Publications by Fares A
624_M8
9.1 The 9.32 figure shows the ACFs for 36 random numbers, 360 random numbers and 1,000 random numbers. a. Explain the differences among these figures. Do they all indicate that the data are white noise? Yes, all three ACF plots are consistent with white noise, but the appearance varies due to sample size: Left (n = 36): You see more apparent �...
13530 sym R (14928 sym/76 pcs) 29 img
622_HW2
library(tidyverse) library(DataExplorer) library(corrplot) library(ggplot2) library(dplyr) library(gridExtra) library(grid) library(scales) library(tibble) library(DT) library(naniar) library(Amelia) library(caret) library(rpart) library(rpart.plot) library(pROC) library(randomForest) library(adabag) library(tibble) This data ...
30401 sym R (29135 sym/82 pcs) 29 img
624_M6 Ch8ex
Hyndman chapter 8 Exercise 8.1 library(fpp3) data(aus_livestock) Use the ETS() function to estimate the equivalent model for simple exponential smoothing. Find the optimal values of α and ℓ0, and generate forecasts for the next four months. Compute a 95% prediction interval for the first forecast using ^y±1.96s where s is the standard devi...
6879 sym R (15318 sym/38 pcs) 17 img
622_HW1
library(tidyverse) library(DataExplorer) library(corrplot) library(ggplot2) library(dplyr) library(gridExtra) library(grid) library(scales) library(tibble) library(DT) library(naniar) library(Amelia) This data is offered in two ways: one where there are only 16 features along with the target variables (y; subscribed status) and ano...
17628 sym R (9098 sym/26 pcs) 24 img
624_M5
(Applied statistical learning, Kuhn and Johnson) Exercise 3.1. The UC Irvine Machine Learning Repository6 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, S...
3425 sym R (13216 sym/31 pcs) 44 img
624_M4-HW3
Produce forecasts for the following series using whichever of NAIVE(y), SNAIVE(y) or RW(y ~ drift()) is more appropriate in each case: Australian Population (global_economy) Bricks (aus_production) NSW Lambs (aus_livestock) Household wealth (hh_budget). Australian takeaway food turnover (aus_retail). Australian Population library(fpp3) pop_mo...
5634 sym R (7174 sym/20 pcs) 20 img
624_M3
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) ## Registered S3 method overwritten by 'tsibble': ## method from ## as_tibble.grouped_df dplyr ## ── Attaching packages ───�...
6708 sym R (11305 sym/48 pcs) 27 img
624_M2
# libraries library(ggplot2) library(fpp3) library(dplyr) library(tidyverse) Exercise 2.1 checking the help for each: help(aus_production) ## starting httpd help server ... done help(pelt) help(gafa_stock) help(vic_elec) Time interval of each series: Bricks aus_production ## # A tsibble: 218 x 7 [1Q] ## Quarter Beer Tobacco Bricks C...
3353 sym R (10642 sym/70 pcs) 33 img
605_week15_hw
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 ) Solution: To find the equation of the regression line for the given set of points, I will be calculating the slope (m) and the y-intercept ...
6056 sym R (1856 sym/8 pcs)
605_M14_hw
Solving these manually, I can go with: 1: \[ \begin{aligned} f(x) &= \frac{1}{1-x}, \\ f'(x) &= \frac{1}{(1-x)^2}, \\ f''(x) &= \frac{2}{(1-x)^3}, \\ f'''(x) &= \frac{6}{(1-x)^4}, \\ f^{(4)}(x) &= \frac{24}{(1-x)^5}, \\ \text{Taylor Series: } P(x) &= \sum_{n=0}^\infty \frac{f^{(n)}(0)}{n!} x^n = 1 + x + x^2 + x^3 + x^4 + \cdots. \end{alig...
1379 sym R (1898 sym/18 pcs)