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Wednesday, October 31, 2018

Happy Halloween!

If you were absent yesterday or today you must make up your quiz(zes) before H period Friday or you will receive a 0! Quarter closes Friday!

1.) Please complete the "Residual Plot Critical Thinking Questions" provided in class 
  • These will be checked/discussed in class Friday, so if you're going out tonight you can do this tomorrow...
  • However, you WILL have homework tomorrow as well! (Posted below if you're curious). 


2.) STAMPS ARE DUE YESTERDAY! If you did not turn in your stamps today (absent, forgot, etc.) BE SURE YOU HAVE THEM TOMORROW!
  • Gather all your stamps--you can leave them on the full paper, or rip off the little stamp, whatever is easier
    • You will get all of these back, so if you have notes on a paper with a stamp, don't worry, you'll get it back
    • If you count your stamps in class you will be given a zero--this is your responsibility OUTSIDE the classroom
  • Count all your stamps--double check!
    • I will choose 5 random names from each class and double check those students' stamps; if your total is incorrect, you will earn a score of 0
  • Fasten all your stamps--put them in a ziploc bag, put them in an envelope, staple them all together, or maybe glue/tape all the cut out stamps to one paper--whatever works best for you
  • Write your name and the total on the front of your stamps
  • Turn this in by Wednesday!
  • Stamps count as a classwork grade!
    • For each class I will find Q3 and use this to determine how many points the stamp grade is out of, so I can't tell you how many you need....
    • It doesn't really matter how many you need anyway, you just have to turn in all your stamps!

3.) Thursday HW:

Page 214: 7, 9
  • Do this thoroughly! Write your answers in as much detail as you would on the AP exam--that's why I'm only giving you two, I'd rather have you take the time to do these two questions the right way, rather than doing lots of questions in a rush! And always score/grade your homework using the answers in the back of the book!

Today's Class Recap:
  • Chapter 8 Vocab Quiz
  • Completed 2015 AP Free Response with a partner
  • "Peer Assess"
    • Scored a classmate's free response using the AP rubric
  • Completed 5 AP MC (classwork grade)

Tomorrow it's on to chapter 9--see you there!
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Tuesday, October 30, 2018

Two's Day HW

Tonight, please complete the following:

1.) Count up your stamps and have them ready to turn in at the start of class tomorrow (instructions below).

2.) STUDY! Tomorrow = chapter 8 vocab quiz (list below)


STAMPS ARE DUE WEDNESDAY!
  • Gather all your stamps--you can leave them on the full paper, or rip off the little stamp, whatever is easier
    • You will get all of these back, so if you have notes on a paper with a stamp, don't worry, you'll get it back
    • If you count your stamps in class you will be given a zero--this is your responsibility OUTSIDE the classroom
  • Count all your stamps--double check!
    • I will choose 5 random names from each class and double check those students' stamps; if your total is incorrect, you will earn a score of 0
  • Fasten all your stamps--put them in a ziploc bag, put them in an envelope, staple them all together, or maybe glue/tape all the cut out stamps to one paper--whatever works best for you
  • Write your name and the total on the front of your stamps
  • Turn this in by Wednesday!
  • Stamps count as a classwork grade!
    • For each class I will find Q3 and use this to determine how many points the stamp grade is out of, so I can't tell you how many you need....
    • It doesn't really matter how many you need anyway, you just have to turn in all your stamps!

Chapter 8 Vocab Quiz Wednesday!

  • Residual (what does it measure? how is it calculated? how is it represented visually on a scaterplot?)
  • Actual Value (y): based on given data or the point in a scatterplot
  • Predicted Value (y-hat): generated by substituting into the LSRL equation or looking at the line of best fit
  • Interpret Slope
  • Interpret Y Intercept
  • Coefficient of Determination = R^2
  • Interpret R^2
  • Correlation
  • Underestimate (connect to residuals)
  • Overestimate (connect to residuals)
  • Linear Model
  • Least Squares Regression Line (LSRL)
  • "Is a linear model appropriate?" (what do we check?)
  • Explanatory Variable
  • Response Variable
  • Scatter plot
  • Lurking Variable

Today's Class Recap:
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Monday, October 29, 2018

QUIZ TOMORROW!


