Don't have my book or audio?  You can download a free sample of my book and audio lectures containing Lesson 1:

You will be using Table A for much of the rest of this assignment.  Here is a link where you can download the table if you have not already done so:

Be sure to thoroughly study and practice my Lesson 4, #5 and #6 before you attempt these two questions.  Be hard on yourself!  You cannot be satisfied until you can repeatedly get 100% correct on my #5 and #6 (do each question at least three times with perfect accuracy). 

I strongly recommend you read my section in Lesson 4 about the Z Bell Curve Ladder and the X Bell Curve Ladder and make the ladder every single time you have a bell curve problem.  Then climb up or down the rungs.  Many students are guilty of not thinking a problem through, and consequently looking at Table A too soon.  The ladder trains you to focus on the fact that Table A deals with z scores and Left Areas, but your problem may be interested in something else.

Make sure you have studied all my Z-Bell and X-Bell Curve problems (Lesson 4, #5 to the end) before you attempt this question.  Make sure you use the X-Bell Curve Ladder to help you work your way through each part of this question.

Again, make sure you have read their hint back in question 6!  Fast students take LESS time to complete an exam.  Slow students take MORE time.

Make sure you have studied Lesson 3 in my book before you answer this and the remaining questions in this assignment.  You should especially look at Lesson 3, #6-11 as illustrations of the Three Principles of Experimental Design and examples of identifying the various factors, factor levels, treatments, experimental units, and response variable for an experiment.  As well as identifying what type of experiment it may be (randomized comparative experiment, block design, matched pairs design).

When they ask for the treatments (part (b)), tell them not only how many treatments there are in the experiment, but what the exact treatments are.  For example, in my Lesson 3, #7(b), I wouldn't just say that there are 6 treatments.  I would say the 6 treatments are: Dog Food A served early; Dog Food B served early; etc. up to Dog Food C served late.

Sometimes, students confuse blocks with factors.  A factor is split into levels that will become the treatments in an experiment.  Blocks are pre-existing conditions that separate some experimental units from others, that we think may also affect the outcomes in an experiment. 

A good rule of thumb is to remember that you can randomly assign treatments, but you cannot randomly assign blocks.  I cannot randomly designate who is a man and who is a woman in my experiment, so gender cannot be a factor in an experiment, but, if I am concerned that results may be affected by gender, I can use gender as a blocking variable, so that I can separate the effects of men vs women.  I cannot randomly assign who has severe arthritis and who has mild arthritis, so, if I am concerned that the severity is an issue, I use severity of arthritis as a blocking variable.  But, I can randomly decide who gets 2 pills and who gets just 1 pill, so strength of dosage would be a factor, split into levels to make randomly assigned treatments.

Here are some extra things to clarify the three principles of experimental design which you may be asked to discuss in questions on an exam (but were not asked in this assignment). 

Note that randomization is used in experiments to randomly determine which unit gets which treatment (when there are many units and each unit will be given exactly one treatment), or to randomly determine the order the treatments will be administered (when one unit is going to receive two or more treatments).

When discussing the principle of control, there is no need to speculate.  Discuss the actual things they have obviously done to control outside factors or certainly should have done.

By repetition, they mean what I call replication; quite simply: how many times is each treatment being applied?

Note also that we learned in Lesson 2 that correlation does not imply causation.  Just because a pattern is observed between x and y does not mean we have proven that x causes y.  But, the whole point of designing an experiment is to identify possible cause and effect.  If an experiment has been designed properly, we have every right to believe we have proven that blank causes blank, provided we have seen a significant difference in the response variable, when applying one treatment as compared to another.