The answer is √3-√2+<span>√6 = in decimal form 2.76732698</span><span /><span>
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Answer:
1. The sum of the residuals is always close to zero.
2.The coefficient of determination measures how much of the variation in the y-values is explained by the regression line.
3. In the equation of the least-squares regression line, y hat is a predicted value when x is known
Step-by-step explanation:
The least square regression line is a line that makes distance from data points to the regression line to be as minimal as possible. This line is a best fit for the data points. Let's say we have a collection of numbers and a scatter plot, this line is a line that exists and best fits the data.
Therefore, the least square regression line minimizes sum of squared error to be close to zero. R² measures the extent to which variations in the value of y is explained by the regression line. In the equation of this line, y hat is a predicted value when x is known.
Answer:
(x-5)*7 = 7x - 3
Step-by-step explanation:
If x is the number, then 5 less is x-5
Answer:
the answer is 4/1 (rise over run)
Step-by-step explanation:
Answer:
1. <u>$14.88</u>
2. <u>$12.40</u>
Step-by-step explanation:
An english translation:
<em>A company transports office cabinets to a location 425km away. Its cost is R $ 2.10 per km traveled. When the cabinets are assembled, the vehicle's capacity is 60 units. When they are disassembled the capacity increases 6 times. We ask: 1- What is the cost per assembled cabinet? 2- What is the savings per cabinet, when these are taken apart.</em>
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<u>Solution:</u>
425 km with 2.10 per km means:
425 * 2.10 = $892.50 total cost
Now, when assembled, there goes 60 cabinets, so cost per assembled cabinet is:
<u>Cost per assembled cabinet = 892.5/60 = $14.875 = $14.88</u>
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The capacity of 60 after disembling, it becomes:
60 * 6 = 360
So, cost per cabinet becomes:
892.5/360 = <u>$2.48</u>
The savings is how much you save up if they were assembled:
14.88 - 2.48 = <u>$12.40</u>
<u>Savings = $12.40</u>