bus-stat-book1

# The variable predicted on the basis of other

This preview shows page 1. Sign up to view the full content.

This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: ents. Marks in 70 Test I Marks in 65 Test II 68 67 55 60 60 75 63 60 72 65 80 60 68 58 75 62 60 70 216 51. Calculate spearman’ s Rank correlation coefficient for the following table of marks of students in two subjects. First 80 64 54 49 48 35 32 29 20 18 15 10 subject Second 36 38 39 41 27 43 45 52 51 42 40 52 subject IV. Suggested Activities Select any ten students from your class and find their heights and weights. Find the correlation between their heights and weights Answers: I. 1. (a). 2. (d) 6. (c) 7. (a) II. 11. Units 14. Pearson 17. Qualitative III. 30. r = 0.9574 33. n = 10 36. r = - 0.96 39. r = +0.64 42. r = +0.746 45. r = +0.0945 48. r = - 0.905 51. r = 0.685 3. (b) 8. (b) 4.(b) 5. (a) 9. (c) 10. (b) 12. Scatter diagram 15. Positive perfect 18. No correlation 13. Multiple 16. Symmetric 31. r = 0.85 34. r = +0.58 37. r = +0.68 40. r = +0.1 43. r = +0.533 46. r = +0.62 49. r = 0.34 32. y = 6.25. 35. r = +0.98 38. r = - 0.92 41. r = +0.98 44. r = +0.596 47. r = - 0.93 50. r = 0.679 217 9. REGRESSION 9.1 Introduction: After knowing the relationship between two variables we may be interested in estimating (predicting) the value of one variable given the value of another. The variable predicted on the basis of other variables is called the “dependent” or the ‘ explained’ variable and the other the ‘ independent’ or the ‘ predicting’ variable. The prediction is based on average relationship derived statistically by regression analysis. The equation, linear or otherwise, is called the regression equation or the explaining equation. For example, if we know that advertising and sales are correlated we may find out expected amount of sales for a given advertising expenditure or the required amount of expenditure for attaining a given amount of sales. The relationship between two variables can be considered between, say, rainfall and agricultural production, price of an input and the overall cost of product, consumer expenditure and disposable income. Thus, regression analysis reveals average relationship between two variables and this...
View Full Document

## This note was uploaded on 01/18/2014 for the course BUS 100 taught by Professor Moshiri during the Winter '08 term at UC Riverside.

Ask a homework question - tutors are online