STAT 210 1ST EditionGradeBuddy Exam 3 Study Guide Lectures 13-16Lecture 13 (Sep 25th) Difference between causal and association variables- Causal is where 1 variable causes or explains changes in the other variable whereas association is where 1 variable doesn’t cause change in another variablebut there is association- Independent variable is X- Dependent variable is YWhat are lurking variables?- Variables that have important effects on relationship between x and y bt isn’t included in the variables being studiedDefine directions of scatterplots- Positive = upward trend from left to right (x decreases so y decreases)- Negative = downward trend from left to right (x decreases so y decreases)- Linear – points follow linear pattern- Quadratic – points follow parabolic system- Exponential – points follow curved patternLecture 14 (Sep 27th)Define Regression Line- Relationship between x and yDefine y intercept and slope- Y = intercept + slope(x)- Slope is amount y changes when x is increased by 1 unitWhat is residual formula?- y – y hatLecture 15 (Sep 30th) What does the regression line help us predict?- We can predict the value of Y for any value of X simply by substituting the value of X into the regression equationDefine extrapolation- Predicting outside the range of the original X data (this should be avoided)How to do a residual plot- Compute the residual for each observation- Create a scatterplot with the independent variable (x) on the horizontal axis and the residuals on the vertical accessOutliers as defined by variables- One variable: an outlier is an observation that is significantly smaller or larger than the majority of the data.STAT 210 1ST Edition- Two variables: an outlier is an observation that falls within the range of the data in the horizontal (X) direction but that lies far from the regression line in the vertical direction and hence produces a large residual.What does the coefficient of determination measure?- It measures the proportion (fraction) of the total variation in the Y values that can be explained by the X values. Lecture 16 (Oct 2nd)Define marginal and conditional distributions- Marginal distribution lists the categories of the variable together with the frequency (count) or relative frequency (percentage) of observations in each category- Conditional Distribution can be in terms of frequencies (counts) or relative frequencies