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Statistical Relationship Between Quantitative Variables
Statistical Relationship Between Quantitative VariablesStatistical relationship points to overall tendencies rather than strict rules (e.g., smoking – cancer). Scatter plots X – explanatory variable Y – response variable Interpreting scatter plots: Form, Direction, Strength.
Statistical Relationship Between Quantitative VariablesThe simplest form is the Linear Relationship Math grade Statistics Grade
More Complicated Forms of Statistical RelationshipsElectricity shortage Mean Monthly Temperature
B. Negative linear relationshipA. Positive linear relationship C. Weak positive relationship D. Strong negative relationship
Linear Correlation CoefficientThe linear correlation coefficient (r) is a measure for the direction and strength of a linear relationship between two quantitative random variables.
r(X,Y) – Basic Idea (Positive)Y – income ($) Y Average of Y X Average of X X – education (yrs)
r(X,Y) – Basic Idea (Negative)Y – Statistics grade Average of Y X Average of X X – Time in Pubs (hrs/week)
Linear Regression Line (predicting Y from X)The ‘best’ line that fits the scatter plot is the one that minimizes the sum of the (square of the) deviations
Chapter 3 Examining Relationships Lindsey Van Cleave AP Statistics September 24, 2006.
Regresi Linear Sederhana Pertemuan 01 Matakuliah: I0174 – Analisis Regresi Tahun: Ganjil 2007/2008.
1-4 curve fitting with linear functions
Scatter Diagrams and Linear Correlation
AP Statistics Chapters 3 & 4 Measuring Relationships Between 2 Variables.
Elementary Statistics Larson Farber 9 Correlation and Regression.
Correlation and Regression. Correlation What type of relationship exists between the two variables and is the correlation significant? x y Cigarettes.
Calculating and Interpreting the Correlation Coefficient ~adapted from walch education.
Correlation & Regression Math 137 Fresno State Burger.
Linear Regression Analysis
Relationships between Variables. Two variables are related if they move together in some way Relationship between two variables can be strong, weak or.
A P STATISTICS LESSON 3 – 2 CORRELATION.
Biostatistics Unit 9 – Regression and Correlation.
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?
Example 1: page 161 #5 Example 2: page 160 #1 Explanatory Variable - Response Variable - independent variable dependent variable.
Graph of a set of data points Used to evaluate the correlation between two variables.
Holt Algebra Curve Fitting with Linear Models 2-7 Curve Fitting with Linear Models Holt Algebra 2 Lesson Presentation Lesson Presentation.
Warm Up Write the equation of the line passing through each pair of passing points in slope-intercept form. 1. (5, –1), (0, –3) 2. (8, 5), (–8, 7) Use.
Elementary Statistics Correlation and Regression.
Topic 10 - Linear Regression Least squares principle - pages 301 – – 309 Hypothesis tests/confidence intervals/prediction intervals for regression.
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