C784 MODULE 6: CORRELATION & REGRESSION EXAM | QUESTIONS & 100% VERIFIEDANSWERS | LATEST UPDATE | PASSED

C784 MODULE 6: CORRELATION & REGRESSION EXAM | QUESTIONS & 100% VERIFIEDANSWERS | LATEST UPDATE | PASSED lurking variable Correct Answer: A variable that is not included in an analysis but that is related to two (or more) other associated variables which were analyzed. simple linear regression Correct Answer: the prediction of one response variable's value from one explanatory variable's value Simpson's Paradox Correct Answer: A counterintuitive situation in which a trend in different groups of data disappears or reverses when the groups are combined. degree Correct Answer: The largest exponent in a mathematical expression or equation. causation 2 Correct Answer: A relationship of cause and effect between two or more variables. linear interpolation Correct Answer: Estimation using the linear regression equation is between known data points. association Correct Answer: A pattern or relationship between two variables. coordinate plane Correct Answer: A tool for graphing consisting of a horizontal x-axis and a vertical yaxis. regression equation Correct Answer: An equation used to model the relationship between two quantitative dependent and independent variables. scatterplot Correct Answer: A graph that uses dots on a coordinate plane to show the relationship between variables. Regression Analysis 3 Correct Answer: a statistical tool that quantifies the relationship betwn a response variable and one or more explanatory variables least squares Correct Answer: A technique for finding the regression line. slope-intercept form Correct Answer: A common format for the equation of a line: y = mx + b, where m is the slope and b is the y-intercept. regression line Correct Answer: The line of best fit to show the relationship between variables, the one that minimizes distance from each data point to the line. A linear regression equation takes the following form: y = mx^2 + b. True or False? Correct Answer: false. This is not the form that a linear regression equation takes. Linear regression is always of degree 1, so the exponent of 2 associated with the x makes this a non-linear equation