Answer: D) The linear model shows a strong fit to the data
The actual strength of the relationship is unknown unless we have the actual values of each data point (so we can compute the correlation coefficient r), but the residuals are randomly scattered about both above and below the horizontal axis. This means we have a fairly good linear fit. If all of the points were above the line, or all below the line, or all residuals fit a certain pattern (eg: parabola), then it wouldn't be a good linear fit.
Answer:
the answer would be option C. I just took the quiz
Step-by-step explanation:
i took the test!!!!!
Answer: Slope = -2.667/2.000 = -1.333
x-intercept = 5/4 = 1.25000
y-intercept = 5/3 = 1.66667
Step-by-step explanation: Slope is defined as the change in y divided by the change in x. We note that for x=0, the value of y is 1.667 and for x=2.000, the value of y is -1.000. So, for a change of 2.000 in x (The change in x is sometimes referred to as "RUN") we get a change of -1.000 - 1.667 = -2.667 in y. (The change in y is sometimes referred to as "RISE" and the Slope is m = RISE / RUN)
Bag weight 1.8 kg
Golf ball weight: 45g
1 kg = 1000g
Total bag weight in grams = 1.8 x 1000 = 1800g
total bag weight / golf ball weight = total balls in bag
1800 / 45 = 40
Total balls in bag = 40.
Answer:
Step-by-step explanation:
slope intercept form is y = mx + b
17 = y + 4x....we have to get y on one side and everything else on the other side.....so the easiest way would be to subtract 4x from both sides
17 - 4x = y + 4x - 4x....combine like terms
17 - 4x = y...rearrange
y = -4x + 17 <===== slope intercept form
