General Addition Rule
... Summarize, represent, and interpret data on two categorical and quantitative variables. Summarize categorical data for two categories in two-way frequency tables. Interpret relative frequencies in the content of the data (including joint, marginal, and conditional relative frequencies). Recognize ...
... Summarize, represent, and interpret data on two categorical and quantitative variables. Summarize categorical data for two categories in two-way frequency tables. Interpret relative frequencies in the content of the data (including joint, marginal, and conditional relative frequencies). Recognize ...
4 Conditional Probability - Notes
... Experiment Yourself – This is a famous problem. On the original show, Let’s Make a Deal, contestants were given a choice of 3 curtains. They chose one and the host, Monty Hall, would show them a ZONK! that was behind one of the doors that they did not choose. They were then given the opportunity to ...
... Experiment Yourself – This is a famous problem. On the original show, Let’s Make a Deal, contestants were given a choice of 3 curtains. They chose one and the host, Monty Hall, would show them a ZONK! that was behind one of the doors that they did not choose. They were then given the opportunity to ...
Discrete Distributions
... Discrete Probability Distribution 1) Gives the probabilities associated with each possible x value 2) Usually displayed in a table, but can be displayed with a histogram or formula ...
... Discrete Probability Distribution 1) Gives the probabilities associated with each possible x value 2) Usually displayed in a table, but can be displayed with a histogram or formula ...
Scott N Gillespie Beautiful Homework #8 Fall 2000 Engineering 323
... Scott N Gillespie Engineering 323 ...
... Scott N Gillespie Engineering 323 ...
Review for the Final
... 4. A random variable X takes on the values −1, 3, and 5 with probabilities 0.1, 0.6, and 0.3 respectively. Find the probability mass function and cumulative distribution function of X. Compute E(X) and var(X). 5. Let U be a uniform random variable on the interval (1, 3). Let X = ln(U ). Find the pro ...
... 4. A random variable X takes on the values −1, 3, and 5 with probabilities 0.1, 0.6, and 0.3 respectively. Find the probability mass function and cumulative distribution function of X. Compute E(X) and var(X). 5. Let U be a uniform random variable on the interval (1, 3). Let X = ln(U ). Find the pro ...
Lecture 5
... In the previous lecture, we saw that graph entropy is subadditive. More useful properties follow. Lemma 1 (Monotonicity). If G = (V, E) and F = (V, E 0 ) are graphs on the same vertex set such that E ⊆ E 0 , then H(G) ≤ H(F ). Proof Let (X, Y ) be random variables achieving H(F ). This implies that ...
... In the previous lecture, we saw that graph entropy is subadditive. More useful properties follow. Lemma 1 (Monotonicity). If G = (V, E) and F = (V, E 0 ) are graphs on the same vertex set such that E ⊆ E 0 , then H(G) ≤ H(F ). Proof Let (X, Y ) be random variables achieving H(F ). This implies that ...
p - Tanya Khovanova
... rightmost interior point to the right-hand end is 1/(n+1). The Infinite Table Let C be the event that Vasya knows the answer to begin with, R the event that he gets it from his right-hand neighbor, L the event that he gets it from his left neighbor. In order to make these events independent, we assu ...
... rightmost interior point to the right-hand end is 1/(n+1). The Infinite Table Let C be the event that Vasya knows the answer to begin with, R the event that he gets it from his right-hand neighbor, L the event that he gets it from his left neighbor. In order to make these events independent, we assu ...
Probability - Cornell Computer Science
... Expectation The expected value EX of a discrete random variable X is the weighted sum of its possible values, each weighted by the probability that X takes on that value: ...
... Expectation The expected value EX of a discrete random variable X is the weighted sum of its possible values, each weighted by the probability that X takes on that value: ...
3.2 Conditional Probability and the Multiplication Rule
... Suppose a researcher randomly pulls the file on one of the 575 patients. – What is the probability that the patient survived their surgery? – Does knowing which hospital the patient was admitted to change that probability? ...
... Suppose a researcher randomly pulls the file on one of the 575 patients. – What is the probability that the patient survived their surgery? – Does knowing which hospital the patient was admitted to change that probability? ...
Conditional probability and independence Bernoulli trials and the
... Bayes’s Formula: Under the hypotheses of the law of total probability, for each index k, ...
... Bayes’s Formula: Under the hypotheses of the law of total probability, for each index k, ...
6.1A Notes File - Northwest ISD Moodle
... A probability model describes the possible outcomes of a chance process and the likelihood that those outcomes will occur. For example, suppose we toss a fair coin 3 times. The sample space for this chance process is HHH HHT HTH THH TT THT TTH TTT Since there are 8 equally likely outcomes, the proba ...
... A probability model describes the possible outcomes of a chance process and the likelihood that those outcomes will occur. For example, suppose we toss a fair coin 3 times. The sample space for this chance process is HHH HHT HTH THH TT THT TTH TTT Since there are 8 equally likely outcomes, the proba ...
