lecture2 - ECON 103 Lecture 2 Statistics Review Maria...

Info icon This preview shows pages 1–7. Sign up to view the full content.

View Full Document Right Arrow Icon
ECON 103, Lecture 2: Statistics Review Maria Casanova January 12th (version 0) Maria Casanova Lecture 2
Image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Requirements for this lecture: Chapter 2 of Stock and Watson Maria Casanova Lecture 2
Image of page 2
1. Random Variables X is a random variable if it takes different values according to some probability distribution Types of random variables: Discrete random variables Take on a finite or number of values Example: outcome of a coin toss Continuous random variables Take on any value in a real interval Each specific value has zero probability Example: wage of a worker in company ABC. Maria Casanova Lecture 2
Image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
2.1. Univariate Probability Distributions Probability distributions of discrete random variables The probability density function of a discrete random variable that takes on values, say, x 1 and x 2 is defined as: f ( x ) = Pr ( X = x 1 ) Pr ( X = x 2 ) Pr ( X 6 = x 1 and X 6 = x 2 ) = 0 The cumulative distribution function (CDF) is the probability that the random variable is less or equal to a particular value. Maria Casanova Lecture 2
Image of page 4
2.1. Univariate Probability Distributions Figure: Probability distributions of discrete random variable 0 .25 .5 .75 1 0 = tails 1 = heads (a) probability distribution 0 .25 .5 .75 1 0 = tails 1 = heads (b) CDF Maria Casanova Lecture 2
Image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
2.1. Univariate Probability Distributions Probability distributions of continuous random variables The cumulative distribution function (CDF) of a continuous variable is the probability that the random variable is less or equal to a particular value. The probability density function (PDF) of a continuous random variable is defined as the derivative of the cumulative distribution function (CDF). The area under the probability
Image of page 6
Image of page 7
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern