9.3  Regression Analysis · Part 12

Example for positive correlation is (a) Income and expenditure (b) Price and demand (c) Repayment period and EMI (d) Weight and Income . If the values of two variables move in same direction then the correlation is said to be (a) Negative (b) Positive (c) Perfect positive (d) No correlation . If the values of two variables move in opposite direction then the correlation is said to be - - (a) Negative (b) Positive (c) Perfect positive (d) No correlation . Correlation co-efficient lies between (a) to ∞ (b) – to + (c) – to (d) – to ∞ . If r ( X , Y ) = the variables X and Y are said to be (a) Positive correlation (b) Negative correlation (c) No correlation (d) Perfect positive correlation . The correlation coefficient from the following data N = , ∑ X = , ∑ Y = , ∑ X = , ∑ Y = , ∑ XY = (a) . (b) - . (c) – . (d) . . From the following data, N = , ∑X = , ∑ Y = , ∑ X = , ∑ Y = , ∑ XY =2827the correlation coefficient is (a) . (b) – . (c) (d) . . The correlation coefficient is (a) r ( X , Y )= ( , ) cov x y v v (b) r ( X , Y )= ( , ) cov x y v v (c) r ( X , Y )= ( , ) cov x y v (d) r ( X , Y )= ( , ) cov x y v . The variable whose value is influenced (or) is to be predicted is called (a) dependent variable (b) independent variable (c) regressor (d) explanatory variable . The variable which influences the values or is used for prediction is called (a) Dependent variable (b) Independent variable (c) Explained variable (d) Regressed . The correlation coefficient (a) r =± xy yx (b) r = b xy yx (c) r = b xy × b yx (d) r =± xy yx . The regression coefficient