# 2019

**Day 1**: mindset, descriptive & inferential statistics, summary statistics, and Stata workshop

**Day 2**: graphing, exponents/logarithms, sampling distributions, and statistical significance

**Day 3**: probability basics, file structure, and data workflow

**Day 4**: variable types, functions, lines of best fit, prediction equations

**Day 5**: matrix algebra basics, reading calculus

*A PDF version of this overview for the 2019 Bootcamp is available here.*

### Instructors

Rebecca Gleit (rgleit@stanford.edu), Amy Johnson (aljohnson@stanford.edu) & Nick Sherefkin (nsherefkin@stanford.edu).

## Day 1

### Learning objectives

…understand the concept of growth mindset and how it applies to math.

…explain the difference between descriptive and inferential statistics.

…calculate mean, median, mode, and standard deviation.

…understand how data are stored in Stata.

…use logical if-statements to subset data.

…use a .do file to write reproducible code.

…begin to use functions to manipulate data. (e.g. variable creation).

### Agenda

- Introductions
- Mindset quiz and video about growth mindset
- Descriptive vs inferential statistics
- Summary statistics
- Mean, median, mode, standard deviation
- Introduction to Stata workshop

## Day 2

### Learning objectives

…conduct basic exponent and logarithm computations.

…present data as graphs and tables.

…explain the difference between a population distribution, sample distribution, and a sampling distribution.

…explain the logic of statistical significance and repeated sampling.

### Agenda

- Exponents and logarithms: Properties, basic calculations, and why they can come in handy with statistics
- Practice generating tables/graphs in Stata
- Statistical significance and repeated sampling

## Day 3

### Learning objectives

…read and use probability notation to describe a situation with bounded uncertainty.

…explain some ways that probabilistic model assumptions can undermine our models of society.

…setup a friendly file structure to manage data and programming workflow.

### Agenda

- Set notation and probability
- Translating between English and probability notation
- Sampling distributions and hypothesis testing
- Organizing data and workflow

## Day 4

### Learning objectives

…categorize variables according to their type.

…understand the difference between a function and a relation.

…explain the meaning of the slope of a line.

…draw a line of best fit and justify its location.

…describe the relationship between two variables in words, graph form, and equation form.

…understand the purpose of prediction equations and how to use them.

### Agenda

- Types of variables
- Relations vs functions
- Equation of a line
- Line of best fit (real example)
- Overview of prediction equations
- Stata practice with graphing and simple regression

## Day 5

### Learning objectives

…understand what a matrix and a vector are, and how to multiple vectors with matrices.

…be able to represent a prediction equation in matrix notation.

…read the notation of, understand, and interpret basic calculus relevant to a statistics context (e.g. limits, derivatives, integrals).

### Agenda

- Vectors, matrices, vectors with matrix multiplication
- Calculus: concepts and interpretation, notation, some basic calculations, and how this relates to statistics
- Limits
- Derivatives (and finding maximum or minimum of a function)
- Integrals

- Interpreting bootcamp topics through the lens of calculus