Publications by Shariq Mian
HW9
library(tidyverse) ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ## ✔ dplyr 1.1.2 ✔ readr 2.1.4 ## ✔ forcats 1.0.0 ✔ stringr 1.5.0 ## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1 ## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0...
4535 sym R (19400 sym/117 pcs) 5 img
HW8
7.2 Friedman (1991) introduced several benchmark data sets create by simulation. One of these simulations used the following nonlinear equation to create data: y=10sin(πx1x2)+20(x3−0.5)2+10x4+5x5+N(0,σ2) where the x values are random variables uniformly distributed between [0, 1] (there are also 5 other non-informative variables also created...
2958 sym R (7562 sym/62 pcs) 3 img
HW7
Summary of Question 6.2 Developing a model to predict permeability (see Sect. 1.4) could save significant resources for a pharmaceutical company, while at the same time more rapidly identifying molecules that have a sufficient permeability to become a drug: Q6.2 (a) Start R and use these commands to load the data: library(AppliedPredictiveMode...
4198 sym R (11696 sym/67 pcs) 1 img
Data624_HW4
library(mlbench) library(corrplot) ## corrplot 0.92 loaded library(ggplot2) library(mlbench) 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 library(tsibble...
2698 sym R (44401 sym/27 pcs) 14 img
DATA624_HW3
Do exercises 5.1, 5.2, 5.3, 5.4 and 5.7 in the Hyndman book. Please submit your Rpubs link as well as your .rmd file with your code. library(fpp3) ## ── Attaching packages ────────────────────────────────────────────── fpp3 0.5 ── ## ✔ tibble ...
2266 sym R (3542 sym/24 pcs) 12 img
HW2
###1. Consider the GDP information in global_economy. Plot the GDP per capita for each country over time. Which country has the highest GDP per capita? How has this changed over time? library(fpp3) library(magrittr) # Install the forecast package if you haven't already # Load the forecast package library(forecast) global_economy %>% autopl...
2646 sym R (2900 sym/27 pcs) 19 img
Data624_HW1
Explore the following four time series: Bricks from aus_production, Lynx from pelt, Close from gafa_stock, Demand from vic_elec. Use ? (or help()) to find out about the data in each series. What is the time interval of each series? Use autoplot() to produce a time plot of each series. For the last plot, modify the axis labels and title. library(...
1904 sym R (6885 sym/48 pcs) 14 img
Final Project Data 605
title: “Final Data605” author: “Shariq Mian” date: “2023-12-14” output: html_document — 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 ...
11375 sym R (18113 sym/109 pcs) 15 img
12.3.5
In Exercises 5 – 8, evaluate fx(x, y) and fy(x, y) at the indicated point. 5. f(x, y) = x2y − x + 2y + 3 at (1, 2) # Define the function f <- function(x, y) { return(x^2 * y - x + 2 * y + 3) } # Define partial derivatives fx <- function(x, y) { return(2 * x * y - 1) } fy <- function(x, y) { return(x^2 + 2) } x_value <- 1 y...
123 sym
HW15
1 Find the equation of the regression line for the given points. Round any final values to the nearest hundredth, if necessary. x <- c(5.6, 6.3, 7, 7.7, 8.4) y <- c(8.8, 12.4, 14.8, 18.2, 20.8) regression_model <- lm(y ~ x) intercept <- coef(regression_model)[1] slope <- coef(regression_model)[2] cat("y =", round(intercept, 2), "+", round(sl...
1384 sym R (1900 sym/16 pcs)