Tuesday = Study + Stamps!

You have two responsibilities tonight:

1.) Chapter 8 Vocab Quiz tomorrow!
2.) STAMPS are due tomorrow! Don't get a 0!

Here's the list for our chapter 8 vocab quiz:

• Explanatory Variable
• Response Variable
• Lurking Variable
• Correlation
• Residual
• Graphically
• How its calculated
• What it measures
• Underestimate (what is it? what type of residual represents an underestimate?)
• Overestimate (what is it? what type of residual represents an overestimate?)
• Slope (how is it interpreted?)
• Y-Intercept (how is it interpreted?)
• Coefficient of Determination (know this is R^2, know how to interpret this value)
• Predicted Value (y-hat, or what do we use to find a predicted value?)
• Linear Model
• "An equation of a line in the form y-hat = a + bx that is used to make predictions of the response variable for given values of the explanatory variable."
• Least Squares Regression Line (what does best fit mean?)
• Is a linear model appropriate? (what 3 things do we check)

And here's the process for turning in your stamps:

• You can turn them in as early as Monday--if we have stamps Monday/Tuesday I'll just add them to your total
• Count up your stamps!
• You are literally counting stamps--so if there's no stamp on a paper it doesn't count! If there are two, that's 2 stamps!
• Get all your stamps together--put them in a ziploc bag, a folder, staple them together--whatever works
• Write your name and the total on the front! 
• Double check your counting...
• I will randomly select 5 students in each class to check totals...
• If your total is incorrect you will be given a 0--so pay attention to detail and double check when you count!
• You will get all of these papers back so don't worry if there are notes on them!

Finally, here's tomorrow night's homework for those of you who are feeling ambitious tonight--this will be the first homework grade of the second quarter!

Monday HW

Help! Rojaun Samuda has an accountability for last year's book, but he probably turned it in...

PLEASE look inside your book to see if you have Rojaun's from last year, or if you have book # AS-09-108! If so, please send me a Remind message or let me know in class tomorrow! Help a fellow classmate out!

Tonight please complete the following in your textbook (lots of residual and predicting practice):

Page 193-196: 35cg, 39acd, 41g, 47bde

Today in class we discussed what "best fit means," did a practice/stamp problem working with residuals, and started to explore residual plots. Tomorrow we'll wrap up chapter 8 with more discussion of residual plots and "Is a linear model appropriate?" questions!

On Wednesday we'll start class with our chapter 8 vocab quiz, then we'll start out chapter 9 notes!

Have an awesome Monday! See you all tomorrow!

STAMPS ARE DUE WEDNESDAY!

Here's the process for turning in your stamps:

• You can turn them in as early as Monday--if we have stamps Monday/Tuesday I'll just add them to your total
• Count up your stamps!
• You are literally counting stamps--so if there's no stamp on a paper it doesn't count! If there are two, that's 2 stamps!
• Get all your stamps together--put them in a ziploc bag, a folder, staple them together--whatever works
• Write your name and the total on the front! 
• Double check your counting...
• I will randomly select 5 students in each class to check totals...
• If your total is incorrect you will be given a 0--so pay attention to detail and double check when you count!
• You will get all of these papers back so don't worry if there are notes on them!

Weekend HW (Counts TRIPLE!!!!)

This weekend please COMPLETE and SCORE the 2016 Investigative Task (#6) provided in class!

• This homework will be counted triple! This will be one of our last homework grades of the quarter, so let's get it done and make it count (triple)!
• One of the biggest challenges on the AP Stat Exam is the Investigative Task; this is our first exposure to this type of question
• The Investigative Task is designed to take 25 minutes and counts twice as much as a "regular" free response
• The Investigative Task is also designed to incorporate things you have not directly learned in class; you are challenged to take your statistical knowledge and apply these ideas in a new context
• You'll see some familiar stuff, and some "new stuff..."
• For example, in this question we definitely learned what we need to know to answer part a, b, and c
• Parts c, d, and e challenge us to use ideas we've learned in class in new ways! 
• The key to success on the Investigative Task is to TRY! Don't leave anything blank; some of this stuff is supposed to look foreign to you--use your knowledge of Stats to come up with some solution!