Today's Class Recap:

Monday Homework: 

1.) Correct your free response from class using the answers below.
2.) Start counting your stamps--you can turn them in tomorrow, as we won't have a stamp tomorrow (quiz to start class)
3.) Start studying your vocab!
  • Here's what you need to score your free response from class:
    • Use a different color pen/pencil to make any corrections or add anything that was missing! This is review for our quiz!
    • Study more! You can use the key from Friday's blog post to check your Thursday hw responses as well!
    • Classwork Key:
      • a.) predicted % weeds killed =  - 20.5893+ 24.3929(# tsp. weed killer)
        • Common mistake: remember to define your variables!
      • b.) Slope:
        • According to the LSRL, for each additional 1 teaspoon of weed killer (used), the predicted % of weeds kills increases by roughly 24.3929%.
      • b.) Y Intercept
        • Based on the model, if no weed killer is used (# of teaspoons of weed killer = 0), the predicted % of weeds killed is -20.5893%. This is unrealistic because the % of weeds killed cannot be negative.
          • *Someone might say this is realistic, but since it isn't immediately clear you'd still want to comment on this--one might say, "This is realistic, as a negative % of weeds killed suggests more weeds grew."
        • Common mistakes: 
          • For both of these interpretations be sure to pay attention to context! Write about the "% of weeds killed" and the "# of teaspoons of weed killer used," not just "% killed" and "# tsp.!" 
          • Remember to use "Based on the data/according to the model" AND "predicted"
      • c.) Here's the thought process....
        • First, we have to think about how the only given information (a residual plot) connects to whether a prediction is too large/too small....
        • Then, we have to notice that the horizontal axis on the residual plot is "predicted," so we can't look for 2.6 on that axis--we need a predicted value
        • Predicted % killed = -20.5893 + 24.3929(2.6) = 42.83
        • Now, we can't find the exact residual for a predicted value of 42.83, but we can see the residuals around this value--based on this info/pattern, would you expect the residual for a prediction of 42.83 to be positive or negative? Why?
        • Finally, now we know if we expect the residual to be positive or negative, so let's connect that to the original question--is the prediction expected to be too large or too small?
        • Here's a sample response:
          • "I would expect the predicted % of weeds killed for 2.6 teaspoons to be too large. Based on the pattern/shape of the residual plot, I would expect the residual for 42.83% killed to be negative, which means my prediction is expected to be an overestimate (too large). OR....
          • "I would expect this prediction to be too large. We predict 42.8% of weeds to be killed with 2.6 tsp of weed killer, and the residuals around 42.8% are all negative, so I'd expect the residual for this prediction to be negative--which means the prediction is too large."
        • What is required for your written response:
          • Do you expect the prediction to be too large or too small?
          • Why?
            • Reference predicted value is 42.8%
            • Comment on the fact that we EXPECT the residual for this prediction to be negative
            • Comment on how we know this--because the residuals around 42.8% are negative or because of the pattern in the residual plot suggests this residual would be negative
          • Context
      • d.) A linear model is not appropriate for the # teaspoons v. % of weeds killed because the residual plot has a clear pattern/curve. 
        • Common mistake: be sure to say there is a pattern IN THE RESIDUAL PLOT
        • If a student states "there is a pattern" without referencing "the residual plot" he/she would earn a 0--because if there is a pattern in the scatterplot, that's good!
          • If a linear model were appropriate then we would want to see a pattern in the scatterplot, we'd want to see "roughly linear' shape!
      • e.) Based on the data, 97.2% of the variability in the % of weeds killed can be explained by variability in the number of teaspoons of weed killer used.
        • Common mistake: if you do not include the "variability in" number of teaspoons or % weed killed then you have written a false statement and your response is incorrect (no points)
      • Extra Question: Find the correlation and describe the strength and direction.
        • r = sqrt(0.972) = +/- 0.9859; r is positive since the slope is positive
        • r = + 0.9859
          • The correlation between the # of teaspoons of weed killer and the % of weeds killed is strong and positive. 
STAMPS ARE DUE WEDNESDAY!
  • Gather all your stamps--you can leave them on the full paper, or rip off the little stamp, whatever is easier
    • You will get all of these back, so if you have notes on a paper with a stamp, don't worry, you'll get it back
    • If you count your stamps in class you will be given a zero--this is your responsibility OUTSIDE the classroom
  • Count all your stamps--double check!
    • I will choose 5 random names from each class and double check those students' stamps; if your total is incorrect, you will earn a score of 0
  • Fasten all your stamps--put them in a ziploc bag, put them in an envelope, staple them all together, or maybe glue/tape all the cut out stamps to one paper--whatever works best for you
  • Write your name and the total on the front of your stamps
  • Turn this in by Wednesday!
  • Stamps count as a classwork grade!
    • For each class I will find Q3 and use this to determine how many points the stamp grade is out of, so I can't tell you how many you need....
    • It doesn't really matter how many you need anyway, you just have to turn in all your stamps!
Chapter 8 Vocab Quiz Wednesday!

