Correlation
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This document discusses correlation and provides examples of its applications. It begins with an introduction that defines correlation as measuring the linear relationship between two variables. It then provides definitions of positive and negative correlation. The next sections discuss types of correlation based on degree, number of variables, and linearity. Correlation coefficient is introduced as a measure of the strength of the linear relationship between -1 and 1. Examples of its applications include the relationships between tree cutting and erosion, study time and test scores, clothing size and growth. Limitations of only considering linear relationships are also covered. Real-life examples of positive, negative, and no correlation between variables like temperature and sales, exercise and body fat, and weather and sales are presented.
Correlation
- 1. WELCOME
- 2. TOPIC
- 3. Presented by Anika Ohab ID:142-15-3568 Abul Hasnath ID:142-15-3532 Umme Habiba ID: 142-15-3677 Shahinur Rahman ID: 142-15-3606
- 4. Contents Introduction Definition Types Of Correlation Correlation Coefficient Types of Correlation Coefficient Limitation Of Correlation Applications
- 5. Introduction Correlation analysis show us how to determine both the nature and strength of relationship between two variables. The word Correlation is made of Co- (meaning "together"), and Relation One of the best statistical tests out there, in my opinion, is the correlation. Correlation is a mutual relationship between two variables.
- 6. DEFINITION A correlation is a linear relationship between two variables. Correlation measures the linear association between two variables.
- 7. Types of correlation On the basis of degree of correlation On the basis of number of variables On the basis of linearity •Positive correlation •Negative correlation •Simple correlation •Partial correlation •Multiple correlation •Linear correlation •Non – linear correlation
- 8. x y It is a relationship between two variables where if one variable increases, the other one also increases. A positive correlation also exists in one decreases and the other also decreases. . POSITIVE CORRELATION
- 9. THAT MEANS THERE IS AN INVERSE RELATIONSHIP BETWEEN TWO VARIABLES - WHEN ONE VARIABLE DECREASES, THE OTHER INCREASES x y Negative Correlation
- 10. CORRELATION COEFFICIENT The Coefficient of Correlation (r) is a measure of the strength of the linear relationship between two variables. A decimal number between .00 and +1.00 or –1.00 that indicates the degree to which two quantitative variables are related. 0.00 +1.00-1.00 no relationship strong positivestrong negative
- 11. 1. Perfect Positive correlation 2. Perfect negative correlation 3. Moderately Positive correlation 4. Moderate negative correlation 5. Absolute no correlation TYPES OF CORRELATION COEFFICIENT
- 12. Limitations of Correlation Although correlation is a powerful tool, there are some limitations in using it: 1.Correlation does not completely tell us everything about the data. Means and standard deviations continue to be important. 2.The data may be described by a curve more complicated than a straight line, but this will not show up in the calculation of r. 3.Just because two sets of data are correlated, it doesn't mean that one is the cause of the other.
- 13. Application 1.As the number of trees cut down increases, the probability of erosion increases. 2.As a student’s study time increases, so does his test average. 3.As a child grows, so does his clothing size. 4.As her salary increased, so did her spending. REAL LIFE Application Of Positive Correlation :
- 14. Application •student absences --------- grades •weather cooling --------air conditioning costs •train speed -------- length of final point •chicken age --------amount of eggs producing REAL LIFE Application Of Negative Correlation:
- 15. Application Temperatu re in ˚C Sales 17.2 ˚C $408 22.6 ˚C $445 18.1 ˚C $421 23.4 ˚C $544 25.1 ˚C $614 19.4 ˚C $412 22.1 ˚C $522 18.5 ˚C $406 15.2 ˚C $332 11.9 ˚C $185 16.4 ˚C $325 14.2 ˚C $215 ##Temperature --- Sales Income
- 16. Application On a scatter plot, here is the same data: So, we can see that more sales occur during warmer weather
- 17. Application ##amount of exercise ---- % body fat Hours of exercise Bodyfat
- 18. Application Weather gets so hot--- sales start Here is the latest graph: The correlation is now 0: "No Correlation" ... !