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Suppose there are $n$ questions on the test. Each can be answered correctly for 1 point or incorrectly for 0 point. Student $i$ has probability $\pi_i$ of answering each question correctly. (This can be generalized for asymmetric test questions.)

The expected number of correct answers is $n\pi_i$.

How to install Make on Windows 10 and later

Install "App Installer" from the Microsoft Store
Open a terminal with cmd (Run as Administrator) ~~or Git Bash~~.
Type winget install -e --id GnuWin32.Make to install GNUWin Make. This will be installed in C:\Program Files (x86)\GnuWin32\bin.
Add this folder to path, so that make and other GNUWin tools you install can be invoked from anywhere.
Launch a terminal again, and check that make results in this error message: make: *** No targets specified and no makefile found. Stop.

WSL alternative

Replicability Manifesto

Cite everything that the replication package relies on.
- Why: Citations in the manuscript and the README are the best way of directing readers to these resources and to give credit to the original authors.
- How: All datasets, whether public or private, included or not, should be cited. It is good practice to cite software packages, but not required, unless the license terms specifically ask you to do so.
Include all exhibits and the code to produce them ALL DATA THAT YOU ARE ABLE (drill down).
- Why: The package should be self contained so that readers can verify that your analysis code produces the output you included.
- How: Save all the output of the analysis in some standard format into the replication package. Tables can be saved as .csv, .xls or .tex files. It is also sufficient to save the Stata .log file. In this case, please report in the README the line numbers where the readers can find the table numbers. Figures can be saved in .eps, .pdf or .png.
- How 2: Re


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	using SpectralKit
	using LinearAlgebra
	using Optim
	using Plots

	const maxiter = 10
	mutable struct SaddlePath
	basis::SpectralKit.FunctionBasis
	coefs::Vector{Float64}
	end

	using LogExpFunctions
	using Distributions

	function generate_data(K=1_000_000)
	X = collect(range(-6, -3, length=K))
	Y = rand.(Bernoulli.(logistic.(X)))
	return X, Y
	end

	# dispatch on different types of numerical methods

	from shapely.geometry import Point
	# suppose function projection(lat, lon) is already defined, returing (x, y) in meters

	neighborgood_gps = (48.2176956, 16.3901985)
	# a list of GPS coordinate pairs
	transitstops_gps = [(), (), ..., ()]

	neighborhood_shapely = Point(projection(neighborgood_gps))

	for point in transitstops_gps:

	#!/usr/bin/env python2.7

	import re, string
	from sys import argv
	from os import path, system, remove

	class WikiParser:
	"""Object that turns Wii LaTeX.

	All formatting commands can be parsed one line at a time, though