Displaying_and_describing_data
One of the first things we do in statistics is try to understand sets of data. This involves plotting the data in different ways and summarizing what we see with measures of center (like mean and median) and measures of spread (like range and standard deviation). This topic focuses on concepts that are often referred to as "descriptive statistics".
Study design focuses on collecting data properly and making the most valid conclusions we can based on how the data was collected. This topic covers explore samples, surveys, and experiments.
Modeling_distributions_of_data
The normal distribution is the most commonly used model in all of statistics. Learn how to measure position using z-scores and find what percent of data falls where in a normal distribution.
A sampling distribution shows every possible result a statistic can take in every possible sample from a population and how often each result happens. This topic covers how sample proportions and sample means behave in repeated samples.
Analysis of variance, also called ANOVA, is a collection of methods for comparing multiple means across different groups.
Confidence_intervals_one_sample_
Confidence intervals give us a range of plausible values for some unknown value based on results from a sample. This topic covers confidence intervals for means and proportions.
Significance_tests_one_sample_
Significance tests give us a formal process for using sample data to evaluate the likelihood of some claim about a population value. We calculate p-values to see how likely a sample result is to occur by random chance, and we use p-values to make conclusions about hypotheses.
Significance_tests_and_confidence_intervals_two_samples_
Learn how to apply what you know about confidence intervals and significance tests to situations that involve comparing two samples to see if there is a significant difference between the two populations.
Advanced_regression_inference_and_transforming_
Advanced regression will introduce you to regression methods when data has a nonlinear pattern.
Inference_for_categorical_data_chi_square_tests_
Chi-square tests are a family of significance tests that give us ways to test hypotheses about distributions of categorical data. This topic covers goodness-of-fit tests to see if sample data fits a hypothesized distribution, and tests for independence between two categorical variables.
Probability tells us how often some event will happen after many repeated trials. This topic covers theoretical, experimental, compound probability, permutations, combinations, and more!
Describing_relationships_in_quantitative_data
If we want to explore a relationship between two quantitative variables, we make a scatterplot of the data. The fun doesn't stop there. We describe pattern, talk about the type of correlation we see, and sometimes fit a line to the data so we can use the pattern to make predictions.
Random variables can be any outcomes from some chance process, like how many heads will occur in a series of 20 flips. We calculate probabilities of random variables and calculate expected value for different types of random variables.
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