Multiple linear regression and inference

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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
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#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

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Why does this
matter?
We can create
linear models
with more
than one X
variable,
MULTIPLE
LINEAR
REGRESSION.
https://www.google.com/search?q=regression%20analysis%20cartoon&tbm=isch&hl=en&rlz=1C1CHBF_enAU841AU846&sa=X&ved=0CMoBEKzcAigAahcKEwiIuZO7tMPuAhUAAAAAHQAAAAAQAg&biw=1013&bih=433#imgrc=mWMAV2iiX–veM
We can also
estimate,
(Confidence
intervals)
and
test
(Hypothesis
testing)
regression
statistics.
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#1 Assumptions in linear regression
https://line.17qq.com/articles/ncpkdmmlv_p3.html
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#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
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Pty Ltd) – 9781442549272/Berenson/Business Statistics /2e

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