Correlation and Regression

Correlation What type of relationship exists between the two variables and is the correlation significant? x y Cigarettes smoked per day Score on SAT Height Hours of Training Explanatory (Independent) Variable Response (Dependent) Variable A quantitative relationship between two interval or ratio level variables Number of Accidents Shoe SizeHeight Lung Capacity Grade Point Average IQ

Correlation  measures and describes the strength and direction of the relationship  Bivariate techniques requires two variable scores from the same individuals (dependent and independent variables)  Multivariate when more than two independent variables (e.g effect of advertising and prices on sales)  Variables must be ratio or interval scale

Negative Correlation–as x increases, y decreases x = hours of training (horizontal axis) y = number of accidents (vertical axis) Scatter Plots and Types of Correlation Hours of Training Accidents

Positive Correlation–as x increases, y increases x = SAT score y = GPA GPA Scatter Plots and Types of Correlation Math SAT

No linear correlation x = height y = IQ Scatter Plots and Types of Correlation Height IQ

Strong, negative relationship but non-linear! Scatter Plots and Types of Correlation

Correlation Coefficient A measure of the strength and direction of a linear relationship between two variables The range of r is from –1 to 1. If r is close to 1 there is a strong positive correlation. If r is close to –1 there is a strong negative correlation. If r is close to 0 there is no linear correlation. –1 0 1

Outliers..... Outliers are dangerous Here we have a spurious correlation of r=0.68 without IBM, r=0.48 without IBM & GE, r=0.21

x y Absences Final Grade Application Final Grade X Absences

xy x 2 y2y2 Computation of r x y

r is the correlation coefficient for the sample. The correlation coefficient for the population is (rho). The sampling distribution for r is a t-distribution with n – 2 d.f. Standardized test statistic For a two tail test for significance: Hypothesis Test for Significance (The correlation is not significant) (The correlation is significant)

A t-distribution with 5 degrees of freedom Test of Significance The correlation between the number of times absent and a final grade r = – There were seven pairs of data.Test the significance of this correlation. Use = Write the null and alternative hypothesis. 2. State the level of significance. 3. Identify the sampling distribution. (The correlation is not significant) (The correlation is significant) = 0.01

t –4.032 Rejection Regions Critical Values ± t 0 4. Find the critical value. 5. Find the rejection region. 6. Find the test statistic. df\p

t 0 –4.032 t = –9.811 falls in the rejection region. Reject the null hypothesis. There is a significant negative correlation between the number of times absent and final grades. 7. Make your decision. 8. Interpret your decision.

The equation of a line may be written as y = mx + b where m is the slope of the line and b is the y- intercept. The line of regression is: The slope m is: The y-intercept is: Regression indicates the degree to which the variation in one variable X, is related to or can be explained by the variation in another variable Y Once you know there is a significant linear correlation, you can write an equation describing the relationship between the x and y variables. This equation is called the line of regression or least squares line. The Line of Regression

Ad $ = a residual (xi,yi)(xi,yi) = a data point revenue = a point on the line with the same x-value Best fitting straight line

Calculate m and b. Write the equation of the line of regression with x = number of absences and y = final grade. The line of regression is:= –3.924x xy x 2 y2y2 x y

Absences Final Grade m = –3.924 and b = The line of regression is: Note that the point = (8.143, ) is on the line. The Line of Regression

The regression line can be used to predict values of y for values of x falling within the range of the data. The regression equation for number of times absent and final grade is: Use this equation to predict the expected grade for a student with (a) 3 absences(b) 12 absences (a) (b) Predicting y Values = –3.924(3) = = –3.924(12) = = –3.924x

The correlation coefficient of number of times absent and final grade is r = – The coefficient of determination is r 2 = (–0.975) 2 = Interpretation: About 95% of the variation in final grades can be explained by the number of times a student is absent. The other 5% is unexplained and can be due to sampling error or other variables such as intelligence, amount of time studied, etc. Strength of the Association The coefficient of determination, r 2, measures the strength of the association and is the ratio of explained variation in y to the total variation in y.