Essential Skills and Standard Clarification Knowledge 8.SP.A.1: Construct and Ability to bivariate data: When we conduct a study that examines the relationship between two integrate interpret scatter plots for variables, we are working with bivariate data. A study that examines a potential technology and bivariate measurement relationship between the height and weight of high school students is based on relate the data to investigate patterns bivariate data, or two variables (height and weight). A study that looks at only one scenarios to of association between two variable, for example a survey to estimate the average height of high school students is authentic quantities. Describe based on univariate data. student-centered patterns such as situations Ability to keep clustering, outliers, scatter plot: Scatter are used to analyze patterns in . These patterns are described pairs of data positive or negative in terms of linearity, slope, and strength. organized in association, linear Linearity refers to whether a data pattern is linear (straight) or nonlinear (curved). relation to one and association, nonlinear refers to the direction of change in variable y when the value of variable x another within association. increases. If the value of y also increases, the slope is positive; but if the value of y two sets of decreases, the slope is negative. If the data points are horizontal, the slope is 0. quantities Strength refers to the degree of "scatter" in the plot. If the data points are widely spread, the relationship between variables is weak. If the points are concentrated around a line, clustered, the relationship is strong. Additionally, scatter can reveal unusual features in data sets, such as clusters, gaps, and . The scatter plots below illustrate some common patterns.
Standard Essential Skills and Knowledge Clarification 8.SP.A.1: Construct and interpret scatter plots for bivariate measurement data to investigate patterns of association between two quantities. Describe patterns such as clustering, outliers, positive or negative association, linear association, and nonlinear association. Ability to integrate technology and relate the scenarios to authentic student-centered situations Ability to keep pairs of data organized in relation to one another within two sets of quantities bivariate data: When we conduct a study that examines the relationship between two variables, we are working with bivariate data. A study that examines a potential relationship between the height and weight of high school students is based on bivariate data, or two variables (height and weight). A study that looks at only one variable, for example a survey to estimate the average height of high school students is based on univariate data. scatter plot: Scatter are used to analyze patterns in . These patterns are described in terms of linearity, slope, and strength. Linearity refers to whether a data pattern is linear (straight) or nonlinear (curved). refers to the direction of change in variable y when the value of variable x increases. If the value of y also increases, the slope is positive; but if the value of y decreases, the slope is negative. If the data points are horizontal, the slope is 0. Strength refers to the degree of "scatter" in the plot. If the data points are widely spread, the relationship between variables is weak. If the points are concentrated around a line, clustered, the relationship is strong. Additionally, scatter can reveal unusual features in data sets, such as clusters, gaps, and . The scatter plots below illustrate some common patterns.
Unit_8_SP_A_1_4_Associations_in_Bivariate_Data_Final.docx