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ABOUT THE CLASS
The course syllabus
Student notes packet
LABS and LAB KEYS
NOTE: LABS MUST BE SAVED IN pdf FORMAT AND EMAILED TO email@example.com
Label all problem numbers in labs clearly and put problems in order!
Exam 1 key
Exam 1 grades
HELPFUL RANDOM STUFF
6.2 homework, #13
Calculating several z-scores at once using StatCrunch
HERE'S A STATCRUNCH STEP-BY-STEP GUIDE!
***I constructed these step-by-step StatCrunch guides for YOU! Please use them!
The binomial and Poisson calculators (chapter 6) in StatCrunch
The normal calculator (chapter 7) in StatCrunch
The normal calculator in StatCrunch for chapter 8
Calculating confidence intervals and sample sizes with StatCrunch
Conducting a hypothesis test for a standard deviation with StatCrunch
Why the symbol in H1 is crucial in hypothesis testing
Regression with StatCrunch
Here's a website that follows our book and has step-by-step examples with StatCrunch!
Hypothesis test/confidence interval/sample size formula flowchart (good for the base of your final exam note sheet)
Log on to Course Compass
PowerPoint lectures with the author of our text, Michael Sullivan
Excellent statistics help by Khan Academy
More stats help from Wolfram Alpha
The mathematics behind the Birthday Problem
Some Normal distribution examples. The first is an application of IQ scores as used by NVC's learning specialist and the second is how normally distributed global temperatures are changing due to global warming. In the second example, note how the change in the mean shifts the distribution left/right while the change in standard deviation changes the shape/width of the curve.
VIDEOS (These are kind of out-of-date, but the concepts are the same)
and (there are two) sections 2.4-3.1- https://youtu.be/iuaj-4njsts
and (there are 3)- https://www.youtube.com/watch?v=gEEC9JEqDss
Video 1 starts with additional examples of The Emperical Rule and Chebyshev's Theorem
Video 1 - Quartiles and the five-number summary
Video 2 starts with section 3.4
Video 3 starts with section 3.5. We compare the two data sets.
These regression videos are based on these data sets: commute time vs. well-being, bear length vs. bear weight, credit score (FICO) vs. interest rate.
Video 1 is half of the class where the other video was consumed by the camera. Greedy bugger. It's about r values and scatterplots.
Video 2 starts with some homework questions about r values and z-scores. We discuss the overall steps of the linear regression process.
Video 3 starts with some estimation and goes through the total overall error.
Video 4 begins with a regression homework problem and finishes up the process started in video 3.
This video is all about probability, most of chapter 5.