1 / 67

Correlation

Correlation. Correlation. Correlation refers to a relationship that exists between pairs of measures. Knowledge of the strength and direction of the relationship allows us to predict one variable from the other with an accuracy greater than chance. Correlation.

dot
Download Presentation

Correlation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Correlation

  2. Correlation Correlation refers to a relationship that exists between pairs of measures. Knowledge of the strength and direction of the relationship allows us to predict one variable from the other with an accuracy greater than chance.

  3. Correlation For example, you can guess someone’s weight more accurately if you know how tall they are because height and weight are positively correlated.

  4. Correlation For example, you can guess someone’s weight more accurately if you know how tall they are because height and weight are positively correlated. When two variables are positively correlated that means they tend to both move higher or both move lower at the same time.

  5. Correlation For example, you can guess someone’s weight more accurately if you know how tall they are because height and weight are positively correlated. When two variables are positively correlated that means they tend to both move higher or both move lower at the same time. Generally, taller people weigh more than shorted people.

  6. Correlation When two variable are negatively correlatedthat means that they change in value inversely. Higher scores on one generally go with lower scores on the other.

  7. Correlation When two variable are negatively correlatedthat means that they change in value inversely. Higher scores on one generally go with lower scores on the other. For example, the outdoor temperature and the weight of one’s clothing are negatively correlated. The higher the temperature is, the less clothing we wear. The lower the outdoor temperature, the more clothing we wear.

  8. Correlation When two variable are negatively correlatedthat means that they change in value inversely. Higher scores on one generally go with lower scores on the other. For example, the outdoor temperature and the weight of one’s clothing are negatively correlated. The higher the temperature is, the less clothing we wear. The lower the outdoor temperature, the more clothing we wear. Another example: How old you are and how much longer you will live are negatively correlated.

  9. Correlation When two variable are negatively correlatedthat means that they change in value inversely. Higher scores on one generally go with lower scores on the other. For example, the outdoor temperature and the weight of one’s clothing are negatively correlated. The higher the temperature is, the less clothing we wear. The lower the outdoor temperature, the more clothing we wear. Another example: How old you are and how much longer you will live are negatively correlated. The existence of a negative correlation does not mean the absence of a relationship. It means that the variables tend to move in opposite directions, not that the variables are unrelated.

  10. Correlation A near zero correlationis said to exist when scores on the two variables are unrelated. Higher scores on one variable are just as likely to be accompanied by higher scores as by lower scores on the other variable.

  11. Correlation A near zero correlationis said to exist when scores on the two variables are unrelated. Higher scores on one variable are just as likely to be accompanied by higher scores as by lower scores on the other variable. An example: The street number on your house and the odometer reading of your car.

  12. Correlation For Each of the Following Examples, State From Your General Knowledge Whether the Correlation Between the Two Variables is Likely to be Positive, Negative, or Near Zero, and Explain Why

  13. Average Number of Calories Eaten Per Day and Body Weight

  14. Average Number of Calories Eaten Per Day and Body Weight Positive – Caloric Intake is One of the Major Determinants of Body Weight

  15. Golf Scores and the Number of Years of Golfing Experience

  16. Golf Scores and the Number of Years of Golfing Experience Negative – Golfers Improve with Experience and Hence Would Be Expected to Get Better (Lower) Scores

  17. Length of Hair and Shoe Size in Adult Males

  18. Length of Hair and Shoe Size in Adult Males Near Zero – It’s Doubtful That These Two Measures Could Be Influenced By Common Factors

  19. Amount of Formal Education One Has Received and the Time Spent Collecting Public Assistance (Welfare)

  20. Amount of Formal Education One Has Received and the Time Spent Collecting Public Assistance (Welfare) Negative – Educated Individuals Are More Likely to Be Employable and Hence Less Likely to Need Welfare

  21. Per Capita Consumption of Alcohol in a Group of Cites and Suicide Rates in Those Cities

  22. Per Capita Consumption of Alcohol in a Group of Cites and Suicide Rates in Those Cities Positive – A Common Set of Stresses and Other Factors Are Likely to Influence Rates of Both Alcoholism and Suicide in a Given Community

  23. Number Correct on a Current Events Test and Time Spent Reading the Newspaper

  24. Number Correct on a Current Events Test and Time Spent Reading the Newspaper Positive – The Newspapers Are Full of Stories Concerning Current Events Around the World

  25. Strength of Traditional Religious Beliefs and Favorableness of Attitude Toward Abortion on Demand

  26. Strength of Traditional Religious Beliefs and Favorableness of Attitude Toward Abortion on Demand Negative – Members of Traditional Religious Groups Are More Likely to Regard Abortion as Immoral Than Others

  27. Height and Political Conservatism

  28. Height and Political Conservatism Near Zero

  29. Correlation Correlations can be represented either graphically by the construction of a special type of graph called a scatter-plot diagram or through the computation of an index called a coefficient of correlation.

  30. Correlation Correlations can be represented either graphically by the construction of a special type of graph called a scatter-plot diagram or through the computation of an index called a coefficient of correlation. Most of the seminal work that went into the development of these representation was done by the early statistician Karl Pearson, employed by the Guinness Brewery.

  31. Correlation Construction of a scatter-plot diagram requires acquiring pairs of scores from each subject. For example, suppose we wanted to look at the relationship between height and self esteem in men. Perhaps we have a hypothesis that how tall you are effects your level of self esteem. So we collect pairs of scores from twenty male individuals. Height, measured in inches, and Self Esteem based on a self-rating scale (where higher scores mean higher self esteem).

  32. Linear Regression

  33. Linear Regression Best Fitting Line Line of Prediction

  34. Coefficient of Correlation

  35. Coefficient of Correlation A statistical computation that indicates the strength and direction of an underlying correlation

  36. Coefficient of Correlation A statistical computation that indicates the strength and direction of an underlying correlation

  37. Coefficient of Correlation A statistical computation that indicates the strength and direction of an underlying correlation Always results in a signed number in the range from -1.00 to +1.00 If the sign is positive, that indicates the underlying relationship is a positive correlation. If the sign is negative, it indicates an underlying negative correlation. The closer the value is to “1” (either positive or negative) the stronger is the indicated underlying relationship.

  38. Coefficient of Correlation A statistical computation that indicates the strength and direction of an underlying correlation Always results in a signed number in the range from -1.00 to +1.00 If the sign is positive, that indicates the underlying relationship is a positive correlation. If the sign is negative, it indicates an underlying negative correlation. The closer the value is to “1” (either positive or negative) the stronger is the indicated underlying relationship.

  39. Coefficient of Correlation A statistical computation that indicates the strength and direction of an underlying correlation Always results in a signed number in the range from -1.00 to +1.00 If the sign is positive, that indicates the underlying relationship is a positive correlation. If the sign is negative, it indicates an underlying negative correlation. The closer the value is to “1” (either positive or negative) the stronger is the indicated underlying relationship.

  40. Coefficient of Correlation r = +.86

  41. Coefficient of Correlation Sign r = +.86

  42. Coefficient of Correlation Magnitude Sign r = +.86

  43. Coefficient of Correlation Magnitude Sign r = +.86 Direction

  44. Coefficient of Correlation Magnitude Sign r = +.86 Direction Strength

  45. Coefficient of Correlation r = +.86 r = +.31 r = -.96 r = +.04 r = +1.02

More Related