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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45 90
/ \ / \
5 9 45 2
/ \ / \
3 3 5 9
5x3x3 / \
3 3
2x5x3x3
The answer would be b - associate w it
Answer:
Step-by-step explanation:
PART 1
y=mx+b
m is slope and slope is 0 sooo
y=0x+b
it passes through (3,7)
7=0(3)+b
7=0+b
b=7
y=0x+7
so just y=7
PART 2
y=mx+b
y=1/5x+b
1/2=1/5(-2/3)+b
1/2=-2/15+b
1/2+2/15=b
15/30+4/30=b
19/30=b
y=1/5x+19/30
