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Chapter 2

Chapter 2

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Chapter 2- Looking at Data- Relationships -associated: term used to describe the relationship between two variables ex. breed and life span -examining relationships: What individuals or cases do the data describe? What variables are present? How are they measured? Which variables are quantitative and which are categorical? Ex. on page 85 -response variable: measures an outcome of a study -explanatory variable: explains or causes changes in the response variables. Ex. many of these do not involve direct causation. Ex. sat scores of high school students help predict future college grades but high sat scores dont CAUSE high college grades. -independent variables: called explanatory variables -dependent variables: called response variables Response variables rely on explanatory variables 2.1- Scatterplots -scatterplots: for showing relationship between two quantitative variables measured on the same individuals. -explanatory variable(s) on x axis called x. (if no explanatory variable, then any of the variables can on either axis) Response variable on y axis called y. -Interpreting scatterplots: Look for overall pattern and deviations from pattern ex. outliers- falls outside the pattern of the relationship. Describe overall pattern by the form, direction, and strength of the relationship. -form: ex. clusters Pg. 87 fig. 2.1 has two clusters Clusters: groups of points on the graph. They suggest that the data describe several distinct kinds of individuals. -positive associated: when two variables are above average values of one tend to accompany above average values of the other and below average values also tend to occur together -negatively associated: when two variables are above average values of one accompany below average values of the other and vice versa. -linear relationship: points roughly follow a straight line Strength of relationship: determined by how closely the points follow a clear form -to add a categorical variable to a scatterplot, use a different plot colour or symbol for each category -smoothing: systematic methods of extracting the overall pattern are helpful. They use resistant calculations so they are not affected by outliers in the plot. www.notesolution.com
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