Publications by Joey Bochnik
DATA 624 Project 1
First do some EDA on the data. Read in the file and get a sense for the data. library(fpp3) ## Warning: package 'fpp3' was built under R version 4.3.3 ## Registered S3 method overwritten by 'tsibble': ## method from ## as_tibble.grouped_df dplyr ## ── Attaching packages ──────────────────�...
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DATA 624 Homework 6
library(fpp3) ## Warning: package 'fpp3' was built under R version 4.3.3 ## Registered S3 method overwritten by 'tsibble': ## method from ## as_tibble.grouped_df dplyr ## ── Attaching packages ──────────────────────────────────────────── f...
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DATA 624 Homework 5
library(fpp3) ## Warning: package 'fpp3' was built under R version 4.3.3 ## Registered S3 method overwritten by 'tsibble': ## method from ## as_tibble.grouped_df dplyr ## ── Attaching packages ──────────────────────────────────────────── f...
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DATA 624 Homework 4
Problem 3.1 The UC Irvine Machine Learning Repository 6 contains a data set related to glass identification. The data consist of 214 glass samples labeled as one of seven class categories. There are nine predictors, including the refractive index and percentages of eight elements: Na, Mg, Al, Si, K, Ca, Ba, and Fe. Part A Using visualizations, exp...
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DATA 624 Homework 3
library(fpp3) ## Warning: package 'fpp3' was built under R version 4.3.3 ## Registered S3 method overwritten by 'tsibble': ## method from ## as_tibble.grouped_df dplyr ## ── Attaching packages ──────────────────────────────────────────── f...
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DATA 624 Assignment 2
Assignment 2 Load libraries library(fpp3) ## Warning: package 'fpp3' was built under R version 4.3.3 ## Registered S3 method overwritten by 'tsibble': ## method from ## as_tibble.grouped_df dplyr ## ── Attaching packages ───────────────────────────────────�...
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DATA 624 Assignment 1
Problem 2.1 Explore the following four time series: Bricks from aus_production, Lynx from pelt, Close from gafa_stock, Demand from vic_elec. library(fpp3) ## Warning: package 'fpp3' was built under R version 4.3.3 ## Registered S3 method overwritten by 'tsibble': ## method from ## as_tibble.grouped_df dplyr ## ── Attaching pa...
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Final Project
Final Project Problem 1 Using R, set a random seed equal to 1234 (i.e., set.seed(1234)). Generate a random variable X that has 10,000 continuous random uniform values between 5 and 15.Then generate a random variable Y that has 10,000 random normal values with a mean of 10 and a standard deviation of 2.89. # Setting the random seed set.seed(1234) ...
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Week 15 Assignment
Week 15 Assignment Problem 1 Find the equation of the regression line for the given points. Round any final values to the nearest hundredth, if necessary. ( 5.6, 8.8 ), ( 6.3, 12.4 ), ( 7, 14.8 ), ( 7.7, 18.2 ), ( 8.4, 20.8 ) We can use R to find the regression line by putting the points into a matrix and fitting a regression model on the data. x ...
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Week 15 Discussion Page 711 Question 9
Page 711 Problem 9 Find \(f_x, f_y, f_{xx}, f_{yy}, f_{xy} and f_{yx}\). \(f(x,y)=x^2y+3x^2 +4y−5\) \(f_x= x^2y+3x^2+4y-5 \frac{\partial}{\partial x}= 2xy+6x\) \(f_y = x^2y+3x^2+4y-5 \frac{\partial}{\partial y}= x^2+4\) \(f_{xx} =2xy+6x \frac{\partial}{\partial x} = 2y+6\) \(f_{yy} = x^2+4 \frac{\partial}{\partial y} = 0\) \(f_{xy} = 2xy+6x ...
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