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sergejj [24]
3 years ago
11

You are told that in a billiards shot, the cue ball was shot at the eight ball, which was 8 inches away. As a result, the eight

ball rolled into a pocket, which was 6 inches away. Knowing that the angle made with the path of the cue ball and the resulting path of the eight ball is larger than 90°, it can be determined that the original distance from the cue ball to the pocket was greater than inches.
Mathematics
2 answers:
mixer [17]3 years ago
7 0
Just did it on edgueinty its 10 inches :)
Trava [24]3 years ago
5 0
The answer is ten inches 
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What happens to the function f(x)=x4 when it becomes f′(x)=x4+5?
elena-s [515]
I think it moves 5 units to the right Im not sure tho!!
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Determine the intercepts of the line.<br> y-intercept:<br> r-intercept:<br> help pls
cricket20 [7]
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3 years ago
I need help it’s hard
Reika [66]

9514 1404 393

Answer:

  f^-1(x) = (x +4)/3

Step-by-step explanation:

The inverse function is found by swapping x and y, then solving for y.

  y = 3x -4 . . . . . function y = f(x)

  x = 3y -4 . . . . . inverse relation

  x +4 = 3y . . . . add 4

  (x +4)/3 = y . . . divide by 3. This is the inverse function

  f^-1(x) = (x +4)/3

6 0
3 years ago
In a study relating the degree of warping, in mm, of a copper plate (y) to temperature in °C (x), the following summary statisti
kipiarov [429]

Answer:

The error sum of squares is SSE=12.97

The regression sum of squares is SSR=6.430

The total sum of squares is SST=19.4

Step-by-step explanation:

Linear regression is a way "to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables)".

Regression estimates are "used to describe data and to explain the relationship between one dependent variable and one or more independent variables"

The linear model is given by the following equation Y=mx +b, where y is the dependent variable, x the independent variable, m the slope and b the intercept.

For this case we have the following info given:

\sum_{i=1}^n (x_i -\bar x)^2 =98775

\sum_{i=1}^n (y_i -\bar y)^2 =19.4

\bar x =26.36

\bar y =0.5188

n= 40  represent the sample size

\sum_{i=1}^n (x_i -\bar x)(y_i -\bar y) =796.94

The total sum of squares is given by this formula:

SST= \sum_{i=1}^n (y_i -\bar y)^2 =19.4

And from the formulas for a simple regression, we need to calculate the slope for the regression like this:

m=\frac{\sum_{i=1}^n (x_i -\bar x)(y_i -\bar y)}{\sum_{i=1}^n (x_i -\bar x)^2}

And if we replace the values given we have:

m=\frac{796.94}{98775}=0.008068

We have another useful equation in order to find the sum of squares for the regression, given by:

SSR=m* \sum_{i=1}^n (x_i -\bar x)(y_i -\bar y)=0.008068*796.94=6.430

And we know this equivalence:

SST= SSR+SSE

And solving for the sum of squares for the error we have:

SSE=SST-SSR=19.4-6.430=12.97

So then we have the final solutions:

The error sum of squares is SSE=12.97

The regression sum of squares is SSR=6.430

The total sum of squares is SST=19.4

6 0
3 years ago
Solve y ≥ -2|x - 1| + 3
Nonamiya [84]

Answer:


Step-by-step explanation:


4 0
3 years ago
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