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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Hello,
function minmax(int p1,int p2,int p3, int adr_big, int adr_small)
{ int mini=p1,maxi=p1;
if (p1>p2) {mini=p2;}
else {maxi=p2;};
if (p3>maxi) maxi=p3;
if (p3<mini) mini=p3;
*adr_big=maxi;
*adr_small=mini;
};
// main
int a=31,b=5,c=19,big,small;
minmax(a,b,c,&big,&small);
Answer:
C) Yes, it’s a reflection over line f.
Step-by-step explanation:
It’s a reflection over line f.
Answer:
170years
Step-by-step explanation:
R/100=2.5%
time= 100=0.025 per year (1/0.025) (5200\1000)-1=168
time=168 years
Answer:
8
Step-by-step explanation:
when multiplying two negative, it turn into a positive.
