regression line

regression line : Consider a collection of m linear regression problems for n observations, related through a set of common predictor variables $\backslash \{x\_\{c\}\backslash \}$, and a jointly normal errors $\backslash \{\backslash epsilon\_\{c\}\backslash \}$ : where the subscript c denotes a column vector of k observations for each measurement ($n..\; More\; from\; Wikipedia$

regression line : In statistics, Bayesian linear regression is a Bayesian alternative to ordinary least squares regression. Consider a standard linear regression problem, in which for $i=1,...,n$ we specify the conditional distribution of $y\_i\backslash ,$ given a $1\; \backslash times\; k$ predictor..   More from Wikipedia

  This video illustrates how to use the Sharp EL531W calculator to find the correlation coefficient (r-value) and the equation of a regression line. For more instructional videos, as well as exercise and answer sheets, go to: freemathtutoring.googlepages.com

  This is a continuation of the last video, "Plotting Statistical Data". It shows the steps for how to fit a best line using the Linear Regression features of the TI-84. Nearly the same steps can be used for other types of lines (ie quadratic, etc.). Created for my high-school Pre-Calculus students.

Question : which regression line is your graph? it's from my chemistry lab,,, but i don't get the question... do you know what's talking about?

Answer : The regression line is also referred to as the line of best fit. The most common and probably most useful regression is a linear fit ie a straight line...   More from Yahoo Answers

Question : For example: in terms of weight as a function of height? I calculated the regression line and my regression constant is very small (-496 or so) what does this tell me besides it determines the elevation of the line and that its the y intercept.

Answer : Very generally - it gives you an idea of (in your case) how much weight you would have if you had no height (I'm surprised your line has a regression constant, since it's difficult to have weight when you have no height and vice versa, but if you've taken sample data, the fact that it's small shows that your data is generally along the right lines)! Since the regression constant is not determined by the value of your variable (x), it represents the amount of your total value not determined by your variable (i.e. the amount of y not determined by x)...   More from Yahoo Answers

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