Dense bundles of multicoloured fibres sweeping across a near-black ground

Week: Regression

Workshop 11: Regression in R

Modeling for System Improvement

Tim Fraser, PhD

Assistant Teaching Professor, Systems Engineering
Cornell University

SYSEN 5300: Systems Engineering & Six Sigma

for Design and Operation of Reliable Systems

Risk Analysis
- Risk assessment and risk characterization
- Failure Modes and Effects Analysis
- Fault trees and event trees under uncertainty
Modeling Reliability
- Component and system reliability
- Physical acceleration models
- Required function, stated conditions, specified time
- One element of the broader risk analysis

Quality Control
- Six sigma and statistical process control
- Detect when performance is deteriorating
- Take corrective action in time
System Improvement
- Optimize system design for reliability
- Design of experiments
- Response surfaces — innovation, problem solving, and discovery

Today's Class

Reading Questions
Coding Demo

The big question:
As X increases, what happens to Y?

Animated scatterplot: a cloud of points shifts from a negative to a positive relationship as the line of best fit swings with it

Reading Questions

Workshop 11

Activity: Explain It

Please complete the following steps:

Pick 2 reading questions from slides below.
Copy the slide matching that function, and add it to our Slide Deck [Link]!
Update it with your Group Number and your names.
Fill in the slide with your answers to these questions.
Take a screenshot of your slide and submit it in the CANVAS text submit box!

Question 1 card, Reporting Effects: estimate the effect of being hit by the tsunami on income per capita, controlling for damage rates, with m equals cities piped into lm of formula income per capita against damage rate plus by tsunami, then tidier of m; report the effect using the sentence frame as X increases by 1 unit, Y increases by BETA units, with a 95 percent confidence interval from A to B, statistically significant or insignificant with a p-value of XXX

• • •

Question 10 card, Intercept when using Categorical Variables: model the effect of each year on income; which year is not represented? The intercept represents that baseline category; cities dollar year piped into unique, then m equals cities piped into lm of formula income per capita against year; calculate the predicted income per capita from 2011 to 2017

https://github.com/timothyfraser/sysen/blob/main/code/11_workshop_exercises.R

Load Data

library(dplyr)
library(readr)
library(broom)
library(texreg)
library(gtools)

cities = read_csv("workshops/jp_matching_experiment.csv") %>%
  # Tell R to treat year and pref as **ordered categories**
  mutate(year = factor(year),
         pref = factor(pref),
         by_tsunami = factor(by_tsunami, levels = c("Not Hit", "Hit")))

cities %>% glimpse()

You'll need these packages….

install.packages(c("dplyr", "readr", "broom", "texreg", "gtools"))

Load Function

# Let's write a little tidier function..
tidier = function(model, ci = 0.95, digits = 3){
  model %>% # for a model object
    # get data.frame of coefficients
    # ask for a confidence interval matching the 'ci' above!
    broom::tidy(conf.int = TRUE, conf.level = ci) %>%
    # And round and relabel them
    reframe(
      term = term,
      # Round numbers to a certain number of 'digits'
      estimate = estimate %>% round(digits),
      se = statistic %>% round(digits),
      statistic = statistic %>% round(digits),
      p_value = p.value %>% round(digits),
      # Get stars to show statistical significance
      stars = p.value %>% gtools::stars.pval(),
      # Get better names
      upper = conf.high %>% round(digits),
      lower = conf.low %>% round(digits))
}

https://github.com/timothyfraser/sysen/blob/main/code/11_workshop.R

Coding Demo

Workshop 11

https://github.com/timothyfraser/sysen/blob/main/code/11_workshop.R