Unit 4 Lesson 5 Demonstrating Mastery M.8.SP.1 To demonstrate mastery of the objectives in this lesson you must be able to:  Construct and interpret.

Slides:



Advertisements
Similar presentations
BiVariate Data When two pieces of data are collected Data can be organized as a SCATTERPLOT – if data is numbers Or in a TWO WAY TABLE – if data is categories.
Advertisements

Vocabulary scatter plot Correlation (association)
Warm-Up: Determine the slope to be positive, negative, zero, or undefined for the two sets of relations and the graph. 1.(2,1),(4,2),(6,3)3. 2. (3,0),
Linear Statistical Model
Bivariate Data & Scatter Plots Learn to take bivariate data to create a scatter plot for the purpose of deriving meaning from the data.
Scatter Plots Find the line of best fit In addition to level 3.0 and beyond what was taught in class, the student may:  Make connection with.
Multi-Period Workforce Scheduling Basketball Team Problem SE 303 Lab.
Chapter 5 Linear Models Linear Models are studied intensively because: Easiest to understand and analyze Relationships are often linear Variables with.
IDENTIFY PATTERNS AND MAKE PREDICTIONS FROM SCATTER PLOTS.
Calculating and Interpreting the Correlation Coefficient ~adapted from walch education.
8/10/2015Slide 1 The relationship between two quantitative variables is pictured with a scatterplot. The dependent variable is plotted on the vertical.
Chapter Scatter plots. SAT Problem of the day  Nicoletta deposits $150 in her savings account. If this deposit represents a 12 percent increase.
Scatter Plots and Trend Lines
© 2012 Common Core, Inc. All rights reserved. commoncore.org NYS COMMON CORE MATHEMATICS CURRICULUM A Story of Ratios Grade 8 – Module 6 Linear Functions.
Prior Knowledge Linear and non linear relationships x and y coordinates Linear graphs are straight line graphs Non-linear graphs do not have a straight.
Investigating Relationships between Variables: Interpreting Scatterplots.
Scatter Plots Dr. Lee. Warm-Up 1 Graph each point. 1. A(3, 2) 2. B(–3, 3) 3. C(–2, –1) 4. D(0, –3) 5. E(1, 0) 6. F(3, –2)
Splash Screen. Over Lesson 1–5 A.A B.B C.C D.D 5-Minute Check 1 Which expression can be used to represent fourteen less than twice the width? A.14 – 2.
Chapter 3 Section 3.1 Examining Relationships. Continue to ask the preliminary questions familiar from Chapter 1 and 2 What individuals do the data describe?
Let’s play “Name That Car!”. What car is this? And this?
Splash Screen. Lesson Menu Five-Minute Check (over Lesson 12–2) CCSS Then/Now New Vocabulary Key Concept: Symmetric and Skewed Distributions Example 1:Distribution.
Basketball Stats Jeremy Lin is a point guard. And a point guard plays an important role on the basketball team. In some ways, the point guard is like.
Lesson 1-7 Pages Scatter Plots. What you will learn! 1. How to construct scatter plots. 2. How to interpret scatter plots.
Jeopardy Topic 1Topic Q 1Q 6Q 11Q 16Q 21 Q 2Q 7Q 12Q 17Q 22 Q 3Q 8Q 13Q 18Q 23 Q 4Q 9Q 14Q 19Q 24 Q 5Q 10Q 15Q 20Q 25.
Scatterplots are used to investigate and describe the relationship between two numerical variables When constructing a scatterplot it is conventional to.
Overview of CCSS Statistics and Probability Math Alliance September 2011.
NCSU WOLFPACK Men’s Basketball. Home & Away Games.
Chapter 3-Examining Relationships Scatterplots and Correlation Least-squares Regression.
4.1 Tables and Graphs for the Relationship Between Two Variables Objectives: By the end of this section, I will be able to… 1) Construct and interpret.
Unit 4 Lesson 7 Demonstrating Mastery M.8.SP.3 To demonstrate mastery of the objectives in this lesson you must be able to:  Interpret the slope and.
Unit 4 Lesson 6 Demonstrating Mastery M.8.SP.2 To demonstrate mastery of the objectives in this lesson you must be able to:  Know that straight lines.
What Do You See?. A scatterplot is a graphic tool used to display the relationship between two quantitative variables. How to Read a Scatterplot A scatterplot.
Lesson 4.7 – Interpreting the Correlation Coefficient and Distinguishing between Correlation & Causation EQs: How do you calculate the correlation coefficient?
.  Relationship between two sets of data  The word Correlation is made of Co- (meaning "together"), and Relation  Correlation is Positive when the.
Section 2.4 Representing Data.
Holt Algebra Scatter Plots and Trend Lines 4-5 Scatter Plots and Trend Lines Holt Algebra 1 Warm Up Warm Up Lesson Presentation Lesson Presentation.
Lesson – Teacher Notes Standard: 8.SP.A.1 Construct and interpret scatter plots for bivariate measurement data to investigate patterns of association.
Linear Best Fit Models Learn to identify patterns in scatter plots, and informally fit and use a linear model to solve problems and make predictions as.
Lesson – Teacher Notes Standard: 8.SP.A.1 Construct and interpret scatter plots for bivariate measurement data to investigate patterns of association.
Chapter 9 Scatter Plots and Data Analysis LESSON 1 SCATTER PLOTS AND ASSOCIATION.
Unit 4 Lesson 8 Demonstrating Mastery M.8.SP.4 To demonstrate mastery of the objectives in this lesson you must be able to: Understand that patterns.
Chapter 2.4 Paired Data and Scatter Plots. Scatter Plots A scatter plot is a graph of ordered pairs of data values that is used to determine if a relationship.
Scatter Plots. Standard: 8.SP.1 I can construct and interpret scatterplots.
STANDARD: 8.SP.2 STATISTICS AND PROBABILITY: SCATTER PLOTS.
GOAL: I CAN USE TECHNOLOGY TO COMPUTE AND INTERPRET THE CORRELATION COEFFICIENT OF A LINEAR FIT. (S-ID.8) Data Analysis Correlation Coefficient.
Correlation & Linear Regression Using a TI-Nspire.
CCSS.Math.Content.8.SP.A.1 Construct and interpret scatter plots for bivariate measurement data to investigate patterns of association between two quantities.
STANDARD: 8.SP.1 STATISTICS AND PROBABILITY: SCATTER PLOTS.
Holt McDougal Algebra 1 Scatter Plots and Trend Lines Holt Algebra 1 Warm Up Warm Up Lesson Presentation Lesson Presentation Lesson Quiz Lesson Quiz Holt.
8th Grade Calculator Practice
Homework: Study unit 6 Notes *Unit test Friday, april 22
Scatter Plots and Association
Presented by Mr. Laws JCMS
Lesson 4.8 – Interpreting the Correlation Coefficient and Distinguishing between Correlation & Causation EQ: How do you calculate the correlation coefficient?
Writing Linear Equations from Situations, Graphs, & Tables
Warm Up.
Writing Linear Equations from Graphs & Tables
WAM 6-10, Questions over homework? Study for Comprehensive Test, Thurs
Common Core Standard 8.SP.A.1
Claim 1 Smarter Balanced Sample Items Grade 8 - Target J
Splash Screen.
Scatter Plots Math 8. Unit 5.
Writing Linear Equations from Graphs & Tables
Lesson – Teacher Notes Standard:
Lesson – Teacher Notes Standard:
Creating and interpreting scatter plots
Lesson 32: Use Visual Overlap to Compare Distributions
Presentation transcript:

