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Q: What can you say about a slope a regression line for variables that are negatively associated?

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line that measures the slope between dependent and independent variables

Yes

The strength of the linear relationship between the two variables in the regression equation is the correlation coefficient, r, and is always a value between -1 and 1, inclusive. The regression coefficient is the slope of the line of the regression equation.

Two variables are negatively correlated when the slope of the best-fit line that is drawn on the scatter plot with the independent variable on the x-axis and the dependent variable on the y-axis is negative.

The graph and accompanying table shown here display 12 observations of a pair of variables (x, y).The variables x and y are positively correlated, with a correlation coefficient of r = 0.97.What is the slope, b, of the least squares regression line, y = a + bx, for these data? Round your answer to the nearest hundredth.2.04 - 2.05

It guarantees that the slope and intercept are minimized.

slope

False.

The sign is negative.

The value depends on the slope of the line.

Linear regression looks at the relationship between two variables, X and Y. The regression line is the "best" line though some data you that you or someone else has collected. You want to find the best slope and the best y intercept to be able to plot the line that will allow you to predict Y given a value of X. This is usually done by minimizing the sum of the squares. Regression Equation is y = a + bx Slope(b) = (NΣXY - (ΣX)(ΣY)) / (NΣX2 - (ΣX)2) Intercept(a) = (ΣY - b(ΣX)) / N In the equation above: X and Y are the variables given as an ordered pair (X,Y) b = The slope of the regression line a = The intercept point of the regression line and the y axis. N = Number of values or elements X = First Score Y = Second Score ΣXY = Sum of the product of first and Second Scores ΣX = Sum of First Scores ΣY = Sum of Second Scores ΣX2 = Sum of square First Scores Once you find the slope and the intercept, you plot it the same way you plot any other line!

Linear regression looks at the relationship between two variables, X and Y. The regression line is the "best" line though some data you that you or someone else has collected. You want to find the best slope and the best y intercept to be able to plot the line that will allow you to predict Y given a value of X. This is usually done by minimizing the sum of the squares. Regression Equation is y = a + bx Slope(b) = (NΣXY - (ΣX)(ΣY)) / (NΣX2 - (ΣX)2) Intercept(a) = (ΣY - b(ΣX)) / N In the equation above: X and Y are the variables given as an ordered pair (X,Y) b = The slope of the regression line a = The intercept point of the regression line and the y axis. N = Number of values or elements X = First Score Y = Second Score ΣXY = Sum of the product of first and Second Scores ΣX = Sum of First Scores ΣY = Sum of Second Scores ΣX2 = Sum of square First Scores Once you find the slope and the intercept, you plot it the same way you plot any other line!

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