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Unit 1

Module 1 - Introduction to Concepts in Statistics
course syllabus; Canvas information (and help); College information; Contract for Success; Precourse Survey

Module 2 - Learning Strategies

Module 3 - The Big Picture

Unit 2

Module 4 - Distributions for Quantitative Data
distinguish between quantitative and categorical variables in context; describe the distribution of quantitative data using a dot plot; describe the distribution of quantitative data using a histogram

Module 5 - Mean and Median
use mean and median to describe the center of a distribution

Module 6 - Measures of Spread about the Median
use a five-number summary and a boxplot to describe a distribution

Module 7 - Standard Deviation
use mean and standard deviation to describe a distribution

Unit 3

Module 8 - Relationships in Categorical Data
analyze the distribution of a categorical variable; analyze the relationship between two categorical variables using a two-way table; calculate marginal, joint, and conditional percentages and interpret them as probability estimates; analyze and compare risks using conditional probabilities; create a hypothetical two-way table to answer more complex probability questions

Unit 4

Module 9 - Probability and Probability Distributions
distinguish between theoretical and empirical probability; interpret probability distributions for categorical and quantitative variables; describe the probability distribution of quantitative variable using shape, center (mean) and spread (standard deviation)

Module 10 - Probability Distributions for Continuous Quantitative Variables
distinguish between discrete quantitative variables and continuous quantitative variables; use probability distributions to estimate probabilities and identify unusual events

Unit 5

Module 11 - Types of Statistical Studies
determine the goal of a statistical study; distinguish between experiments and observational studies; use study design to determine what types of conclusions are appropriate

Module 12 - Collecting Data: Sampling
for an observational study - critique the sampling plan and recognize implications and limitations of the plan

Module 13 - Collecting Data: Conducting an Experiment
identify features of experiment design that control the effects of confounding; avoid overgeneralization of experiment results

Unit 6

Module 14 - Introduction to Inference
use simulation and probability to draw conclusions based on data; distinguish situations that involve inference about a population mean from those that involve a population proportion; distinguish problems that call for estimating a parameter from problems that call for testing a hypothesis about a parameter

Module 15 - Distribution of Sample Proportions
describe the sampling distribution for sample proportions and use it to identify unusual (and more common) sample results; distinguish between a sample statistic and a population parameter; use a z-score and the standard normal model to estimate probabilities of specified events

Module 16 - Introduction to Confidence Intervals
using the margin of error, find a confidence interval to estimate a population proportion when conditions are met; interpret the confidence interval in context; interpret the confidence level associated with a confidence interval

Unit 7

Module 17 - Estimating a Population Proportion
recognize situations that call for testing a claim about a population proportion or estimating a population proportion; construct a confidence interval to estimate a population proportion when conditions are met; interpret the confidence interval in context; for a confidence interval, interpret the meaning of a confidence level and relate it to the margin of error

Module 18 - Introduction to Hypothesis Testing
when testing a claim, distinguish among situations involving one population mean, one population proportion, two population means, or two population proportions; given a claim about a population, determine null and alternative hypotheses; recognize the logic behind a hypothesis test and how it relates to the P-value; recognize type I and type II errors

Module 19 - Hypothesis Test for a Population Proportion
recognize when a situation calls for testing a hypothesis about a population proportion; conduct a hypothesis test for a population proportion and state a conclusion in context; interpret the P-value as a conditional probability; distinguish statistical significance from practical importance; from a description of a study, evaluate whether the conclusion of a hypothesis test is reasonable

Unit 8

Module 20 - Distribution of Sample Means
recognize when to use a hypothesis test or a confidence interval to draw a conclusion about a population mean; describe the sampling distribution of sample means; draw conclusions about a population mean from a simulation; estimate the probability of an event using a normal model of the sampling distribution

Module 21 - Estimating a Population Mean
construct a confidence interval to estimate a population mean when conditions are met; interpret the confidence interval in context; interpret the meaning of a confidence level associated with a confidence interval; adjust the margin of error by making changes to the confidence level or sample size

Module 22 - Hypothesis Test for a Population Mean
recognize when to use a hypothesis test or a confidence interval to draw a conclusion about a population mean; under appropriate conditions, conduct a hypothesis test about a population mean and state the conclusion in context; under appropriate conditions, conduct a hypothesis test about a mean for a matched pairs design and state a conclusion in context; interpret the P-value as a conditional probability

Module 23 - Hypothesis Test for a Difference in Two Population Means
under appropriate conditions, conduct a hypothesis test about a difference between two population means and state a conclusion in context; construct a confidence interval to estimate a difference in two population means (when conditions are met); interpret the confidence interval in context

Unit 9

Module 24 - ANOVA One-way F-test
if conditions are met use the ANOVA F-test to compare more than two population means and state the conclusion in context; if conditions are met use the ANOVA F-test to examine the relationship between a categorical explanatory variable that has more than two values and a quantitative response variable and state the conclusion in context

Unit 10

Module 25 - Chi-Square Test of Independence
conduct a chi-square test of independence and interpret the conclusion in context

Unit 11

Module 26 (Part 1) - Scatterplots
use a scatterplot to display the relationship between two quantitative variables; describe the overall pattern (form, direction, and strength) and striking deviations from the pattern

Module 26 (Part 2) - Linear Relationships and Correlation
use a correlation coefficient to describe the direction and strength of a linear relationship; recognize the limitations of the correlation coefficient as a measure of the relationship between two quantitative variables; distinguish between association and causation; identify lurking variables that may explain an observed relationship

Module 27 - Linear Regression
for a linear relationship, use the least squares regression line to model the pattern in the data and to make predictions

Unit 12

Module 28 - Exponential Relationships
use an exponential model (when appropriate) to describe the relationship between two quantitative variables; interpret the exponential model in context

Other Resources

Resources
applets and other resources

Final Exam Preparation
available when appropriate