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ADMS 3330 (30)

Michael Rochon (6)

Study Guide

School

York UniversityDepartment

Administrative StudiesCourse Code

ADMS 3330Professor

Michael RochonStudy Guide

FinalThis

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ADMS 3330

FINAL EXAM

STUDY GUIDE

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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.

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- 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.

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