Normal Curve Checks Video Guide
This will involve three short videos.  Start with the first one: https://www.learner.org/series/against-all-odds-inside-statistics/normal-curves/ (12:08)

Be sure to write in your own words any longer-answer questions.
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Period and teacher? (e.g. 4, Frazier) *
1. (Around 1:20) “To make it even easier to focus on _____________ shapes…” should help you understand the big leap we are taking from real, lumpy bumpy data displays and the Normal graphs.  (What word goes in the blank?) *
2. (Around 1:40) A benefit of making Normal curves emerges here.  You can compare completely un____________________ data sets. *
We’ll refer to any Normal curve as a model called N.
3. What centers occur at the center of N?  Pardis shows us two.
THE CENTER CHECK
What this means is that when those centers as well as mode don’t co-align in the raw data, it’s an argument AGAINST calling this distribution Normal.  This also justifies why we will only use mean when describing N (not median, mode).  YOU CAN THINK OF THIS AS THE CENTER CHECK.
4. Notice in the three bird graphs that the shape of the raw data distribution seems to “agree” with the N model.  This means the shape is ________________ (mention modality) and _____________________ (mention symmetry or skew).  YOU CAN THINK OF THIS AS THE SHAPE CHECK.
5. N is defined by mean and ___________ (spell it out), and we’ll define it this way, using the symbols for each, which you see at about 4:10: N ( μ , _____) (put symbol or spell out the word in blank)
6. To get the Year 1 and Year 33 graphs to rise to the same height, what was the y axis changed to?
7. Consider the Blackpoll Warbler graph.  N with a larger standard deviation is _____ than N with a smaller standard deviation.
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Work with "Normal Calculations" Video
Now continue with "Normal Calculations" (https://www.learner.org/series/against-all-odds-inside-statistics/normal-calculations/)  12:49 -- you can skip the example (3:20 to 6 min) if you are tight on time.

8. The 68-95-99.7 Rule (aka Empirical Rule) says that about 68% of the observations are within ____ standard deviations of the mean, _______ within ____ standard deviations of the mean, and ________ within ______ standard deviations of the mean.  THINK OF THIS AS THE 1-2-3 SD CHECK.  
9. Once we have a Normal distribution, we can calculate a statistic called the _____ - score, how many standard deviations the observation stands from the mean, and in what direction.
10. Negative z scores are observations __________ the mean.
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11. Write the general equation for the z-score.  (Use parentheses.)
12. How can we find the proportion under the curve that corresponds with a z-score.  List both tools.
13. Pause or replay the video so you can use your own textbook’s table, on page A-79 (that’s one of the very last pages of your book) as Pardis does.  We’re looking for z of 0.98 (separated by places, in the top and left margins) to find the 4-place decimal in the body of the table.  Notice the shading on the top left of your table, telling you it is returning the area in N to the left of your critical value (0.98).  What does Pardis do to find the area on the right?  NOTE: YOUR CALCULATOR WORKS TOO, BUT WHAT HAPPENS WHEN YOUR TECH FAILS YOU?  Get to know your paper table.
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Last video!  Start at 5:30.  :)
And finish with "Checking Assumption of Normality" (10:46) https://www.learner.org/series/against-all-odds-inside-statistics/checking-assumption-of-normality/, starting at 5:30

14. In a box plot, what do we look for to see if the distribution is normal?
15. The final check to determine if a distribution of raw data can be called Normal is the NORMAL PROBABILITY PLOT CHECK (which is in your textbook), which Pardis calls the Normal Quantile Plot.  If observations are Normally distributed, the Quantile Plot points will fall in a ____________________________.
16. In a Normal Quantile (Probability) Plot, observed values are on the ___ axis, and expected values are on the ___ axis.
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