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by: Jordane Kemmer


Jordane Kemmer
GPA 3.79

Spencer Muse

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About this Document

Spencer Muse
Class Notes
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This 6 page Class Notes was uploaded by Jordane Kemmer on Thursday October 15, 2015. The Class Notes belongs to ST 301 at North Carolina State University taught by Spencer Muse in Fall. Since its upload, it has received 9 views. For similar materials see /class/223943/st-301-north-carolina-state-university in Statistics at North Carolina State University.

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Date Created: 10/15/15
Chapter 2 Looking at Relationships Chapter Objectives 0 Display relationships between two quantitative variables with scatterplots 0 Describe the form of relationships between two quantitative variables 0 Use the correlation r to measure the strength and direction of a linear relationship between two variables 0 Quantify a relationship between two variables using the least squares regression line 21 Scatterplots TWO variables measured on the same individuals are associated if some values of one variable tend to appear With certain values of the second variable 0 X 2 height Y 2 weight of adults 0 X 2 blood pressure Y cholesterol level 0 X 2 age Y salary Associations can also include categorical variables 0 X 2 corn yield Y 2 corn variety grown 0 X 2 genetic variant Y 2 disease Y or N Sometimes but not always we use one variable to predict or explain the value of a second variable A response variable measures the outcome of a study An explanatory variable eXplains or causes change in the value of the response variable Alternate terms dependent and independent variables 0 X 2 SAT score Y 2 college GPA 0 X 2 population density Y 2 crime level 0 X 2 speed Y length of vehicle skid marks 0 X 2 alcohol consumed Y 2 blood alcohol level Displaying associations between two quantitative variables A scatterplot is used to display associations between two quantitative variables measured on the same individuals m a x 1 X1 Y1 2 X2 Y2 3 gt4 1 lt 1 Cincinatti 53 51 n 45 u 5 055 m a women m Labor Farm was Interpreting Scatterplots When describing a scatterplot we will point out 0 Overall pattern 0 Form 0 Direction 0 Strength 0 Deviations from the pattern outliers Linear 35 25 15 25 Clusters Curved Weak Positive Strong Negative Outlier 2o


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