Statistics Module 6 (A+ Graded 100% Verified)

Statistics Module 6 (A+ Graded 100% Verified) P-value Ans: The probability that a result was caused by chance. observational study Ans: The researcher observes if there is an association between variables. There is no treatment or control group. population Ans: An entire pool from which a sample is drawn. causation Ans: A relationship of cause and effect between two or more variables. simple linear regression Ans: The prediction of one response variable's value from one or more explanatory variables' value when there is a linear relationship between the two variables. Simpson's Paradox Ans: A counterintuitive situation in which a trend in different groups of data disappears or reverses when the groups are combined. hypothesis test Ans: A statistical test that tell us whether a result is significant. regression equation Ans: An equation used to model the relationship between the response and explanatory variables in a regression. correlation coefficient Ans: A measure of the linear relationship between two attributes. The numerical value demonstrates how closely the attributes vary together. Correlation coefficients near -1 and +1 have strong linear correlation, while a correlation coefficient near 0 has weak (or no) linear correlation. slope-intercept form Ans: A common format for the equation of a line: y = mx + b, where m is the slope and b is the y-intercept. positive correlation Ans: A linear relationship between two quantitative variables in which the dependent variable increases as the independent variable increases. regression line Ans: The line of best fit to show the relationship between variables, the one that minimizes distance from each data point to the line. statistically significant Ans: The presumption that a given result or relationship is caused by more than just random chance. degrees of freedom Ans: A number whose value is one less than the sample size, when conducting a hypothesis test. scatterplot Ans: A graph that uses dots on a coordinate plane to show the relationship between variables. linear interpolation Ans: Estimation using the linear regression equation in between known data points. extrapolate Ans: Using information from a data set to make predictions about data outside of the original set. coordinate plane Ans: A tool for graphing consisting of a horizontal x-axis and a vertical y-axis. degree Ans: The largest exponent in a mathematical expression or equation. least squares Ans: A technique for finding the regression line. association Ans: A pattern or relationship between two variables. lurking variable Ans: A variable that is not included in an analysis but that is related to two (or more) other associated variables which were analyzed. negative correlation Ans: A linear relationship between two quantitative variables in which the dependent variable increases as the independent variable decreases. significance level Ans: The p-value cutoff for statistical significance. Any p-value below the set significance level is considered statistically significant. linear extrapolation Ans: Estimation using the linear regression equation is made outside known data points. experimental study Ans: The researcher applies a treatment to one group and no treatment (or placebo) to a control group, to determine if there is causation between variables. significant difference Ans: A measurable difference between two groups or samples that reflects a real difference, rather than the difference being by chance. correlation Ans: An observed relationship between two quantitative variables. While this is most commonly a linear relationship, it does not need to be. Note that observing a relationship does NOT imply that there is a meaningful causal link between the variables. control group Ans: In an experimental study, this group does not receive treatment or receives a placebo. regression analysis Ans: A statistical analysis tool that quantifies the relationship between a response variable and one or more explanatory variables. causal relationship Ans: A relationship between two variables that can be classified as cause-and-effect.