• # Lesson Material and Schedule Outline

The below information is also posted on Canvas and can be accessed through the SSO (sso.dentonisd.org) or dentonisd.instructure.com using the google student sign in information for those who have received and accepted a Canvas invitation through email.

MODULE/UNIT 1

Unit 1 Objectives: (Lesson number is in parenthesis)

Data in general:

• Identify and classify variables as Categorical (binary) or Quantitative (discrete or continuous) (1.2)
• Collect data measured with various categorical and quantitative variables using frequency tables (1.2, 1.3)

Categorical Data:

• Determine the marginal distribution of a categorical variable (1.2)
• Calculate relative frequencies and conditional frequencies from a distribution of categorical data (1.2)
• Represent categorical data using bar charts, segmented bar charts, and pie charts (1.2)
• Describe and interpret a distribution of categorical data from various representations (1.2)

Quantitative Data:

• Represent quantitative data using box plots, histograms, and stem-and-leaf plots (1.3, 1.4)
• Describe and interpret a distribution of quantitative data using measures of center, unusual qualities, shape, and spread (1.3, 1.4, 1.5)
• Compare two distributions of quantitative data (1.4)
• Calculate the 5-number summary of a data set (1.3)
• Calculate the fence value for a box plot and determine if outliers exist (1.3)
• Describe and/or calculate the effects of outliers on the 5-number summary, mean, median, range, IQR, standard deviation, and variance of a data set (1.5)
• Describe and/or calculate the effects of adding or removing a data point on the five number summary (1.5)
• Know the difference between robust and non-robust statistics (1.5)

Schedule:

First Day August 16/17
Overview of Statistics: Smelling Parkinson's Investigation/Simulation
Quick Syllabus Review, answer any questions
HW: Complete Module 0 in Canvas

Lesson 1.2: Types of Data; Collecting and Representing Categorical Data
Dates: August 18 (A) and 21 (B)
Homework: Pages 38-39 #23, 24, 25, 26

Lesson 1.3A: Representing and Summarizing Quantitative Data with Histograms and Stem-and-Leaf Plots
Dates: August 22 (A) and 23 (B)
Summarizing Data (PSU Online Lessons 2.1 through 2.3): https://onlinecourses.science.psu.edu/stat500/node/11
Homework: Pages 73-79 #5, 9, 44, 45, 48

Lesson 1.3B: Representing and Summarizing Quantitative Data with Numerical Measures and Box Plots
Dates: August 24 (A) and 25 (B)
(Powerpoint Notes for Chapter 3 in lesson 1.3A)
Summarizing Data (PSU Online Lessons 2.1 through 2.3): https://onlinecourses.science.psu.edu/stat500/node/11
Homework: Pages 75-78 #18, 19, 28, 33, 37

Lesson 1.4: Comparing Two or More Distributions of Quantitative Data
Dates: August 28 (A) and 29 (B)
Summarizing Data (PSU Online Lessons 2.1 through 2.3): https://onlinecourses.science.psu.edu/stat500/node/11
Homework: Pages 97-101 #7, 12, 15, 23, 26

Lesson 1.5: Determining Effects of Changing Data on Summary Measures
Dates: August 30 (A) and 31 (B)
Summarizing Data (PSU Online Lessons 2.1 through 2.3): https://onlinecourses.science.psu.edu/stat500/node/11
Homework: Pages 131-133 #5, 11, 1224

Review for Unit 1 Test and catch-up
Dates: September 1 (A) and 5 (B)
Test Review: Pages 138-146 #1, 11, 15, 16, 18, 19, 28, 31 (omit f), 32 (omit d & e), 33

Unit 1 Test: Categorical and Quantitative Data
Dates: September 6 (A) and 7 (B)
AP Set #1 Due on test day
HW: Take the free online IQ test and print out your results OR take a screen shot to show me in class. http://www.free-iqtest.net/

MODULE/UNIT 2

Note: Lessons 2.11A and 2.11B are switched on purpose.

