Publications by Neo L
Rpub
Introduction Coffee shops play an integral role in the lives of students, serving as spaces for studying, socializing, and relaxation. Despite the numerous local coffee shops around SJSU, many students prefer larger chains such as Starbucks. This study aims to understand the factors influencing student satisfaction across various coffee shops a...
2349 sym R (7986 sym/55 pcs) 2 img 1 tbl
Publish Document
Introduction Coffee shops play an integral role in the lives of students, serving as spaces for studying, socializing, and relaxation. Despite the numerous local coffee shops around SJSU, many students prefer larger chains such as Starbucks. This study aims to understand the factors influencing student satisfaction across various coffee shops a...
3161 sym R (7034 sym/61 pcs) 2 img
Project task 7
Introduction Coffee shops play an integral role in the lives of students, serving as spaces for studying, socializing, and relaxation. Despite the numerous local coffee shops around SJSU, many students prefer larger chains such as Starbucks. This study aims to understand the factors influencing student satisfaction across various coffee shops a...
2788 sym R (7107 sym/63 pcs) 2 img
Publish Document
Introduction Coffee shops play an integral role in the lives of students, serving as spaces for studying, socializing, and relaxation. Despite the numerous local coffee shops around SJSU, many students prefer larger chains such as Starbucks. This study aims to understand the factors influencing student satisfaction across various coffee shops a...
3319 sym R (7034 sym/61 pcs) 2 img
Task7
Introduction Coffee shops play an integral role in the lives of students, serving as spaces for studying, socializing, and relaxation. Despite the numerous local coffee shops around SJSU, many students prefer larger chains such as Starbucks. This study aims to understand the factors influencing student satisfaction across various coffee shops a...
3321 sym R (7034 sym/61 pcs) 2 img
Class exercise 16
Question 1: Naive Method values <- c(17, 13, 15, 11, 17, 14) forecasts <- c(NA, values[-length(values)]) # Previous values used as forecasts errors <- values[-1] - forecasts[-1] # Remove the initial NA mae <- mean(abs(errors), na.rm = TRUE) mse <- mean(errors^2, na.rm = TRUE) mape <- mean(abs(errors) / values[-1] * 100, na.rm = TRUE) fore...
561 sym R (3717 sym/26 pcs) 2 img
Class exercise 16
Question 1: Naive Method values <- c(17, 13, 15, 11, 17, 14) forecasts <- c(NA, values[-length(values)]) # Previous values used as forecasts errors <- values[-1] - forecasts[-1] # Remove the initial NA mae <- mean(abs(errors), na.rm = TRUE) mse <- mean(errors^2, na.rm = TRUE) mape <- mean(abs(errors) / values[-1] * 100, na.rm = TRUE) fore...
264 sym R (3717 sym/26 pcs) 2 img
Exercise 16
## Question 1: ## Mean Absolute Error (MAE): 3.8 ## Mean Squared Error (MSE): 16.2 ## Mean Absolute Percentage Error (MAPE): 27.55464 % ## Forecast for Week 7: 14 ## Cumulative Averages: ## [1] 240.0000 296.0000 274.0000 270.5000 272.4000 280.6667 272.0000 276.7500 ## [9] 272.6667 276.4000 273.0909 ## Question 2: ## MSE for Moving Average: 996....
149 sym 2 img
Class exercise 15
# Load necessary libraries library(readxl) ## Warning: package 'readxl' was built under R version 4.4.2 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 librar...
36 sym R (4140 sym/32 pcs) 1 img