by tracing the lines to the side of the bar and adding the lines.
Find the number that is most to the right of the line plot.
Mean numbers are the average. To find the mean or the average, you have to add all the numbers in the data and you divide the sum by the total number of numbers there are. You divide the sum by how much numbers there are. For example, if the data numbers for a line plot is 1,2,3,4,2,0. You do this: 1+2+3+4+2+0=12 (Add all the numbers together) 12 divided by 6 = 3 (Take the sum and divide by how much numbers there are) 3 = mean number or average So you just take the numbers from the data or if you're gonna plot it on a line, just take those numbers that you're going to plot and do what I did up there.
The sum of the squares of the deviations (how far each point is from the line) is less, for such a line, than for any other line.
listen in school
divide by the gradient
Answer - The mode of a line plot is the number that shows up the most. ex. 1,2,3,3,4,4,6,6,7,8,8,8,9 mode = 8
ask someone who knows
There is no typical number.
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!
So that you can plot out the points of a straight line on graph paper.
The scale that you find appropriate. If you have a plot of line distances between cities for example you may use a scale 100000 to 1.