Relationships between quantitative variables: is one value related to the value of the other? How strong is the relationship?Displaying the relationship between quantitative variables: use scatterplotsGraph types we’ve learned about so far: pie charts, bar graphs, boxplots, histogramsAnswer to slide 5: yesEach dot represents one individual in the data set, location of the dot is determined by the value of the individual (refer to slide 6  as cars weight goes up, the gas mileage goes down)Middle graph on slide 7: as weight increases, price increasesLast graph on slide 7: cheaper cars have better gas mileage, there’s an obvious curve on this graph and an outlierAnswer to slide 8: B, weight and price have a positive association (one goes up, the other goes up)Negative association: one goes up, the other goes downX-axis: variable that is “explaining” the other variable; the variation in weight determines the gas mileage and the variation in weight determines or explains the price of the car (the price doesn't explain the weight of the car)Here, the weight is the explanatory variable and the gas mileage is the response variableDoing the explaining- x axisThing that’s being explained- y axisAnswer to slide 13: A  as the mother’s age goes up, the father’s age goes up (positive association)Answer to slide 14: A  positive associationAnswer to slide 16: C  no associationWhat do scatterplots tell us?The direction of the association between two variablesAn idea of the strength of the associationThe form of the association  sometimes there’s a linear association between two variables. You can draw a rough line through the cloud of points on a scatterplot that will capture the shape of the pointsThe points fall roughly along a straight lineThe change in the y variable is about the same for any given change in the x variableEx: slide 19- any change in the mother’s age would result in about the same change of the father’s ageIf you can’t draw a line on the scatter plot, the association is called non-linear or curvilinearSlide 23: as you add fluoride to the water, cavity rate drops quickly but there’s a limit, eventually it levels offSlide 23: Clear relationship between income and life expectancy, but this levels off and at about $1000, the life expectancy stops increasing exponentiallyGraphs on slide 23 are curvilinear you can basically draw a smoothly connected curve through the points on the scatterplotRelationships between variables 02/10/2014-Relationships between quantitative variables: is one value related to the value of the other? How strong is the relationship?-Displaying the relationship between quantitative variables: use scatterplots-Graph types we’ve learned about so far: pie charts, bar graphs, boxplots, histograms-Answer to slide 5: yes-Each dot represents one individual in the data set, location of the dot is determined by the value of the individual (refer to slide 6  as cars weight goes up, the gas mileage goes down)-Middle graph on slide 7: as weight increases, price increases-Last graph on slide 7: cheaper cars have better gas mileage, there’s an obvious curve on this graph and an outlier -Answer to slide 8: B, weight and price have a positive association (one goes up, the other goes up) -Negative association: one goes up, the other goes down -X-axis: variable that is “explaining” the other variable; the variation in weight determines the gas mileage and the variation in weight determines or explains the price of the car (the price doesn't explain the weight of the car)-Here, the weight is the explanatory variable and the gas mileage is the response variable -Doing the explaining- x axis-Thing that’s being explained- y axis-Answer to slide 13: A  as the mother’s age goes up, the father’s age goes up (positive association)-Answer to slide 14: A  positive association -Answer to slide 16: C  no association -What do scatterplots tell us? -The direction of the association between two variables-An idea of the strength of the association-The form of the association  sometimes there’s a linear association between two variables. You can draw a rough line through the cloud of points on a scatterplot that will capture the shape of the pointsoThe points fall roughly along a straight line-The change in the y variable is about the same for any given change in the x variable -Ex: slide 19- any change in the mother’s age would result in about the same change of the father’s age-If you can’t draw a line on the scatter plot, the association is called non-linear or curvilinear-Slide 23: as you add fluoride to the water, cavity rate drops quickly but there’s a limit, eventually it levels of-Slide 23: Clear relationship between income and life expectancy, but this levels of and at about $1000, the life expectancy stops increasing exponentially -Graphs on slide 23 are curvilinear you can basically draw a smoothly connected curve through the points on the