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Vocabulary Challenge

Simple Linear Regression Key Terms

Simple Linear Regression a quantitative statistical method that allows us to find the best model for a linear relationship between the explanatory (independent) and predictor (dependent) variables
Linear Regression Model DATA = FIT + RESIDUAL
Linear Regression Equation Ypred = βo + β1*Xexp + ϵ
Explanatory Variable the independent variable whose value can be used to predict the value of the predicted or response variable
Predicted Variable the dependent variable whose value can be explained by the value of the explanatory or the predictor variable
Scatterplot a graph that shows the relationship between two quantitative variables measured on the same individual (does not connect the points)
Slope the estimated rate of change of the response variable per unit change of the explanatory variable
y-Intercept the estimated value of the predicted variable when the value of the explanatory variable is zero
Residual the difference between the observed value of y and the predicted value of y
Correlation Coefficient used in statistics to determine how well the variables are related; a statistical measure of the linear correlation between the two given variables (also known as Pearson's r)
Least-Squares Regression Line the best fit line that is calculated by minimizing the sum of the squares of the differences between the observed and the predicted values of the line
Assumptions tests that must be met before moving forward with a linear regression
Linear Relationship correlational relationship in which the data points cluster around a straight line
Variance the expectation of the squared deviation of a random variable from its mean; informally measures how far a set of numbers are spread out from their mean
Homoscedasticity The data is evenly spread out around the line of regression
Normality used to determine if a data set is well-modeled by a normal distribution
Multicollinearity occurs when the independent variables are highly correlated among themselves in a multiple regression
Tolerance measures the influence of one independent variable on all other independent variables
Outlier a value that "lies outside" (is much smaller or larger than) most of the other values in a set of data
Confidence Interval a range of values so defined that there is a specified probability that the value of a parameter lies within it
Created by: fussy72