  • Residual (what does it measure? how is it calculated? how is it represented visually on a scaterplot?)
  • Actual Value (y): based on given data or the point in a scatterplot
  • Predicted Value (y-hat): generated by substituting into the LSRL equation or looking at the line of best fit
  • Interpret Slope
  • Interpret Y Intercept
  • Coefficient of Determination = R^2
  • Interpret R^2
  • Correlation
  • Underestimate (connect to residuals)
  • Overestimate (connect to residuals)
  • Linear Model
  • Least Squares Regression Line (LSRL)
  • "Is a linear model appropriate?" (what do we check?)
  • Explanatory Variable
  • Response Variable
  • Scatter plot
  • Lurking Variable
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Friday, October 26, 2018

Weekend HW + Stamps

Today's Class Recap:

Weekend HW: 

Problems found on pages 190 - 193:
  • 15 (is a linear model appropriate and interpret R^2)
  • 19 (write equation, predict, interpret slope, interpret intercept, use residual)
  • 35bcdefg (interpret R^2, find LSRL equation, is a linear model appropriate and create residual plot, interpret slope and y intercept, use residual)
  • Is a linear model appropriate? Writing help....
    • If a linear model IS appropriate, you must explain why referencing all 3 conditions....
      • "A linear model for is appropriate because the scatterplot is roughly linear, both ____ and ___ are quantiatitve variables, and the residual plot does not show a pattern."
        • Be sure to specify that the SCATTERPLOT is roughly linear and the RESIDUAL PLOT does not have a pattern--this is critical to your response. 
    • If a linear model IS NOT appropriate, then you only need to reference the condition that fails (or makes it not approrpiate):
      • "A linear model for ____(context)_____ is not appropriate because......."
        • Again, be sure to name the graph your referencing, either the scatterplot or the residual plot

STAMPS ARE DUE WEDNESDAY!

  • Gather all your stamps--you can leave them on the full paper, or rip off the little stamp, whatever is easier
    • You will get all of these back, so if you have notes on a paper with a stamp, don't worry, you'll get it back
  • Count all your stamps--double check!
    • I will choose 5 random names from each class and double check those students' stamps; if your total is incorrect, you will earn a score of 0
  • Fasten all your stamps--put them in a ziploc bag, put them in an envelope, staple them all together, or maybe glue/tape all the cut out stamps to one paper--whatever works best for you
  • Write your name and the total on the front of your stamps
  • Turn this in by Wednesday!
  • Stamps count as a classwork grade!
    • For each class I will find Q3 and use this to determine how many points the stamp grade is out of, so I can't tell you how many you need....
    • It doesn't really matter how many you need anyway, you just have to turn in all your stamps!


Quiz Tuesday!
  • On Monday we'll practice all this; Tuesday we'll start class with a (short) quiz, all writing--it's the same questions as the homework we had Wednesday (key below), so use this to study!
  • Quiz = Given a computer output....
    • Interpret slope
    • Interpret y intercept
    • Describe strength and direction based on r
    • Interpret R^2
  • You won't be given the data, you'll be given a computer output, so be sure you know how to read one of those and find each of the values above!

