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Syllabus.docxSyllabus
Click on the date for more information about each lecture.
A detailed version of the full syllabus is available here.
Here is the link to the book “Statistical Thinking for the 21st century” (ST21).
|
date |
topic |
reading |
learning goals |
|
1/7 |
Overview: What is statistics? |
Chapter 1, ST21 |
Describe the central goals and fundamental concepts of statistics. Describe the difference between experimental and observational research with regard to what can be inferred about causality Explain how randomization provides the ability to make inferences about causation. |
|
1/9 |
Working with data |
Chapter 2, ST21 |
Distinguish between different types of variables (quantitative/qualitative, discrete/continuous) Describe the concept of measurement error Distinguish between the concepts of reliability and validity and apply each concept to a particular dataset |
|
SECTION |
R Lab: Introduction to R |
Chapter 3, ST21 |
Interact with an RMarkdown notebook in RStudio Use R/RStudio as a calculator Define different kinds of variables in R |
|
1/14 |
Summarizing data |
Chapter 4, ST21 |
Compute absolute, relative, and cumulative frequency distributions for a given dataset Generate a graphical representation of frequency distributions Describe the difference between a normal and a long-tailed distribution, and describe the situations that give rise to each |
|
1/16 |
Visualizing data |
Chapter 6, ST21 |
Describe the principles that distinguish between good and bad graphs, and use them to identify good versus bad graphs. |
|
SECTION |
R Lab: Representing data |
Chapter 5, ST21 |
How to store vectors as variables Basic operations with vectors Create a dataframe Load data from an R package and view the data Plot summary graphs using ggplot |
|
1/21 |
Fitting models: central tendency |
Chapter 8, ST21 |
Describe the basic equation for statistical models (outcome=model + error) Describe different measures of central tendency, how they are computed, and which are appropriate under what circumstance. |
|
1/23 |
Fitting models: variability |
Chapter 8, ST21 |
Describe different measures of dispersion, how they are computed, and how to determine which is most appropriate in any given circumstance. Describe and compute z-scores. |
|
SECTION |
R Lab: Visualizing and summarizing data |
Chapter 7, ST21 |
Understand advantages of “tidy” data Visualize raw data as histograms and scatterplots |
|
1/28 |
Probability: basic rules, everyday randomness |
Chapter 10, ST21 |
Describe the sample space for a selected random experiment. Compute relative frequency and empirical probability for a given set of events Compute probabilities of single events, complementary events, and the unions and intersections of collections of events. Describe the law of large numbers. Learn about common cognitive illusions when thinking about probabilities |
|
1/30 |
Probability: conditioning and independence |
Chapter 10, ST21 |
Describe the difference between a probability and a conditional probability Describe the concept of statistical independence Use Bayes’ theorem to compute the inverse conditional probability. |
|
SECTION |
R Lab: Descriptive statistics (central tendency, dispersion) |
Chapter 9, ST21 |
Apply filtering to a dataframe Compute standard measures of central tendency and dispersion Apply z-score normalization to data |
|
2/4 |
Sampling: sampling error and the Central Limit Theorem |
Chapter 12, ST21 |
Distinguish between a population and a sample, and between population parameters and statistics Describe the concepts of sampling error and sampling distribution Describe how the Central Limit Theorem determines the nature of the sampling distribution of the mean |
|
2/6 |
Sampling: Monte Carlo simulation and bootstrapping |
Chapter 14, ST21 |
Learn about role of Monte Carlo simulation in statistics Learn about resampling techniques to estimate the sampling distribution |
|
SECTION |
R Lab: Probability |
Chapter 11, ST21 |
Gain additional intuition for probability concepts by conducting simulations in R |
|
2/11 |
Hypothesis testing: comparing means across two groups |
Chapter 16 & 28, ST21 |
Determine whether a one-sample t-test or two-sample t-test is appropriate for a given hypothesis. Identify the components of a hypothesis test, including the parameter of interest, the null and alternative hypotheses, and the test statistic. Describe the proper interpretations of a p-value as well as common misinterpretations Distinguish between the two types of error in hypothesis testing, and the factors that determine them. |
|
2/13 |
Hypothesis testing: comparing means across three or more groups; confidence intervals; effect size |
Chapter 28, ST21 |
Compute a one-sample, two-sample t-test, and ANOVA on relevant datasets, and compute the effect size and confidence intervals associated with each of these tests. Describe the proper interpretation of a confidence interval, and compute a confidence interval for the mean of a given dataset. Define the concept of effect size, and compute the effect size for a given test. Learning how to read R output |
|
SECTION |
R Lab: Sampling |
Chapter 13 & 15, ST21 |
Gain intuition for Central Limit Theorem by conducting a simulation Compute standard error of the mean for different sample sizes Construct 95% confidence intervals |
|
2/18 |
Resampling; statistical power; limitations of NHST |
Chapter 14, ST21 |
Describe how resampling can be used to compute a p-value. Define the concept of statistical power, and compute statistical power for a given statistical test. Describe the main criticisms of null hypothesis statistical testing |
|
2/20 |
Quantifying effects: confidence intervals and effect size |
Chapter 18, ST21 |
Describe the proper interpretation of a confidence interval, and compute a confidence interval for the mean of a given dataset. Define the concept of effect size, and compute the effect size for a given test. |
|
SECTION |
R Lab: Hypothesis testing |
Chapter 17 & 19, ST21 |
Compute and interpret measures of effect size Conduct t-test for difference in means and interpret results |
|
2/25 |
Modeling continuous relationships (continuous outcome; continuous predictor) |
Chapter 24, ST21 |
Describe the concept of the correlation coefficient and its interpretation and compute it for a bivariate dataset Describe the potential causal influences that can give rise to a correlation. |
|
2/27 |
Modeling categorical relationship (categorical outcome; categorical predictor) |
Chapter 22, ST21 |
Describe the concept of a contingency table for categorical data. Describe the concept of the chi-squared test for association and compute it for a given contingency table. |
|
SECTION |
R Lab: ANOVA and correlation |
Chapter 25, ST21 |
Conduct ANOVA for difference in means and interpret results Compute Pearson correlation and gain intuition for what it means |
|
3/3 |
General Linear Model: how different types of modeling are related to each other |
Chapter 26, ST21 |
Understand t-tests, ANOVA, regression as variations of the General Linear Model |
|
3/5 |
Experimental Research: replication/reproducibility; meta-analysis; challenges to cumulative science |
Chapter 32, ST21 |
Describe the concept of P-hacking and its effects on scientific practice Describe the concept of positive predictive value and its relation to statstical power |
|
SECTION |
R Lab: Linear Regression |
Chapter 27, ST21 |
Fit a linear regression model to data and interpret the results |
|
3/10 |
Observational Research: strategies for modeling real-world data |
|
Describe how to determine what kind of model to apply to a dataset |
|
3/12 |
People’s Choice |
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|
SECTION |
R Lab: Final Project Workshop |
Chapter 30, ST21 |
Demonstrate the ability to apply statistical models to real data in R |
