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Fun Stuff

Statistics

Collecting Data

Planning a study

Sampling methods

Sources of bias in sampling methods

Designing an experiment

Interpreting the results of an experiment

Probability, Random Variables, and Probability Distributions

Using simulation to estimate probabilities

Calculating the probability of a random event

Random variables and probability distributions

The binomial distribution

The geometric distribution

Inference for Categorical Data: Proportions

Constructing and interpreting a confidence interval for a population proportion

Setting up and carrying out a test for a population proportion

Interpreting a p-value and justifying a claim about a population proportion

Type I and Type II errors in significance testing

Confidence intervals and tests for the difference of 2 proportions

Overview

Overview

Inference for Quantitative Data: Slopes

Confidence intervals for the slope of a regression model

Setting up and carrying out a test for the slope of a regression model

Selecting an appropriate inference procedure

Inference for Quantitative Data (Means)

Constructing and interpreting a confidence interval for a population mean

Setting up and carrying out a test for a population mean

Interpreting a p-value and justifying a claim about a population mean

Confidence intervals and tests for the difference of 2 population means

Sampling Distributions

Variation in statistics for samples collected from the same population

The central limit theorem

Biased and unbiased point estimates

Sampling distributions for sample proportions

Sampling distributions for sample means

Inference for Categorical Data: Chi-Square

The chi-square test for goodness of fit

The chi-square test for homogeneity

The chi-square test for independence

Selecting an appropriate inference procedure for categorical data

Exploring Two-Variable Data

Comparing representations of 2 categorical variables

Calculating statistics for 2 categorical variables

Representing bivariate quantitative data using scatter plots

Describing associations in bivariate data and interpreting correlation

Linear regression models

Residuals and residual plots

Departures from linearity

Exploring One-Variable Data

Variation in categorical and quantitative variables

Representing data using tables or graphs

Calculating and interpreting statistics

Describing and comparing distributions of data

The normal distribution