Homework Step 1: Complete the 2016 Investigative Task! (if you really want some authentic practice give yourself a 25 minute timer! I think most of us could do this one in less than 25 minutes!)

Homework Step 2: Score your 2016 Investigative Task

• Read the 2016 AP FR Scoring Rubric (click me!)
• First, read through the entire solution examples and scoring rubric (pages 17-20)
• Then, go back and re-read the scoring sections
• Score each section (section 1 = a/b, section 2 = c/d, section 3 = e) as E (essentially correct), P (partially correct), or I (incorrect)
• Then use page 20 to give yourself an overall score of 1-4 based on your scores on the 3 sections
• USE A DIFFERENT COLOR PEN/PENCIL TO SCORE YOUR RESPONSES!

Here's the FR question in case you lost yours or were out:

STAMPS ARE DUE WEDNESDAY!

Here's the process for turning in your stamps:

• You can turn them in as early as Monday--if we have stamps Monday/Tuesday I'll just add them to your total
• Count up your stamps!
• You are literally counting stamps--so if there's no stamp on a paper it doesn't count! If there are two, that's 2 stamps!
• Get all your stamps together--put them in a ziploc bag, a folder, staple them together--whatever works
• Write your name and the total on the front! 
• Double check your counting...
• I will randomly select 5 students in each class to check totals...
• If your total is incorrect you will be given a 0--so pay attention to detail and double check when you count!
• You will get all of these papers back so don't worry if there are notes on them!

And finally, here's an extra credit stamp you can try (and turn in on Monday)--I'll give you one stamp per (completely) correct response!

• When you have to interpret the residual in a sentence (part c)  be sure to reference what value you predicted for, what you were predicting, and if the prediction was an over or underestimate
• Use this example based on #4 from yesterday's stamp (predicting income based on years experience)...
• Residual for 0 years experience = $-5K
• "The predicted income for 0 years experience was overestimated by $5K."
• The calculator screen shows the LinReg results for this data (to save you some time)
• 
  

Quiz Tomorrow!

Tonight please complete the "Pop Quiz: Interpreting Bivariate Statistics" worksheet provided in class (or below)!

• This was the original version of your quiz! If you can interpret the values in 2a-d correctly you should be ready to ace tomorrow's quiz!
• You will have to read a computer output tomorrow to determine the values of slope, intercept, and R^2.
• For #1 (this is not on your quiz tomorrow) you should check that both variables are quantitative and create a scatterplot to see if it's roughly linear
• I will not check this homework; this is your practice so that you can get an A tomorrow! 15 minute quiz to start class!

Here are the questions:

And here's the answer key so you can check your work:

• You didn't have question 3 on your homework but feel free to check it out if you're curious!

Tomorrow in class we'll start to get into our last main idea for this chapter--we have to learn lots about residuals...

• How do we calculate residuals?
• How do we graphically represent a residual?
• What does a +/- residual tell us?
• How do we create residual plots? 
• How do we read residual plots? What do we use them for?
• How do we determine if a linear model is appropriate?

Wednesday and Thursday HW

Tonight, please complete the following in your textbook:

Page 190-196: 15b, 36ab, 41b, 47f

• Period A: notice this book assignment is shortened from class!
• All of the answers are provided below so you can check (using the same type of wording I taught you in class)
• 15b, 41b, and 47f all ask you to interpret R^2--use the writing template provided today in class (or below)!
• Here are the homework answers so you can check:
• 15b.) About 92.4% of the variability in nicotine content (mg) in cigarettes can be explained by variability in the amount of tars (mg). Other factors may include the type of cigarette.
• 36a.) A linear model is appropriate because the scatterplot appears roughly linear and both variables, fat (g) and # of calories, are quantitative.
• 36b.) There is a strong association between amount of fat and number of calories in chicken sandwiches because r = 0.947 (which is close to 1).
• 41b.) About 33.4% of the changes in mean annual temperature (degree Celsius) can be explained by changes in the mean annual CO2 concentration in the atmosphere (ppm). Other factors may include
• 47f.) About 43% of the variability in annual mortality rate (deaths per 100,000) for males in England and Wales can be explained by changes in calcium concentration (water hardness), measured in parts per million (ppm).

• *Notice that when I wrote my context I did not simply copy the variable names off of the scatterplot--read the context and be more thorough than what's provided on the graph!

