Colorado State University — Pueblo; Fall 2016
Math 156, Introduction to Statistics
Common Comments on Students' Work

   Big Ideas
BI1
  1. Not so easy on this one, but try to be very specific: give a speicifc, particular fact, term, etc.
BI2  
BI3
  1. When there are multiple ideas, you really only need to give one, carefully and in detail of course.
BI4
  1. We're looking for a clear, complete specification of some particular idea or technique. E.g. "Data can be presented in different ways, such as in a table" is very general: describe exactly how to make such a table — in general, not just with an example.
  2. Careful! The frequency is the number of times a particular measurement was seen in the collected data.
  3. Always if you mention something new, describe exactly what it is, and/or how to make it. E.g., saying a table is made by "arranging data" is not specific enough: how is the data to be arranged?
BI5
  1. You don't have to (and it is not enough to) evaluate a new idea or method, saying it is nice or useful or you like it: tell the content, instead: how to make it or how to use it, very precisely and completely.
  2. Pick one thing, explain it carefully and fully (and technically).
BI6
  1. Give a general description, not (only) an example!
BI7
  1. You don't need to talk about the whole section, just one point, if you give a complete, careful description — e.g., with a formula, all of whose parts are defined and explained — of one result.
  2. Remember, we want something very specific. Discussing general terms at a very high level can be too abstract, not useful information for later work or study. (That's your goal, by the way, to summarize some part of the reading or video in a way that would be useful later.)
   Exercises
1.1.16
  1. Randomness is good (in part a.) — but why?
  2. In part b., we're not just interested in drawing an inference. The problem asks for certainty!
  3. The issue in a. is not so much that it has parameters, but that they are the same for the sample and the population. In other words, that the sample is "representative."
  4. In b., the main point revolves around how big the sample is, compared to the population.
1.3.4
  1. Remember: always at least a very minimal bit of explanation is required, even in a very simply problem like this. Say what you did to get the numbers!
2.1.4
  1. Don't skip stems in the middle of your dataset just because they have no corresponding leave. That would give the impression that the data was right next to other data, when in fact there is a gap.
  2. Don't use commas between leaves: they add extra width and make it harder to compare the rows of leaves just on how long they are.
  3. Remember: always at least a very minimal bit of explanation is required. Say what you did to get the numbers!
  4. The relative frequency histogram and frequency histograms should look almost identical, with only different labels on the $y$-axis
2.1.8
  1. Remember, total has to be 1 (=100%)
  2. Explain!
2.1.14
  1. You can also make a pie chart!
  2. Finish!
  3. Explain!
2.2.10
  1. Explain!
  2. Problem asks for mean *less than* median
2.2.12
  1. Explain!
2.2.16
  1. Explain!
2.2.26
  1. Explain!
  2. In a), not asking for total, but *per bag*...
  3. The mean is the total divided by the sample size. So other collections with the same totals and sample sizes will have the same mean. You can find examples with different medians... try it!
  4. Make examples where the total is 1003lbs...
  5. the total is not the same as the mean
2.3.12
  1. Explain!
  2. They don't all have to be 0!
2.3.16
  1. Explain!
  2. That's not what it asked for
2.3.18
  1. Explain!
  2. Arithmetic must b e wrong: these three datasets should have the same standard deviation!
   ASEs
#1