Class 3 - Linear Association

Class 3 - Linear Association - Linear Association between T...

Info iconThis preview shows pages 1–5. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Linear Association between T wo Quantitative Var iab We will use three methods to assess linear association between two quantitative variables. 1. X-Y Scatter plot If one variable is clearly a variable who's value is a respo the value of another variable then the r esponse var iable (dependent variable) is on the Y or ver tical axis and the explanator y var iable (independent variable) is on the X or hor izontal axis . Examine the scatterplot to visually determine if there appears to be an associatio between the two variables. 2. The Cor r elation Coefficient (r ) is a unit-free measure of the amount linear association (relationship) between two quantitative variables. Measureme values range from -1 to +1. r=0 implies no linear association. The amount or st of linear association increases as r departs from 0 moving toward either -1 or +1. implies a positive assocation where one expects the value of one variable to incre the value of the other increases. r<0 implies a negative assocation where one expec value of one variable to decrease if the value of the other increases. If r=-1 or if r=1, th are all exactly on a straight line having negative slope for r=-1 and a positive slope for r= Since other measures of correlation exist , the coefficient used in this class and by the te often called the Pearson Correlation Coefficient. 3. Linear Regr ession uses the data to estimate the values to determine a linea equation to describe the relationship between the two quantitative variables using the explanatory variable (independent or X variable) to predict the response variable (depen or Y variable). bles onse to plotted plotted on t of ent trength . r>0 ease if cts the the data r=1. ext is ar ndent Supermarket Sales data from Sharpe Chapter 8 Data set Town Sales Population S-mean P-mean 1 5540456 42389-371520.2-97.1 ### 9428.41 2 10699662 57107 4787685.8 14620.9 ### ### 3 10531653 101355 4619676.8 58868.9 ### ### 4 5995282 66910 83305.8 24423.9 ### ### 5 5090095 28911-821881.2-13575.1 ### ### 6 3955072 14412-1956904.2-28074.1 ### ### 7 2773754 11081-3138222.2-31405.1 ### ### 8 4828213 25749-1083763.2-16737.1 ### ### 9 5510831 19607-401145.2-22879.1 ### ### 10 4194744 57340-1717232.2 14853.9 ### ### Mean 5911976.2 42486.1 Sum ### ### Std. Dev. ### 28399.52 ### 28399.52 (pg. 167) Correlation = Correlation 0.75 =CORREL(B3:B12,C3:C12) Sales Population Sales 1 Population 0.75 1 (S-mean) 2 (P-mean) 2 1. Cor r e pages 16 gr een an 2. Cor r e CORREL 3. Cor r e Data Ana (S-mean)*(P-mean) 36074611.42-0.14 70000275313.22 1.81 0.51 0.93 271955291571.52 1.75 2.07 3.62 2034652528.62 0.03 0.86 0.03 11157119478.12-0.31-0.48 0.15 54938324201.22-0.74-0.99 0.73 98556182013.2298556182013....
View Full Document

This note was uploaded on 06/20/2011 for the course MGMT 524 taught by Professor Andrews,r during the Spring '08 term at VCU.

Page1 / 18

Class 3 - Linear Association - Linear Association between T...

This preview shows document pages 1 - 5. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online