Study Guides (390,000)
CA (150,000)
York (10,000)
ADMS (1,000)
Study Guide

[ADMS 3330] - Final Exam Guide - Everything you need to know! (68 pages long)


Department
Administrative Studies
Course Code
ADMS 3330
Professor
Michael Rochon
Study Guide
Final

This preview shows pages 1-3. to view the full 68 pages of the document.
York
ADMS 3330
FINAL EXAM
STUDY GUIDE

Only pages 1-3 are available for preview. Some parts have been intentionally blurred.

Only pages 1-3 are available for preview. Some parts have been intentionally blurred.

Chapter 16- Simple Linear Regression
- Simplest form of regression.
- One (x) and one (y) to deal with these problems
- (X) Is your independent variable.
- (Y) Is your dependent variable.
- You will have data for both (X) and (Y), you will go through regression if they are connected.
- One to one relationship we are looking at here.
- When we look for relationship that exists between (X) & (Y) we are going to draw a line that
represents that relationship.
- (+) as one goes up, other one goes down
- (-) as one goes up, other one comes down
- Zero/ no relationship- as one goes up, other one does nothing.
- Simple because we are looking at (X) & (Y), linear because we are drawing a line that represents
the relationship of (X) & (Y), or find a line.
- Regression is I’m going to regress the data in an attempt to find a relationship
- The whole thing is about looking at (X) & (Y) and trying to find a relationship that exists between
(X) & (Y) and trying to find a relationship that exists between (X) & (Y) that is our goal here. And
if we can find a relationship, we could use it to make a decision.
find more resources at oneclass.com
find more resources at oneclass.com
You're Reading a Preview

Unlock to view full version

Only pages 1-3 are available for preview. Some parts have been intentionally blurred.

- For ex- smoking and lung cancer, what is the relationship? Positive. As you smoke more, the
probability of you getting lung cancer will go up. Smoking is independent, lung cancer is
dependent, only works one way.
- Designation of (X) & (Y) is important, is how you approach the problem.
- For ex- Home Depot, Canadian tire if you start to look at the shelf, certain product have certain
shelf space and certain product have certain designated space. Retailer charges them a
premium. Eye level is the best position. Looking up is better than looking down.
- Regression Analysis is used to analyze the relationship between quantitative variables.
Quantitative variables (interval, numbers). We are looking at only interval data of (X) & (Y).
- We are hoping to find predictive model, which is called the least squares of regression line.
- What is allows us to do is predict the value of one variable which is Y, based on the other
variable is which is X. So, once I have the model done, I’ll be able to insert a value for (X) and
predict the value for (Y). Predictability of the model is using that model to see what happens
into the future.
-Main focus is to generate a line.
- Population least squared Line
- (Y)- dependent variable
- Beta 0- Y intercept (where your line hits the Y axis point)
- Beta 1- Slope of the line
- And the last one is error
- So what we have to calculate is Y-intercept and slope.
- Slope is also very important for us.
- You may know the slope as rise over run.
- Slope could be negative or positive or close to zero.
- The larger your slope the more aggressive your line is.
- When we calculate the numbers, we will be able to tell is it a positive or negative slope, then we
will be able to test the aggressiveness of the slope instead what’s called a t-test.
find more resources at oneclass.com
find more resources at oneclass.com
You're Reading a Preview

Unlock to view full version