Publications by Bryan Persaud
Data 605 Discussion 6
Chapter 3 Section 3.2 Exercise 12 2 A poker hand is a set of 5 cards randomly chosen from a deck of 52 cards. Find the probability of a (a) royal flush (ten, jack, queen, king, ace in a single suit). (b) straight flush (five in a sequence in a single suit, but not a royal flush). (c) four of a kind (four cards of the same face value). (d) full ho...
3515 sym
Data 605 Discussion 7
Chapter 6 Section 6.1 Exercise 29 In the casino game of blackjack the dealer is dealt two cards, one face up and one face down, and each player is dealt two cards, both face down. If the dealer is showing an ace the player can look at his down cards and then make a bet called an insurance bet. (Expert players will recognize why it is called insur...
1927 sym
Data 605 HW 10
Smith is in jail and has 1 dollar; he can get out on bail if he has 8 dollars. A guard agrees to make a series of bets with him. If Smith bets A dollars, he wins A dollars with probability .4 and loses A dollars with probability .6. Find the probability that he wins 8 dollars before losing all of his money if he bets 1 dollar each time (timid st...
1815 sym R (140 sym/4 pcs)
Data 605 Discussion 15
Chapter 12 Section 12.3 Exercise 17 In Exercises 9 – 26, find fx, fy, fxx, fyy, fxy and fyx. f(x, y) = cos(5xy^3) Solution: To find fx, you take the derivative of f(x, y) and treat y as a constant. fx(x, y) = \(-5{ y }^{ 3 }sin(5x{ y }^{ 3 })\) To find fy, you take the derivative of f(x, y) and treat x as a constant. fy(x, y) = \(-15x{ y }^{ ...
1406 sym
Data 605 Final Exam
Problem 1 Using R, generate a random variable X that has 10,000 random uniform numbers from 1 to N, where N can be any number of your choosing greater than or equal to 6. Then generate a random variable Y that has 10,000 random normal numbers with a mean of \(\mu =\sigma =(N+1)/2\). N <- 11 # Set a value for N numbers <- 10000 # Set the amount f...
7280 sym R (37259 sym/82 pcs) 9 img
Data 608 HW 1
Principles of Data Visualization and Introduction to ggplot2 library(ggplot2) library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union I have provided you with data ...
1985 sym R (122397 sym/17 pcs) 4 img
Data 621 Blog 2
Multiple Linear Regression For my second blog I will continue to demonstrate a linear regression model by showing how to do a multiple linear regression model. A multiple linear regression model is a model that shows the relationship between an dependent variable, y, and one or more independent variables. Load Dataset I will be using the diamond...
1508 sym R (4028 sym/11 pcs) 1 img
Data 621 Blog 1
Simple Linear Regression For my first blog I will be demonstrating how to create a simple linear regression model. A linear regression model is a model that shows the relationship between a dependent variable, y, and an independent variable, x. Load Dataet I will be using the diamonds dataset to show an example on how to create a simple linear r...
1974 sym R (5087 sym/13 pcs) 1 img
Data 621 Blog 3
Ridge Regression For my third blog I will be demonstrating ridge regression. Ridge regression can be used to create a model when the number of predictors you have are more than the number of observations. It can also be used for a dataset that has multicollinearity. Load Dataset I will be using the mtcars dataset to demonstrate this. To also hel...
1093 sym R (1717 sym/13 pcs) 1 img
Data 621 Blog 5
Generalized Linear Models For my fifth blog I will be talking about generalized linear models. Generalized linear models(GLMs) consist of two factors, an exponential family of distributions and a link function. Load Dataset To demonstrate GLMs I will be using the Boston dataset. library(MASS) head(Boston) ## crim zn indus chas nox rm ...
756 sym R (1126 sym/6 pcs) 4 img