The regression between height and age follows y=a+bx. The curve is
Ans. is 'c' straight line Regression:If two variables are highly correlated, it then becomes possible to predict the value of the dependent variable from the value of the independent variable by using regression technique .Regression analysis is the mathematical modelling to describe the effect that one or more independent variables have on a dependent variable.Types:Linear regression: The value of one variable (X) is used to predict the value of the other variable (Y) by means of a simple mathematical formula. In linear regression the dependent variable (Y) depends upon a single independent variable (X).The linear regression equation takes the form as the equation for any straight line. y = a + bx where - a = is a constant k/a intercept constant b = is the regression coefficient x = is the independent variable.y = is the dependent variable whose value is to be found out.Multiple regression: More than one variable is used to predict the expected value of Y.Y = a + b1X1 + b2x2 +b1x1 where a = intercept constantb = regession coefficient for independent variable X1 eg. Rubin found that the birth weight of a baby (Y in grams) can be partially predicted from the no. of cigarettes smoked on the daily basis by both the baby's mother and the baby's father (X2). According to multiple regression equation Y = 3385 - 9x1 - 6x2Nonlinear regression: Involves more complicated mathematical forms, including logarithmic functions.
Hyperbola
Sigmoid
Straight line
parabola
Biostatistics