The assumptions of a regression model can be evaluated by plotting and analyzing the error terms.
Important assumptions in regression model analysis are
- There should be a linear and additive relationship between dependent (response) variable and independent (predictor) variable(s).
- There should be no correlation between the residual (error) terms. Absence of this phenomenon is known as auto correlation.
- The independent variables should not be correlated. Absence of this phenomenon is known as multi col-linearity.
- The error terms must have constant variance. This phenomenon is known as homoskedasticity. The presence of non-constant variance is referred to heteroskedasticity.
- The error terms must be normally distributed.
Hence we can conclude that the assumptions of a regression model can be evaluated by plotting and analyzing the error terms.
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Answer:
<em> Anna's wand can cast 70 spells</em>
<em></em>
Step-by-step explanation:
Elsa's wand can cast 53 spells
her wand casts 17 fewer cells than her sister Anna's
Amount of spells cast by Anna's wand = ?
We write the question down in the form of an equation
where 
is the amount of spells Anna's wand can cast.
we then proceed to solve by collecting like terms to different sides of the equation. We'll have
which leaves us with
This means that<em> Anna's wand can cast 70 spells.</em>
Answer:
13
Step-by-step explanation:
using BODMAS
13-11=2
SO 2*2=4
4/4=1
1*72=72
THEREFORE it is 85-72 =13
Answer: 32
Step-by-step explanation:
#of sides - 3 is 35-3=32