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Wednesday, October 24, 2018

Wednesday!

Today's Class Recap:
Wednesday HW: 

1.) Please THOROUGHLY complete the "Pop Quiz: Interpreting Bivariate Statistics" worksheet provided in class (or below).
  • For #1 please create a scatterplot, sketch it, and make sure that it is roughly linear, and comment on whether or not we have two quantitative variables
    • Don't worry about the residual plot stuff--we'll learn that on Friday
  • For #'s 2a-d, be thorough! Write these as you would on a quiz!
  • We will have a quiz to start class on Tuesday that will be very similar to this; the only difference is that on Tuesday you will have to read a computer output to get these statistics (slope, y-intercept, R^2, r) rather than generating them with your calculator
  • See the answer key at the bottom of this post to check your responses.
2.) Please complete the 3 multiple choice questions below. 

  • SHOW ALL WORK FOR THE LAST QUESTION (#8)


3.) Bonus Stamp Opportunity (up to 3 stamps): look up what we mean by "best fit!" Then, try to re-write this in your own words, in your notes! I'll come look tomorrow when I check homework!
  • Your definition cannot state "the line that best fits the data"--that's the question, what does BEST mean?
    • Hint: your answer should have something to do with residuals/errors (remember, error and residual are the same thing)....
  • If you copy the definition and do not paraphrase/put it in your own words = 1 stamp
  • Copy the definition AND translate this definition into your "own words" = 3 stamps
  • Label your definitions:
    • DEFINITION: 
    • OWN WORDS: 

Friday in class we'll discuss what "best fit" means and start to work with residual plots! We'll finish up our notes next week and do some classwork/practice, with the goal of finishing this section by Wednesday!


Oh, and if you're looking to get  head start--this will be our weekend HW.
  • 15 (is a linear model appropriate and interpret R^2)
  • 19 (write equation, predict, interpret slope, interpret intercept, use residual)
  • 35bcdefg (interpret R^2, find LSRL equation, is a linear model appropriate and create residual plot, interpret slope and y intercept, use residual)

"Interpret Pop Quiz" Answer Key:



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Tuesday, October 23, 2018

Today's Class Recap:
  • Stamp = first/top two chapter 8 slides
  • Back to chapter 8 notes...
    • Discussed residuals and took notes
      • How do we calculate a residual? 
      • What is a residual? What does it measure?
      • What does the negative/positive sign of a residual tell us?
      • How can we interpret a residual, in context?
      • How can we draw residuals on a graph?
    • Started to explore/discuss what we mean by "best fit"
  • Review: Check out the "AP Stat Guy" videos for help interpreting slope and y intercept!

Tuesday HW: The following assignment will be checked on tomorrow!

Page 189-197: *Skip both questions about interpreting R^2, we'll learn this tomorrow*

  • 41 (find r, interpret R^2, write equation, interpret slope/intercept, is a linear model appropriate)
    • 41f: This question asks if there is "evidence of a violation of any assumptions behind the regression..." 
      • In other words, remember we identified 4 conditions to determine "if a linear model is appropriate:--this question (f) is asking you to decide if any of these 4 conditions has not been met
      • 4 Conditions: scatterplot is roughly linear, no outliers in the scatterplot (nothing crazy--there aren't any here),  both variables are quantitative, and the residual plot should not have a pattern (we'll discuss why moving forward)
  • 47 (describe association, write equation, interpret slope/yint, interpret residual, predict, interpret R^2)

Bonus Stamp Opportunity (up to 3 stamps): look up what we mean by "best fit!" Then, try to re-write this in your own words, in your notes! I'll come look tomorrow when I check homework!
  • If you copy the definition and do not paraphrase/put it in your own words = 1 stamp
  • Copy the definition AND translate this definition into your "own words" = 3 stamps

Tomorrow in class we'll discuss what "best fit" means; then, we'll  look at the meaning of R^2 with the AP Stat Guy, and we'll finish up with some AP MC if we have time!


