Yes....one is an enlargement of the other!
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Answer: <span>C) The slope of the line of best fit would increase because the point lies below the original line of best fit.
The line of best fit is a line that best represents the data in a scatterplot. When you draw a line of best fit, you want it to roughly "balance out" the points above and below it on the scatter plot, making sure the points are distributed evenly.
It's easiest to visualize what a point above or beneath the graph would do. A point underneath that line would be "pulling the line down," so it would be decreasing the slope (making the line more horizontal). A point above the line would be "pulling the line up," so it would be increasing the slope.
1) Figure out where (15, 7) is in relation to the line of best fit. Plug x=15 into </span><span>y = 0.5x + 1.5 to find where the line is when x=15:
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That means (15, 7) is under the line, since y=7 at x=15 for the point, but y=9 for the graph.
2) Since (15, 7) is under the line, you can imagine it to be "pulling the line of best fit down" and decreasing the slope. If it's removed then the line would become steeper (aka larger slope), making c the answer.
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Answer: C) The slope of the line of best fit would increase because the point lies below the original line of best fit. </span>
Answer:
34 X + 95 is equal to 3 into bracket 14 x 1 + 9 (close
Step-by-step explanation:
it it is the for the algebra of class 10th not for 7 for all other classes
Answer: C.) The coefficient of determination is 0.215. 21.5% of the variation is explained by the linear correlation, and 78.5% is explained by other factors.
Step-by-step explanation:
Given that :
Number of observations = 40
Linear Correlation Coefficient (R) = 0.464
The Coefficient of determination ( R^2) =?
The Coefficient of determination (R^2) is the squared value of the linear correlation Coefficient value (R) . The value value ranges from 0 to 1 and depicts the proportion of the variation in the dependent variable that can be accounted for by the independent variable.
For the scenario given above,
The Coefficient of determination (R^2) = 0.464^2 = 0.215296 = (0.215296 * 100%) = 21.5%
This means that 21.5% of the variation can be explained by the relationship between both variables while (100% - 21.5% = 78.5%) can be explained by other factors.