This document discusses topics for an upcoming exam, including:
1. The exam will be 80 minutes, allow 2 pages of notes, and all information will be on the course website.
2. When taking the exam, students should read all questions first and start with the easiest, they can come back to harder questions later.
3. The recap covers expectation, mean, variance, Bernoulli and discrete uniform distributions.
4. The binomial distribution is defined as the number of successes in n independent Bernoulli trials with constant probability p of success. Its probability mass function and mean and variance are discussed. Tables can be used to calculate binomial distribution probabilities.
The document defines the moment generating function (MGF) of a random variable X as the expectation of e^tx, provided the expectation exists in some neighborhood of 0. The MGF fully characterizes the distribution of X and can be used to find moments. For the uniform distribution on [0,1], the MGF is (e^t - 1)/t. For the normal distribution with mean μ and variance σ^2, the MGF is e^(tμ + 1/2t^2σ^2). The MGF of independent random variables X and Y is the product of their individual MGFs.
This document discusses moment generating functions (MGFs), which are defined as the expectation of e^tx, where x is a random variable and t is a real number for which the expectation is finite. The MGF completely determines the distribution of a random variable. Higher moments describe properties like symmetry, peakedness, and kurtosis of a probability distribution. The MGF can be used to find moments of a random variable.
This document discusses topics for an upcoming exam, including:
1. The exam will be 80 minutes, allow 2 pages of notes, and all information will be on the course website.
2. When taking the exam, students should read all questions first and start with the easiest, they can come back to harder questions later.
3. The recap covers expectation, mean, variance, Bernoulli and discrete uniform distributions.
4. The binomial distribution is defined as the number of successes in n independent Bernoulli trials with constant probability p of success. Its probability mass function and mean and variance are discussed. Tables can be used to calculate binomial distribution probabilities.
The document defines the moment generating function (MGF) of a random variable X as the expectation of e^tx, provided the expectation exists in some neighborhood of 0. The MGF fully characterizes the distribution of X and can be used to find moments. For the uniform distribution on [0,1], the MGF is (e^t - 1)/t. For the normal distribution with mean μ and variance σ^2, the MGF is e^(tμ + 1/2t^2σ^2). The MGF of independent random variables X and Y is the product of their individual MGFs.
This document discusses moment generating functions (MGFs), which are defined as the expectation of e^tx, where x is a random variable and t is a real number for which the expectation is finite. The MGF completely determines the distribution of a random variable. Higher moments describe properties like symmetry, peakedness, and kurtosis of a probability distribution. The MGF can be used to find moments of a random variable.
The document discusses usability testing, which involves testing a product on representative users to identify usability problems, collect data on user performance, and measure satisfaction, in order to improve the product design through an iterative process before public release. It covers planning tests, conducting tests by having users complete tasks while observers take notes, and analyzing the results to identify issues and make design modifications. The goal of usability testing is to create products that are useful, efficient, engaging, error-tolerant, and easy to learn for the intended users.
The document discusses prototyping and provides guidance on creating paper prototypes. It emphasizes that prototyping is an iterative process used to gain feedback and insights. It recommends starting with storyboarding to plan interactions and convey the setting, sequence, and user experience. Tips are provided for creating paper prototypes quickly using various materials like paper, cardboard, and transparencies. The goal of paper prototyping is to test interaction flows at low cost before implementing a digital prototype.
1. Gamification can be used to supplement and innovate user experiences by adding gaming elements.
2. Octalysis identifies eight types of motivational affordances in gamification including meaning, accomplishment, empowerment, ownership, social influence, scarcity, unpredictability, and avoidance of loss.
3. To implement gamification, one should break down functions into modular components, consider how to incorporate motivational elements into each, and ensure they fulfill intrinsic and extrinsic user motivations.
The document discusses usability testing, which involves testing a product on representative users to identify usability problems, collect data on user performance, and measure satisfaction, in order to improve the product design through an iterative process before public release. It covers planning tests, conducting tests by having users complete tasks while observers take notes, and analyzing the results to identify issues and make design modifications. The goal of usability testing is to create products that are useful, efficient, engaging, error-tolerant, and easy to learn for the intended users.
The document discusses prototyping and provides guidance on creating paper prototypes. It emphasizes that prototyping is an iterative process used to gain feedback and insights. It recommends starting with storyboarding to plan interactions and convey the setting, sequence, and user experience. Tips are provided for creating paper prototypes quickly using various materials like paper, cardboard, and transparencies. The goal of paper prototyping is to test interaction flows at low cost before implementing a digital prototype.
1. Gamification can be used to supplement and innovate user experiences by adding gaming elements.
2. Octalysis identifies eight types of motivational affordances in gamification including meaning, accomplishment, empowerment, ownership, social influence, scarcity, unpredictability, and avoidance of loss.
3. To implement gamification, one should break down functions into modular components, consider how to incorporate motivational elements into each, and ensure they fulfill intrinsic and extrinsic user motivations.