• I'm starting to second guess needing the "other factors" part for the AP exam when interpreting R^2...notice I left this off. I am going to reach out to some other Stat teachers and try to find out if we need this with our interpretation.

Here's what we covered today in class:

• Stamp problem = practice with reading computer outputs
• Homework questions?
• Next we intepreted the slope and y-intercept for the computer output we worked with yesterday in class (# of passenger seats vs. operating cost per hour) so that we would have one more example of these interpretations in our notes
• Then we focused on the meaning of R^2, or the Coefficient of Determination...
• We watched the AP Stat Guy video about R^2 in class (watch it)! (unit 2, video #9)
• We also discussed how to interpret R^2--here's a writing template you can use for your homework/quizzes/tests/etc
• "__R^2__% of the variation in __(response variable)__ can be explained by variation in __(explanatory variable)__."

• Here's an example based on the computer output:
• "Approximately 57% of the variation in operating cost per hour can be explained by variation in the number of passenger seats. Other factors may include the weight of the plane or the length of the trip."

Tomorrow in class we'll wrap up any questions we have about slope, intercept, and R^2 and we'll start to explore residuals! See you there! On Friday we'll start class with a (15 minute) quiz--all writing/interpreting!

And if you're feeling ambitious here's tomorrow night's homework--all practice to get ready for our quiz Friday! (this assignment is actually just a different version of the quiz)

Tuesday HW!

Tonight please complete the following in your textbook--this provides us with some essential practice with slope and y-intercept, as well as reading a computer output--all things we discussed today in class!

Page 189- 196: 13,  41acde, 47abce

Here's what you should be able to do after tonight's homework for this unit--if you can do all of this you're in good shape!

• Identify explanatory and response variables
• Create a scatter plot, find correlation (r), R^2, and write the equation of the line of best fit using your graphing calculator
• Understand correlation (r): what does it measure? what types of variables should we use? does it have units? what is strong/weak? 
• Describe an association (in context, of course)
• Interpret r = describe the strength and direction of an association
• Use the LSRL equation to make a prediction (substitute for x)
• Identify/consider possible lurking variables
• Read a computer output to identify r, R^2, slope, and y-intercept
• Interpret slope
• Interpret y-intercept

Tomorrow in class we'll continue with our chapter 8 notes--we'll learn add one more example of slope/y-intercept to our notes and we'll discuss how to interpret R^2. Then, we'll start to learn what "best fit means." We'll continue with these ideas on Thursday and Friday, aiming to finish chapter 8 by next Tuesday/Wednesday.

See you there!

Weekend Homework!

Tomorrow we have our chapter 7 quiz (or quizzes)--vocab and math! Tonight--study! You should also complete the textbook problems listed below if you did not already do so:

Page 160-166: 1, 5, 11, 13, 25, 29, 31

Here's an outline of the content on the math quiz:

• Identify explanatory and response variables
• Create/sketch scatterplot using graphing calc.
• Find the correlation using graphing calc.
• Describe an association (like Thursday's homework)
• Write the equation of the LSRL (least squares regression line) using calc
• Use the equation to make a prediction
• Identify mistakes made in statements about correlation (must have 2 quantitative variables, correlation is between -1 and 1, no units on correlation, know what a strong/weak correlation looks like)
• Identify a possible lurking variable

And here's the chapter 7 vocab list:

• Scatterplot
• Explanatory Variable
• Response Variable
• Lurking Variable
• Correlation
• Positive Association (as x increases, y increases)
• Negative Association (as x increases, y decreases)
• y-hat (represents a predicted value)

Finally, here are the answers to Thursday night's homework so you can check yours and use these to study--remember, this is definitely on our quiz tomorrow!

• Precipitation v. Cucumber Yield
• The association between the amount of precipitation (mm) and cucumber yield (kg per square meter) is pretty strong, roughly linear, and positive. Based on this scatter plot, as the amount of precipitation increases, the predicted cucumber yield increases.
• Missed Classes and Exam Score
• The association between the number of missed classes and exam score is moderately strong, negative, and roughly linear with r = -0.79. Based on this data, as a student misses more classes, his/her predicted exam score decreases. (Or, based on this data, as the number of missed classes increases the predicted exam score decreases).