Oh, and if you're looking to get  head start--this will be our weekend HW (we'll have a different homework assigned tomorrow to be collected Friday):

  • 15 (is a linear model appropriate and interpret R^2)
  • 19 (write equation, predict, interpret slope, interpret intercept, use residual)
  • 35bcdefg (interpret R^2, find LSRL equation, is a linear model appropriate and create residual plot, interpret slope and y intercept, use residual)



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Monday, October 22, 2018

Monday HW!

Today's Class Recap:

  • 3 possible stamps: 1 open ended and 1 AP MC
  • Back to chapter 8 notes...
    • Interpret slope, interpret y intercept (using ankle range of motion v. balance score context)
    • Check out the "AP Stat Guy" videos for help interpreting slope and y intercept!
    • Students were given our "chapter 8 slides" and a handout we used for notes on reading computer outputs
    • Line of best fit vocabulary: line of best fit = linear model = linear regression equation = LSRL = least squares regression line
    • Notes: reading computer outputs


Monday HW: The following assignment will be checked on Wednesday!

Page 189-197:

  • 41 (find r, interpret R^2, write equation, interpret slope/intercept, is a linear model appropriate)
  • 47 (describe association, write equation, interpret slope/yint, interpret residual, predict, interpret R^2)

Tomorrow in class we'll start to look at the meaning of R^2 with the AP Stat Guy, and then we'll begin to explore what "best fit" actually means! See you there!
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Friday, October 19, 2018

Today's Class Recap:

  • Took our chapter 7 "math quiz" and chapter 7 vocab quiz (22 min)--see the blog post below for info about this quiz if you have to make it up
    • If you were out today you must make up this quiz during study hall/lunch/after school by the end of the day Wednesday
  • Chapter 8 notes: back to our "ankle range of motion" vs. "balance score" context....
    • Wrote the equation of the LSRL
    • Discussed "predicted values" and "actual values" and their notation
    • Started to discuss how we interpret slope

Weekend Homework =Please complete the following three problems in your textbook. I'm going to check this one, and it's super short--I think we've earned a short weekend homework after our great job on the unit 1 test! 
  • Page 189-197: 13, 27abc
Want to get a little ahead? Watch some AP Stat Guy Videos!
Other than that, enjoy your weekend! Rest up, relax, and do something fun! I'll see you all for some more of chapter 8 on Monday!

Some of next week's HW: I'm not sure which days all assign some of these, but over the course of the next week these are my favorite book problems that I'll plan to use for homework assignments (not all at once). You don't know how to do all of this, but you can do some--if you'd like to get a head start, feel free. 

 Page 189-197:
  • 15 (is a linear model appropriate and interpret R^2)
  • 19 (write equation, predict, interpret slope, interpret intercept, use residual)
  • 35bcdefg (interpret R^2, find LSRL equation, is a linear model appropriate and create residual plot, interpret slope and y intercept, use residual)
  • 41 (find r, interpret R^2, write equation, interpret slope/intercept, is a linear model appropriate)
  • 47 (describe association, write equation, interpret slope/yint, interpret residual, predict, interpret R^2)
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Thursday, October 18, 2018

Today's class recap:
  • Looked at Stat-wide test data (for our Unit 1 test), briefly discussed the Unit 1 test
  • Completed our notes on chapter 7: discussed correlation and how we can determine if "a linear model (and correlation) is appropriate to use."
  • Complete some practice questions (below) in preparation for our quiz--contact a classmate for the answers!
Tomorrow = Math and Vocab Quiz! Start studying!

