STAT 1000 Lecture Notes - Lecture 11: Dependent And Independent Variables, Yottabyte

• School: University of Manitoba
• Department: Statistics
• Course: STAT 1000
• Professor: Zenaida Mateo

Document Summary

X is your predictor variable for y (explanatory variable) Regression method : (cid:1877) = (cid:1828)(cid:1004)+(cid:1828)(cid:1005)+ (cid:1005) (cid:1828)(cid:1005)= slope of the line (cid:1877) = predicted value for y. There are a few formulas when calculating the slope and intercept of the least square regression line: The least-squares regression line is the line that minimizes the sum of the squares of the residual. The residual is the dista(cid:374)(cid:272)e fro(cid:373) our o(cid:271)ser(cid:448)ed (cid:862)y(cid:863) (cid:448)alue to our li(cid:374)e. Just rearranging from the original formula/ equation. = 0. 019 r = 0. 9876 (cid:1876)(cid:1005)= (cid:1007)(cid:1009)(cid:1004) (cid:1877)(cid:1005)= (cid:1007)(cid:1009)(cid:1004) (cid:1876)= (cid:1009)(cid:1004) (cid:1877)= (cid:1005)(cid:1013). (cid:1013)(cid:1006)(cid:1013) = (cid:1006)(cid:1005). (cid:1010)(cid:1004)(cid:1006) = (cid:1012). (cid:1011)(cid:1005)(cid:1004) (cid:1006)(cid:1005). (cid:1010)(cid:1004)(cid:1006)(cid:4667) (cid:1012). (cid:1011)(cid:1005)(cid:1004) (cid:1828)(cid:1005)= (0. 9876)( (cid:1828)(cid:1004)= (cid:1877) (cid:1828)(cid:1005) (cid:1828)(cid:1004)= 19. 929 - (0. 39982)(50) (cid:1828)(cid:1005)=(cid:1870) (cid:4666)(cid:1871)(cid:3052)(cid:1871)(cid:3052)(cid:4667) The least squares regression line would be : (cid:1877) = 0. 019 + 0. 3982(cid:1876) *this (cid:374)e(cid:449) ter(cid:373)s (cid:373)ay (cid:271)e (cid:448)ery (cid:272)o(cid:374)fusi(cid:374)g (cid:271)ut it"s (cid:271)asi(cid:272)ally y=m(cid:1876)+(cid:1829)which we learned in high. If the question is asking for (cid:1870)(cid:1006)then just square root your r.