STAT 1301 Chapter 8 Scatter Plots, Correlation. For Regression Unit You Should Know n How to plot points n Equation of a line Y = mX + b m = slope b =

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Presentation transcript:

STAT 1301 Chapter 8 Scatter Plots, Correlation

For Regression Unit You Should Know n How to plot points n Equation of a line Y = mX + b m = slope b = Y-intercept n Plotting line from equation Y = 3X + 2

Data Set X Y Y X

Y X X Y X Y Y = 3X + 2 Y = 3X + 2..

For Regression Unit You Should Know n How to plot points n Equation of a line Y = mX + b m = slope b = Y-intercept n Plotting line from equation Y = 3X + 2 n Chapter 7 - Good Review if needed

Histogram n displays distribution of 1 variable Scatter Diagram Scatter Diagram n displays joint distribution of 2 variables n plots data as “points” in the“x-y plane.”

Association Between Two Variables indicates that knowing one helps in predicting the otherindicates that knowing one helps in predicting the other n Linear Association our interest in this courseour interest in this course points “swarm” about a linepoints “swarm” about a line n Correlation Analysis measures the strength of linear associationmeasures the strength of linear association

Hypothetical Father-Son Data

(association)

Regression Analysis n we want to predict the dependent variable using the independent variable DependentVariable(Y) Independent Variable (X)

Correlation Coefficient - measures linear association perfect no perfect perfect no perfect negative linear positive relationship relationship relationship n We use the letter “ r ” to denote the correlation coefficient.

Positive Correlation - - high values of one variable are associated with high values of the other Examples: n Father’s height, son’s height n daily grade, final grade n r = 0.93 for plot on the left

Negative Correlation - - high with low, low with high Examples: n Car weight, miles per gallon n Days absent, final grade n r = for plot shown here

Zero Correlation - - no linear relationship Examples: n height, IQ score n r = 0.0 for plot here

-.75, 0,.5,.99

r = 0.00

r = 0.40

r =

r = 0.8

r = 0.95