  • Tonight you should complete the practice quiz provided in class (or below)
    • The questions in red are added/not on the worksheet 
    • And here is an answer key so you can check your work:
  • Here is your chapter 7 vocab list WITH DEFINITIONS for the vocab portion of the quiz:
      1. Scatterplot: a graphical display that shows the association between two quantitative variable
      2. Explanatory Variable: the independent variable (on the x-axis)
      3. Response Variable: the dependent variable (on the y-axis)
      4. Lurking Variable: a variable related to the explanatory variable and to the response variable that leads to an apparent association between the explanatory and response
      5. Describe an association (what do we describe?): describe shape/form, direction, strength, reference r (if possible), and a "generally" statement
      6. Conditions for Correlation = Is a linear model appropriate? (what do we check): scatterplot is roughly linear with no outliers, residual plot has no pattern, and the two variables are quantitative
      7. Outlier: a point that falls outside the general pattern in a scatterplot
      8. y-hat: predicted value, generated from the equation of the LSRL
      9. Correlation: a measure of the strength and direction of the linear association between two variables 
      10. Positive Association: as the explanatory variable increases, the response variable also increases
      11. Negative Association: as the explanatory variable increases, the response variable decreases
  • Finally, here are the examples we used in class in case you were out:
Tomorrow in class (after our quiz) we'll start to look at the equation of the least squares regression line, slope, and y-intercept. See you there!

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Wednesday, October 17, 2018

Weds, Thurs HW!

Tonight, please complete the following in your textbook for homework--I'll be checking!

  • Page 161: 5, 11, 13ab, 25, 29, 31
Today's class recap:
  • Discussed how to describe an association--what should we include?
  • Reviewed sketching scatterplot, finding r, R^2, and the equation of the line of best fit with our calculator (using the balance score/ankle range of motion context on our slide)
  • Described the association between ankle range of motion and balance score (sample response)
  • Discussed how to determine strength based on graph
  • Discussed/took notes on correlation (r)
  • Tuesday's Class:
    • Notes on introductory vocab: scatterplot, explanatory variable, response variable
    • Discussed lurking variables
    • Introduced how to describe an association
Friday = Math and Vocab Quiz! Start studying now!

  • Tomorrow's homework will be a practice quiz--this is below if you'd like to get a head start!
    • The questions in red are added/not on the worksheet you'll get tomorrow
    • And here is your chapter 7 vocab list: (we will discuss #'s 6-8 tomorrow)
      1. Scatterplot
      2. Explanatory Variable
      3. Response Variable
      4. Lurking Variable
      5. Describe an association (what do we describe?)
      6. Conditions for Correlation or Is a linear model appropriate? (what do we check)
      7. Outlier = a point that falls outside the general pattern in a scatterplot
      8. y-hat = predicted value
      9. Correlation
      10. Positive Association
      11. Negative Association

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Tuesday, October 16, 2018

Tonight's Homework: Please complete the "Unit 2: Bivariate Data!" worksheet provided in class or below.
  • This assignment reviews the fundamental concepts we need for this unit (and many of the ideas mentioned above to review)
  • So, by doing this homework, you're reviewing all the stuff you need to know for Monday! Use those "summer day 2" notes to help!
  • Use the AP Stat Guy, Khan Academy, and any other online resources (or your textbook) to help with this as well!
  • We will have a quiz on all of this stuff and our ch. 7 vocab quiz on Friday!
  • Some templates for c, g, and h:
    • c.) The association between __________ and ________ is _______, _______, and ______ with r = _______. Generally, as _________ increases, ________ increases/decreases.
    • g.) According to the model, for each additional 1 ________, the predicted ________ increases/decreases by approximately _______.
    • h.) According to the model, when the ________ is 0 the predicted _______ is roughly ______.


Here is the answer key-- you should complete and check your answers to last night's homework:





Be sure look over our "day 2" notes and some of the summer assignment questions that relate to unit 2 so that you are prepared for class! This will also help with your homework!
  • Day 2 notes = "Linear Regression and the Graphing Calculator" worksheet
    • Creating a scatterplot (and sketching it) using our graphing calculator (#1)
    • Using our calculator to find the equation of the line of best fit, the correlation (r), and R^2 given a data set (#2, 4)
    • Write the equation of the line of best fit and use it to make predictions (#2, #3)
  • Summer Assignment
    • Questions 1-12, 24, 26-27, and 29 are all based on unit 2, so it would be a good idea to also review these over the weekend
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