Answer:
Hence P values of beta becomes smaller(< 0.0001). and doest affect the mean response
Step-by-step explanation:
Given:
AS Predictor become more highly correlated .
To find:
Descriptive Nature of high correlated Predictor .
Solution:
A predictor is high correlated means:
1)It means that the two variables are strongly related to each other.
2)This is also called as problem of multicollinearity when two variables are
in Regression.
Effects when predictor are highly correlated ;
- <em>The estimated coefficient of one any one variable depends on the other predictor variable in model.</em>
- <em>Estimated coefficient of regression decrease as predictor variables are added.</em>
- <em>Hypothesis test Beta = zero gives different conclusion depending upon variable.</em>
- <em>High correlated of predictor variable does not provide good precision of predication of response in within model.</em>
In short ,Mulitcollinearity does not affect the mean response and new response of the model.
Hence P values of beta becomes smaller(< 0.0001). and doest affect the mean response
Answer:
- the way its set up its kind of confusing ↑
Step-by-step explanation:
Find the volume
volume=hpir^2
d/2=r=16/2=8
1.
hmm, ok, after 1/2 hour or 30 mins
30/5=6
6*1=6, 6 cubic meters
volume=6, solve for height
v=hpir^2
but r=8 all the time, solve for h
6=hpi8^2
6=hpi64
6/(64pi)=h
the depth is
meters or about 0.02984155182973037545666570563235 meters
the depth is about 0.03 meters
2. the full volume is
v=6pi8^2=6pi64=384pi
1 cubic meter every 5 mins so
384pi/1 is how many 5 mininute intervals
so 384pi times 5=total minutes=1920pi minutes or about 6031.8578948924030178482752958966minutes