problemsolving skills through handson data analysis and interpretation.

To prepare students for the AP Statistics exam, including the ability to apply statistical concepts to solve complex problems.
Course Curriculum :

Introduction to Statistics

Descriptive statistics

Inferential statistics

Data types and sources


Organizing Data

Frequency tables

Histograms and bar graphs

Stemandleaf plots

Box plots


Descriptive Statistics

Measures of central tendency (mean, median, mode)

Measures of variability (range, interquartile range, variance, standard deviation)

Measures of position (percentiles, zscores)


Probability

Fundamental counting principle

Probability rules

Conditional probability

Independent and dependent events


Random Variables and Probability Distributions

Discrete and continuous random variables

Probability mass function (PMF) and probability density function (PDF)

Binomial distribution

Normal distribution


Sampling and Sampling Distributions

Simple random sampling

Stratified and cluster sampling

Sampling distributions of means and proportions

Central Limit Theorem


Confidence Intervals

Confidence interval estimation

Margin of error

Confidence intervals for means and proportions

Determining sample size for estimating proportions


Hypothesis Testing

Null and alternative hypotheses

Type I and Type II errors

Onesample ttests

Chisquare tests


Inference for Categorical Data

Tests of independence

Goodnessoffit tests

Contingency tables


Inference for Regression

Simple linear regression

Correlation coefficient

Coefficient of determination (Rsquared)

Inference for regression parameters


Analysis of Variance (ANOVA)

Oneway ANOVA

Ftest

Post hoc tests


Experimental Design

Randomized controlled experiments

Matched pairs design

Blocking and factorial designs


Probability Simulation

Monte Carlo simulation

Random number generation

Simulating experiments


Data Analysis and Interpretation

Data visualization techniques

Exploratory data analysis

Interpretation of statistical results
