Method of Least Squares

STAT 20: Introduction to Probability and Statistics

Agenda

• Announcements
• Concept Questions
• Problem Set 16: The Method of Least Squares
• Lab 5.1: Understanding the Context of the Data

Announcements

• Problem Sets:
  • PS 16 released Tuesday and due next Tuesday at 9am
  • Extra Practice released Thursday (non-turn in)

• Lab 5:
  • Lab 5.1 released Tuesday and due next Tuesday at 9am
  • Lab 5.2 released Thursday and due next Tuesday at 9am
  • Lab 5 Workshop next Monday

Concept Questions

Concept Question 1

An engineer working for Waymo self-driving cars is working to solve a problem. When it rains, reflections of other cars in puddles can disorient the self-driving car. Their team is working on a model to determine when the self-driving car is seeing a reflection of a car vs a real car.

Think of a potential response and predictor, and about whether this is a regression or classification problem.

01:00

This is a serious challenge encountered by self-driving cars at the moment. This is probably best thought of as a classification problem, with the response being either reflection / not or puddle / not. The predictors could be sensor input (cameras, lidar) as well as weather info (to know if it’s been raining).

Concept Question 2

• Here is a function f.

f <- function(x, y) {
  y*(x + 3) 
}

What will the following line of code return?

f(3,5)

This question reminds students that if function arguments are unnamed, they are assumed to have been input in the order they were originally specified as in the function definition. x will be assigned 3, and y will be assigned 5, so the correct answer is 30.

Problem Set

25:00

Break

Lab

25:00

End of Lecture