Lesson 2.11B: Recognizing Sources of Bias
Activity and Goal: Students will use examples of bias to create a skit representing a given type of bias; Students will compare/contrast the various types of bias presented
Dates: September 8 (A) and 11 (B)
Homework: Pages 301 #12, 13, 15, 16

Lesson 2.11A: Types of Sampling Methods
Activity and Goal: Students will use and compare various sampling methods and discover their effects on estimating a population parameter
Dates: September 12 (A) and 13 (B)
Online lesson on sampling methods from PSU: https://onlinecourses.science.psu.edu/stat100/node/18
Homework: Read pages 280 - 285 until the "Just Checking" box and complete the "Just Checking" exercise.  Complete #7, 9, 10 on page 300-301.

Lesson 2.11C: Survey Design
Activity and Goal: Students will use their knowledge of bias and additional examples of political polls to design a survey for Guyer students assessing a topic determined by the class.
Dates: September 14 (A) and 15 (B)
Homework: Pages 302 #24, 25, 26

Lesson 2.11D: Sampling from a population
Activity and Goal: Students will work with a partner to gather a sample of Guyer students from the sampling frame for implementation.
Remaining time will be spent reviewing for the test next class
Dates: September 18 (A) and 19 (B)
Homework: Pages 303-304 #36, 37, 38; Finish AP Set #2

Test Unit 2: Sample Surveys, Bias, & Sampling Methods
Dates: September 20 (A) and 21 (B)
AP Set #2 Due on test day

IQ PROJECT
Main Objectives: Use graphical and numerical summaries to compare distributions of quantitative data; Use the empirical rule to calculate percentiles; Use a simulation to determine the existence of a statistically significant difference between two independent samples
Dates: October 2/3 through 6/9
DUE October 10 at the end of the day, no exceptions!

MODULE/UNIT 3

# Unit 3 Objectives  (Lesson number is in parenthesis)

The Normal Probability Distribution:

• Determine standardized values (z-scores) for observations from various populations (3.5A)
• Use standardized scores to compare across different populations (3.5A)
• Use the Empirical Rule for efficient and accurate percentile calculations (3.5A)
• Use the Empirical Rule to efficiently determine observed values from given percentile rankings (3.5A)
• Use z-scores to calculate one-sided normal distribution probabilities with and without technology (3.5B)
• Use z-scores to calculate two-sided normal distribution probabilities with and without technology (3.5B)
• Use percentile rankings to determine specific observations from populations with and without technology (3.5C)
• Interpret ogive curves to determine the shape of a distribution and percentile rankings for observations (3.5C)
• Use systems of equations to solve for parameter values of non-standard normal distributions (3.5C)

Schedule:

Lesson 3.5A: Understanding the Normal Distribution

Dates: October 17, 19 (B)  and 18, 20 (A)

Focus Topics: Comparisons with z-scores, The Empirical Rule

Continuous Distributions (Focus on the Normal Distribution): https://onlinecourses.science.psu.edu/stat500/node/23

Homework 3.5A1: Pages 132-135 #25, 26, 30, 35  Solutions

Homework 3.5A2: Pages 132-133 #16, 17, 19, 22, 27  Solutions

Lesson 3.5B: Probability Calculations with the Normal Distribution

Dates: October 23, 25 (B) and 24, 26 (A)

Focus Topics: Using the normalcdf and invnorm functions in the calculator for one and two-sided calculations

Solutions to Practice problems from the notes

Continuous Distributions (Focus on the Normal Distribution): https://onlinecourses.science.psu.edu/stat500/node/23

Homework 3.5B1: Page 136 #39, 40, 41, 42, 43 Solutions

Homework 3.5B2: Pages 136-137 #46, 47, 48, 49 Solutions

Lesson 3.5C: Ogive Curves and Review

Dates: October 27 (B) and 30 (A)

