If you were absent today, you are still expected to have the take home test on Monday!
All tests that are not turned in Monday will be docked 10 points (out of 38), 10 more for the next day, and so on. NO EXCUSES! Get it in on Monday!
By doing your take home test, you are definitely studying for Monday. Here's some more info to help you study:
- If you use your Barron's book, check the table of contents and study topics 1,2,3,5, and 11.
- Help each other! Study together!
- Use quizlet.com!
- And here's a breakdown of Monday's test:
- Identify a variable as categorical or quantitative (chapter 2)
- Reading a histogram: (chapter 4)
- Find sample size, median, describe the shape
- Determine if a data set has outliers, given the 5 number summary (fences!) (chapter 5)
- Find probabilities/%'s given a two-way (or contingency) table (chapter 3)
- Use the Normal model to find percentages (chapter 6)
- Study the "storms" problem we did in class (you also have an answer key)
- Know how shifting/rescaling affect measures of center/spread (chapter 6)
- Compare boxplots (chapter 5; study our quiz with the AP problem and rubric)
- Describe shape and spread given a dotplot
- Know how shape affects mean/median (skewed left, mean
- Independence: know how to compare two (given) percentages to determine if there is a relationship between two variables (chapter 3)
- For instance, if I tell you that 39% of students in CT take Statistics, but 66% of people students at EHHS take Statistics, then taking Stat and location are not independent--if you are at EHHS, you are more likely to take Statistics!
- Find "cutoff" values given a percentage or percentile (chapter 6)
- Find the overall mean given two samples and two averages (we had a homework question like this!
- For example...suppose period A has 20 students and a class average of 81 on a test. Period B has 27 students and an average of 84. What is the overall average for the classes combined?
- Know how to read boxplots! (chapter 5)
- Know which graphical displays are appropriate for quantitative data! (chapter 4/5)
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