Unit 4 Lesson 5 Demonstrating Mastery

M.8.SP.1 To demonstrate mastery of the objectives in this lesson you must be able to:  Construct and interpret scatter plots for bivariate measurement data.  Identify linear versus nonlinear pattern.  Distinguish between a positive or negative linear association.  Determine the presence of clusters and outliers.

Performance Task for Demonstration of Mastery Task: You are one of the managers for the Tigers basketball team. Your job is to keep track of playing time and points scored for each player during the season. Now that the season is over, the coach wants you to:  display the data set in a scatter plot  analyze the scatter plot for patterns  identify any clusters or outliers and possible meanings  determine if there is a statistical relationship between playing time and points scored.  present your findings to the coaching staff

You have calculated the averages for each player and displayed them in the table below. Use this information to construct the scatter plot. Average minutes played Average points scored

Your report based on conclusions drawn from the scatter plot should include the following:  The scatter plot indicates that there is a positive linear association between the average minutes played and the average points scored.  The statistical relationship supports the conclusion that the more minutes a player plays, the more points he will score.  There is clustering around the minute range which indicates that several players play and score about the same amount.  Point (22,6) is a noticeable outlier. This point means that a player averages 22 minutes of playing time and only averages 6 points which does not support the conclusion of more playing time equals more points scored.  Possible reasons for the outlier primarily a defensive player and rebounder point guard