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Bivariate Analysis. S Tauvette , S Aldous. Univariate and Bivariate Analysis. Univariate analysis involves a single variable – Example: The height of all of the women basketball players in the WNBA. Comparing their heights and weights is called bivariate analysis.
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Bivariate Analysis S Tauvette, S Aldous
Univariate and Bivariate Analysis Univariate analysis involves a single variable – Example: The height of all of the women basketball players in the WNBA. Comparing their heights and weights is called bivariate analysis.
Is there a relationship? Think of some pairs of variables you think are related in similar ways to these three examples.
Key Words Take turns with your partner. On your turn, pick and word and tell your partner what it means.
Play Correlation Four-in-a-Line with your partner. • Take turns. On your turn, say what correlation the situation has. If your partner agrees, put your coin or counter on that square. • The winner is the one who gets four in a line first.
Never, Sometimes, or Always True? • Look at the cards. Decide if each statement is never, sometimes, or always true. Sort the cards. • Provide evidence for your decisions.
The PMCC • The product moment correlation coefficient, r, is a measure of the linear correlation of two variables. • The PMCC takes values between - 1 and 1. • A PMCC value of 1 implies perfect positive linear correlation and a value of - 1 implies perfect negative linear correlation. • A value of 0 implies no linear correlation.
Using your GDC to find PMCC (a) Find the mean number of study hours and errors for these pupils. (b) Find the standard deviation of hours and errors. (c) Write down the value of r for this data. (d) Interpret your value for r. GDC: 1. After resetting, you must use the Catalogue to turn diagnostic ON 2. Enter data into L1 and L2 3. Find the standard deviation by using 2-Var Stats 4. Find r by using LinReg
