1. List at least three examples that fit the definition of
aggression, and at least one that does not. (Examples
can be hypothetical or real)
2. Why do people deny the harmful effects of violent media when
the research evidence linking violent media to aggression is so
conclusive?
3. Consider the various causes of aggression described in this
module and elsewhere, and discuss whether they can be changed to
reduce aggression, and if so how
For a general second order control
system. On a complex plane graph, with real and imaginary axes,
show the region where the roots of a stable second order control
system must lie in order to satisfy the following performance
conditions:
Settling time between 0.4 and 0.8 seconds
Peak overshoot below 5%
Frequency of oscillation below 8 rad/sec
Some statisticians prefer complex models, models that try to fit
the data as closely as one can. Others prefer a simple model. They
claim that although simpler models are more remote from the data
yet they are easier to interpret and thus provide more insight.
What do you think? Which type of model is best to use? When
formulating your answer to this question you may think of a
situation that involves inference that you do and need to present...
chi square goodness of fit test
Epistasis is a phenomenon in which one gene controls the
expression of another. In one case, a dihybrid cross should yield a
9:3:4 ratio. You collect the following data on mice: black coat: 69
brown coat: 23 white coat: 30 Is there any reason to doubt the
ratio of 9 black : 3 brown : 4 white?
My professor asked us if the least square estimated regression
equation is indeed the best unique linear combination of predictor
variables. I said yes but he asked me if there are other potential
linear combination of the predictor variables that may work that
could provide insight? he then asked that I give an example. what
other linear combinations are there???
*Please provide r studio code/file*
1) Find the equation of the best fit line using least
squares
linear fit of x,y:
set.seed(88)
x <- 1:100
y <- jitter(1.5*x+8,amount=10)
2) For question 1, Draw the P=0.95 prediction intervals for y
when x=1:150
3) For question 1, Find the equation of the best fit line
using
median-based linear fit of x,y.
4) For question 3, draw the P=0.95 prediction interval for y
# when x=1:150