# Section 3 Part 1. Relationships between two numerical variables

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1 Section 3 Part 1 Relationships between two numerical variables 1

2 Relationship between two variables The summary statistics covered in the previous lessons are appropriate for describing a single variable. In many investigations, there is interest in evaluating the relationship between two variables. Depending on the type of data, different statistics are used to measure the relationship between two variables. 2

3 Measuring relationship between two variables Nominal binary data Odds Ratio or Risk Ratio (covered in Section 3 part 2) Numerical data Pearson Correlation Coefficient Ordinal data or Skewed Numerical data Spearman Rank Correlation Coefficient 3

4 Correlation Coefficients Correlation coefficients measure the linear relationship between two numerical variables. Most often the Pearson Correlation Coefficient is used to describe the linear relationship Note if just the term correlation coefficient is used, it is referring to the Pearson Correlation Coefficient If one variable is numerical and the other variable is ordinal or if both variables are ordinal, use the Spearman rank correlation coefficient instead. 4

5 Correlation Analysis Examples For correlation analysis, you have pairs of values for each individual (x, y). The goal is to measure the linear relationship between these values. For example, what is the linear relationship between: Weight and height Blood pressure and age Daily fat intake and cholesterol Weight and cholesterol Age and bone density 5

6 Types of Relationships between 2 variables There are 2 types of relationships between numerical variables Deterministic relationship the values of the 2 variables are related through an exact mathematical formula. Statistical relationship this is not a perfectly linear relationship. This is what we will explore through correlation analysis. 6

7 Example of Deterministic Linear Relationship Weight in kg Deterministic linear relationship Weight in pounds The relationship between weight measured in lbs. And weight measured in kg. is deterministic because it can be described with a Mathematical formula: 1 pound = *kg All the points in the plot fall on a straight line in a deterministic relationship 7

8 Statistical Linear Relationship Var Y Var X In a statistical linear relationship the plotted (x,y) values do not fall exactly on a straight line. You can see, however, that there is a general linear trend in the plot of the (x,y) values. The correlation coefficient provides information about the direction and strength of the linear relationship between two numerical values. 8

9 Nonlinear Relationship Var. Y Nonlinear relationship Var. X A nonlinear relationship between two variables results in a scatter plot of the (x,y) values that do not follow a straight line pattern. The relationship between these two variables is a curvilinear relationship. The correlation coefficient does not provide a description of the strength and direction of nonlinear relationships. 9

10 Random scatter: no pattern IQ Height This scatter plot of IQ and height for 18 individuals illustrates the situation where there is no relationship between the two variables. The (x,y) values do not have either a linear or curvilinear pattern. These points have a random scatter, indicating no relationship. 10

11 Scatter Plots Plot the 2 variables in a scatter plot The pattern of the dots in the plot indicates the direction of the statistical relationship between the variables Positive correlation (weight and height) Negative correlation (age and bone density) 11

12 Height and Weight: a positive linear relationship Ht (in) Wt (lbs) Weight (lbs) Height (In) 12

13 Age and Bone Density (mg/cm 2 ): a negative linear relationship Age Bone Density Bone density Bone Density and Age for Women Age 13

14 Strength of the Association The correlation coefficient provides a measure of the strength and direction of the linear relationship. The sign of the correlation coefficient indicates the direction Positive coefficient => Negative coefficient => Strength of association Correlation coefficients closer to 1 or -1 indicates a stronger relationship Correlation coefficients = 0 (or close to 0) indicate a nonexistent or very weak linear relationship 14

15 Correlation Coefficient r = [ ( x ( x x) 2 x)( y ][ y) ( y y) 2 ] The statistic r is called the Correlation Coefficient r is always between 1 and 1 The closer r is to 1 or -1, the stronger the linear relationship 15

16 Strong positive relationship r = 0.85 Weight (lbs) Height (In) The correlation coefficient for the (height, weight) data = 0.85 indicating a strong (close to 1.0), positive linear relationship between height and weight. 16

17 Strong negative relationship 1100 Bone Density and Age for Women Bome density r = Age The correlation coefficient for the (age, bone density) data = indicating a strong (close to -1.0), negative linear relationship between age and bone density for women. 17

18 Correlation Coefficient Interpretation: Guidelines* These guidelines can be used to interpret the correlation coefficient 0 to 0.25 ( or 0 to 0.25) weak or no linear relationship 0.25 to 0.5 (or 0.25 to 0.5) fair degree of linear relationship 0.50 to 0.75 (or 0.5 to 0.75) moderately strong linear relationship > 0.75 ( or -0.75) very strong linear relationship 1 or 1 perfect linear relationship deterministic relationship * Colton (1974) 18

19 Nonlinear Relationship: r = 0 Nonlinear relationship 15 Var. Y Var. X This plot illustrates that the correlation coefficient only measures the strength of linear relationships. Here there is a nonlinear (curvilinear) relationship between X and Y but the correlation coefficient = 0. 19

20 Random Scatter: r = IQ Height When there is a random scatter of points, the correlation coefficient will be near 0 indicating no linear relationship between the variables 20

21 Notes about Correlation Coefficient The correlation coefficient is independent of the units used in measuring the variables The correlation coefficient is independent of which variable is designated the X variable and which is designated the Y variable 21

22 Correlation vs. Causation Strong correlation does not imply causation (i.e. that changes in one variable cause changes in the other variable). Example*: There is a strong positive correlation between the number of television sets /person and the average life expectancy for the world s nations. Does having more TV sets cause longer life expectancy? *Moore & McCabe 22

23 Coefficient of Determination The coefficient of Determination is the correlation coefficient squared (R 2 ) R 2 = the percent of variation in the Y variable that is explained by the linear association between the two variables From example of age / bone density: Correlation coefficient for age and bone density r = Coefficient of determination=r 2 = (-0.96) 2 = % of the variation in bone density is explained by the linear association between age and bone density 23

24 Effect of outlier on r One outlier has been added to the random pattern plot of height and IQ: a 74 inch tall person with IQ of 165 Addition of this one point results in r changing from to 0.44 Random Pattern with outlier r = 0.44 IQ Height 24

25 Correlation Coefficient and Outliers The Pearson s correlation coefficient can be influenced by extreme values (outliers). Outliers are most easily identified from a scatter-plot. If an outlier is suspected, calculate r with and without the outlier. If the results are very different, consider calculating the Spearman rank correlation coefficient instead. 25

26 Spearman Rank Correlation (Spearman s Rho) Non-parametric: analysis methods that make no assumptions about the data distribution (often based on the rank of a data piece). Spearman rank correlation is the appropriate correlation analysis to use when Both variables are ordinal data One variable is continuous and one is ordinal Continuous data is skewed due to outlier(s) 26

27 Spearman rank correlation Procedure Order the data values for each variable from lowest to highest Assign ranks for each variable (1 up to n) Use the ranks instead of the original data values in the formula for the correlation coefficient 27

28 Skewed Data due to outlier Pearson Correlation = With the outlier point (17,9) r = Without the outlier, r =

29 Original values and Rank values X Y X-rank Y-rank The smallest X value is given rank 1, the largest is given rank 7 Similarly, the smallest Y value is given rank 1, the largest is given rank 7 Other values are ranked according to their order. 29

30 Spearman s Rho: the Spearman rank correlation coefficient Ranked values of Y Spearman s Rho = Ranked values of X Ranking the values of X and Y removes the influence of the outlier Spearman s Rho is not as large as the Pearson s correlation coefficient for the original data (r = 0.44) and the outlier is not excluded from analysis. 30

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