Syllabus

Click on the date for more information about each lecture

Detailed version of the full syllabus is available here

Date Topic Reading
1/7 What is statistics? overview of the course

Learning Objectives:

After this lecture, you should be able to:
* 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.

Chapter 1
1/9 No class (sick day) - content will be made up on 1/23
 
1/11 R Lab: Basics

Learning Objectives:

After this lecture, you should be able to:
* Interact with an RMarkdown notebook in RStudio
* Describe the difference between a variable and a function
* Create a vector, matrix, or data frame and access its elements
* Load a data file into a data frame and plots its contents

Links:

* For additional practice with R, check out the free courses provided by Datacamp. In particular, their Introduction to R provides a nice basic overview of working in R.

Install RStudio (instructions)
Complete Intro to basics section of DataCamp Introduction to R (https://campus.datacamp.com/courses/free-introduction-to-r)
1/14 Probability

Learning Objectives:

After this lecture, you should be able to:
* 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.

Links:

* R notebook for lecture

Chapter 3 (Sections 3.1-3.3)
1/16 Probability, cont.

Learning Objectives:

After this lecture, you should be able to:
* 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.

Chapter 3 (Sections 3.4-3.10)
1/18 R lab: probability

Learning Objectives:

After this lecture, you should be able to:
* Intro to Rmarkdown notebooks
* Compute probabilities of combinations of events
* Compute an empirical probability distribution
* Describe the different functions available for the normal distribution, and their usage

http://www.cyclismo.org/tutorial/R/probability.html
1/21 no class, MLK day **
1/23 Working with data (make-up for session 2)

Learning Objectives:

After this lecture, you should be able to:
* Distinguish between different types of variables (quantitative/qualitative, discrete/continuous, scales of measurement)
* Describe the concept of measurement error
* Distinguish between the concepts of reliability and validity and apply each concept to a particular dataset

Chapter 2
1/25 Summarizing data

Learning Objectives:

After this lecture, you should be able to:
* 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

Links:

* R Notebook for lecture
* Social network data

Chapter 4
1/28 R lab: Data wrangling and visualization

Learning Objectives:

After this lecture, you should be able to:
* Describe the concept of tidy data
* Load a data file and prepare it for analysis
* Plot summary graphs using ggplot

Introduction and Section 2.1 of https://garrettgman.github.io/tidying/
Sections 3.1-3.6 of http://r4ds.had.co.nz/data-visualisation.html
1/30 Fitting models (central tendency)

Learning Objectives:

After this lecture, you should be able to:
* Describe the basic equation for statistical models (outcome=model + error)
* Describe different measures of central tendency and dispersion, how they are computed, and how to determine which is most appropriate in any given circumstance.

Links:

* R Notebook for lecture

Chapter 5
2/1 Visualizing data

Learning Objectives:

After this lecture, you should be able to:
* Describe the principles that distinguish between good and bad graphs, and use them to identify good versus bad graphs.

Chapter 6
2/4 Sampling

Learning Objectives:

After this lecture, you should be able to:
* 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

Links:

* R notebook for lecture

Chapter 7
2/6 Resampling and simulation

Learning Objectives:

After this lecture, you should be able to:
* Describe the statistical concept of a random number
* Describe the concept of Monte Carlo simulation
* Describe the concept of the bootstrap and use it to estimate the sampling distribution of a statistic

Links:

* R notebook for lecture

Chapter 8
Watch video: https://www.youtube.com/watch?v=tClZGWlRLoE
2/8 R Lab: Simulation and resampling

Learning Objectives:

After this lecture, you should be able to:
* Demostrate the ability to implement a Monte Carlo simulation in R

Watch video: https://www.youtube.com/watch?v=tvv4IA8PEzw&t=482s (note that the video talks about several concepts that we have not learned about in class - just go with it and focus on the R-related material)
2/11 Hypothesis testing

Learning Objectives:

After this lecture, you should be able to:
* 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 and a confidence interval as well as common misinterpretations
* Distinguish between the two types of error in hypothesis testing, and the factors that determine them.

Links:

* R notebook for lecture: https://rawgit.com/psych10/psych10/master/notebooks/Session16-HypothesisTesting/Session16-HypothesisTesting.html

Chapter 9
2/13 Hypothesis testing, cont.

Learning Objectives:

After this lecture, you should be able to:
* 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

Chapter 9, cont.
2/15 Confidence intervals and effect sizes

Learning Objectives:

After this lecture, you should be able to:
* 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.

Chapter 10
2/18 No class, Presidents Day
 
2/20 Bayesian analysis

Learning Objectives:

After this lecture, you should be able to:

Chapter 11
https://www.youtube.com/watch?v=BcvLAw-JRss
2/22 Bayesian analysis, cont.

Learning Objectives:

After this lecture, you should be able to:

 
2/25 Bayesian analysis, cont. 2
 
2/27 Modeling categorical relationships

Learning Objectives:

After this lecture, you should be able to:
* 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.

Chapter 12
3/1 Modeling continuous relationships (RP Gone - need guest lecturer)

Learning Objectives:

After this lecture, you should be able to:
* 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.

Links:

* Spurious Correlations

Chapter 13
3/4 The general linear model

Learning Objectives:

After this lecture, you should be able to:
* Describe the concept of linear regression and apply it to a bivariate dataset
* Describe the concept of the general linear model and provide examples of its application

Chapter 14
3/6 Comparing means

Learning Objectives:

After this lecture, you should be able to:
* Determine whether a one-sample t-test or two-sample t-test is appropriate for a given hypothesis.
* Compute a one-sample and two-sample t-test on relevant datasets, and compute the effect size and confidence intervals associated with each of these tests.

Chapter 15
3/8 Statistical Inference R lab

Learning Objectives:

After this lecture, you should be able to:
* Demonstrate the ability to apply statistical models to real data in R

 
3/11 Statistical modeling: Practical examples

Learning Objectives:

After this lecture, you should be able to:
* Describe how to determine what kind of model to apply to a dataset

Chapter 16
3/13 Doing reproducible research

Learning Objectives:

After this lecture, you should be able to:
* 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

Links:

* Fivethirtyeight P-hacking demo

Chapter 17
Simmons et al (available on Canvas)
https://www.buzzfeed.com/stephaniemlee/brian-wansink-cornell-p-hacking?utm_term=.gtAVwLX2GM#.fep9L6pw78
3/15 Doing reproducible research, cont
 
3/21 Final meeting with project presentations, 8:30-11:30am