STOR 155 Lecture Notes - Lecture 1: Statistical Inference
13 Jan 2018
School
Department
Course
Professor
Document Summary
Next week"s income: deterministic: /hr flat rate pay, will work for 30 hrs next week, income next week. Floor tiles 4 squares: deterministic: room 8" x 10", how many tiles needed, 80 x 9 = Other examples of random: tips, waiter, commission, financial markets, anything involving biological phenomenon. Weather, can"t predict is precisely: chemistry and physics, anything involving human behavior, gambling, games of chance, sports. X = diameter of individual rock (output) (mm: between 30mm(a) and 50mm(b, more dispersion vs more concentrated w 40mm to 50mm. Make sure structure and parameter makes sense: for this example, the number can"t be negative bc it"s a rock, temperature, stock market changes, annual profits, etc can have negative values. A cannot be higher than b ! If were only given x values, we can go around the other way of the paradigm and guess our a and b values: look at min and max values, statistical inference.
