### Multiple linear regression and inference

1
1
STAM4000
Quantitative Methods
Week 10
Multiple linear regression and
inference
http://claudiaflowers.net/rsch8140/Lec1.html
MLR, here we have:
Many X variables
One Y variable
Kaplan Business School (KBS), Australia 1
2
COMMONWEALTH OF AUSTRALIA
WARNING
This material has been reproduced and communicated to you by or on behalf of Kaplan
Business School pursuant to Part VB of the
Copyright Act 1968 (the Act).
The material in this communication may be subject to copyright under the Act. Any further
reproduction or communication of this material by you may be the subject of copyright
protection under the Act.
Do not remove this notice.
2
Kaplan Business School (KBS), Australia 2

 3 r d #1 #2 Assumptions in linear regression Multiple linear regression, MLR Inference in regression, Confidence intervals and hypothesis testing of population regression coefficients or slopes #3 Week 10 Multiple linea regression an inference Learning Outcomes

Kaplan Business School (KBS), Australia 3
4
Why does this
matter?
We can create
linear models
with more
than one X
variable,
MULTIPLE
LINEAR
REGRESSION.
We can also
estimate,
(Confidence
intervals)
and
test
(Hypothesis
testing)
regression
statistics.
Kaplan Business School (KBS), Australia 4
5
#1 Assumptions in linear regression
https://line.17qq.com/articles/ncpkdmmlv_p3.html
Kaplan Business School (KBS), Australia 5
6
#1 Assumptions in linear regression
Use the acronym LINE
Linearity: the underlying relationship between X and Y is linear
Independence of errors: error values are statistically independent
Normality of error: error values (ε) are normally distributed for any given
value of X
Equal variance (homoscedasticity): the probability distribution of the errors
has constant variance
Copyright © 2013 Pearson Australia (a division of Pearson Australia Group
Pty Ltd) – 9781442549272/Berenson/Business Statistics /2e

Note: