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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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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

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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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