Question 1201156
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I'll provide the answers first at the top of the page.
The next section will do a deeper dive explaining things.


Answers:
<ul><li><font color=red>null hypothesis</font> {{{mu[matrix(1,3,"","",A)] = matrix(1,2,"",mu[matrix(1,3,"","",B)])}}} aka {{{mu[matrix(1,3,"","",A)]-matrix(1,2,"",mu[matrix(1,3,"","",B)])= matrix(1,2,"",0)}}}</li><li><font color=red>alternate hypothesis</font> {{{mu[matrix(1,3,"","",A)] <> matrix(1,2,"",mu[matrix(1,3,"","",B)])}}} aka {{{mu[matrix(1,3,"","",A)]-matrix(1,2,"",mu[matrix(1,3,"","",B)])<> matrix(1,2,"",0)}}}</li></ul>mu = {{{mu}}} is used for the population mean.
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Explanation:


Standard practice with population parameters generally involves Greek letters.
Examples:<ul><li>Greek letter mu = {{{mu}}} = population mean</li><li>Greek letter sigma = {{{sigma}}} = population standard deviation</li><li>Greek letter rho = {{{rho}}} = population correlation coefficient</li></ul>and so on. We use the lowercase version of each letter.


The one exception to this rule, that I can think of anyway, is the use of 'p' for the population proportion (the sample version is phat). 


We're looking at the mean weights so we go for mu or {{{mu}}}. 
Both "mean" and "mu" start with M, which is one way to remember the connection.


If we want to talk about two (or more) different population means, it's common to attach a number to the bottom right corner of this variable.<ul><li>mu1 = {{{mu[matrix(1,3,"","",1)]}}} = population mean of group 1</li><li>mu2 = {{{mu[matrix(1,3,"","",2)]}}} = population mean of group 2</li><li>etc</li></ul>Some textbooks will use letters instead of numbers.<ul><li>muA = {{{mu[matrix(1,3,"","",A)]}}} = population mean of group A</li><li>muB = {{{matrix(1,2,"",mu[matrix(1,3,"","",B)])}}} = population mean of group B</li><li>etc</li></ul>To be even more descriptive, the letters can be replaced with short names.
Examples:<ul><li>mu_dogs = {{{mu[matrix(1,3,"","",dogs)]}}} = population mean of dogs' weight</li><li>mu_cats = {{{mu[matrix(1,3,"","",cats)]}}} = population mean of cats' weight</li></ul>It will depend on context which is the better format.
Statistical software like Minitab often uses a format similar to the last set of examples shown above.


Since your teacher is involving letters, I'll go for that style. 


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The instructions mention "<font color=blue>In an experiment to determine whether there is a systematic difference between the weights</font>"
Meaning that the researcher wants to test the claim if {{{mu[matrix(1,3,"","",A)] <> matrix(1,2,"",mu[matrix(1,3,"","",B)])}}} is the case or not.
The flip of that statement would be {{{mu[matrix(1,3,"","",A)] = matrix(1,2,"",mu[matrix(1,3,"","",B)])}}}


For each statement, we can optionally subtract {{{mu[matrix(1,3,"","",B)]}}} from both sides.
Eg: {{{mu[matrix(1,3,"","",A)] = matrix(1,2,"",mu[matrix(1,3,"","",B)])}}} becomes {{{mu[matrix(1,3,"","",A)]-matrix(1,2,"",mu[matrix(1,3,"","",B)])= matrix(1,2,"",0)}}}


Why do we bother with this subtraction? It's to conduct a hypothesis test on the difference of the population means. 
Treat that difference as a new random variable on its own. 


Rule: The null hypothesis <b><u>ALWAYS</u></b> has the equal sign. 
This is to lock the parameter(s) into one spot and set up one distribution.


Therefore,<ul><li><font color=red>null hypothesis</font> {{{mu[matrix(1,3,"","",A)] = matrix(1,2,"",mu[matrix(1,3,"","",B)])}}} aka {{{mu[matrix(1,3,"","",A)]-matrix(1,2,"",mu[matrix(1,3,"","",B)])= matrix(1,2,"",0)}}}</li><li><font color=red>alternate hypothesis</font> {{{mu[matrix(1,3,"","",A)] <> matrix(1,2,"",mu[matrix(1,3,"","",B)])}}} aka {{{mu[matrix(1,3,"","",A)]-matrix(1,2,"",mu[matrix(1,3,"","",B)])<> matrix(1,2,"",0)}}}</li></ul>This is a two-tailed test because of the "not equals" in the alternate/alternative hypothesis.


I'll stop here since it seems like all you need are the null and alternate hypotheses. 
Let me know if have further questions. Or please make a new post on this website.
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