Focus Topics: Creating and Reading Ogive curves; Review for Test

Homework 3.5C: Page 102 #29, 30; Test Review Solutions to handout

Normal Curve Practice Solutions (Handout we used as notes and highlighted colors of problems)

Test review problems from the textbook: SOLUTIONS

Unit 3 Test: The Normal Probability Distribution

Dates: October 31 (B) and November 1 (A)

AP Set #3 Due on test day

MODULE/UNIT 4

Objectives for Regression and Correlation

• Calculate by hand and understand the theory behind the correlation coefficient (r) - why are the z-scores of x and y used?
• Describe an association between two quantitative variables using strength (weak, moderate, strong), direction (positive, negative), and form (linear, non-linear)
• Understand that a correlation between two variables does not imply that one causes the other
• Calculate by hand and understand the theory behind the Least Squares Regression Line
• Calculate and interpret the residuals from a regression model as well as the residual plot
• Determine if a particular residual corresponds to an overestimate or an underestimate of the response
• Interpret computer output from a regression analysis and use it to create a regression equation
• Interpret R2 - "coefficient of determination"; the proportion of variation in the response that is explained by the predictor
• Interpret s - the standard deviation of the residuals; observations are on average s units from the LSRL
• Interpret t and p - is an explanatory variable a statistically significant predictor of the response?
• Interpret standard error of the slope
• Use CONTEXT when interpreting a regression equation, slope, and y-intercept
• Use a regression equation to make a prediction about the response variable
• Use statistical software to perform a regression analysis on a bivariate data set
• Compare and contrast outliers and influential points
• Determine the effects of outliers and influential points on regression output
• Understand the dangers of extrapolation and identify when it is used in the media

Lesson 4.6: Correlation Does not imply Causation; Calculating and interpreting the Correlation Coefficient

Dates: October 20, November 1(B) and October 31, November 2 (A)

Spurious Correlations Activity: Nicholas Cage movies do not cause people to drown. (Or do they?)

Unpacking Past AP Exam Questions on Regression - what do you have to know about this unit?

Calculating the Correlation Coefficient with M&M's - It's all based on z-scores

Key Words/Concepts: Explanatory Variable, Response Variable, Correlation Coefficient (r), Association, Strength and Direction of an Association

Video on association: https://mediaplayer.pearsoncmg.com/assets/_LDUYyhAE3mgU4ia6ejCl5zjI8_U6ODS

Chapter 6 Powerpoint Notes

HW: Pages 167-175 #2, 5, 9, 10, 47 SOLUTIONS

Lesson 4.7: What is the Least Squares Regression Line (LSRL)? How is it calculated and what do I have to interpret?

Dates: November 3, 7, 9 (B) and 4, 8, 10 (A)

Randomness and Scrabble Activity - Which variable explains the most about a word's Scrabble score? -->Interpreting slope, y-intercept, r, and R-squared

Chapter 7 Powerpoint Notes

Video Collection: R-squared  / Regression and LSRL  / Slope and Y-intercept

Step-by-Step Calculating a Regression Equation

Homework 4.7A: Pages 199-203 #2, 3, 11, 15, 16, 43abc   SOLUTIONS

Due 11/7 (B) 11/8 (A)

Homework 4.7B: Finish Scrabble problem #3

Due 11/9 (B) 11/10 (A)

Homework 4.7C pg. 199-205 #18, 38, 47, 49;  Read pages 209-221

Due 11/15 (B)  11/16 (A)

Barbie Bungie Jumping

Lesson 4.8/4.9

Dates: 11/15 (B) and 11/16 (A)

Linear Transformations with M&M's: How to determine a linear model when data are not linear

Back-transforming an equation to predict a y-value

HW: Test Review (see below)

Review for Test

Pages 225-229 #17, 19, 25, 31, 33

Pages 248-253 #5, 7, 9, 18-22

Pg. 264-265 Multiple Choice #1-10

SOLUTIONS

Unit 4 Test: Regression and Correlation

Dates: November 17 (B) and 18 (A)