Solve for x where x is a real number. squareroot of (7x+36) - squareroot of (x+21) = 3

Baladdaa9
2022-08-01
Answered

Solve for x where x is a real number. squareroot of (7x+36) - squareroot of (x+21) = 3

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suchonos6r

Answered 2022-08-02
Author has **14** answers

squareroot of (7x+36) - squareroot of (x+21) = 3

squaring on both sides

7x+36+x+21 -2squareroot of (7x+36)*squareroot of (x+21) = 9

8x+48=2squareroot of (7x+36)*squareroot of (x+21)

4x+24=squareroot of (7x+36)*squareroot of (x+21)

squaring on both sides

(4x+24)^2=(7x+36)*(x+21)

=>16x^2+576+192x=7x^2+183x+756

=>9x^2 +9x-180=0

=>x^2+x-20=0

==>x=4,-5

x cannot be -5

so x is 4

squaring on both sides

7x+36+x+21 -2squareroot of (7x+36)*squareroot of (x+21) = 9

8x+48=2squareroot of (7x+36)*squareroot of (x+21)

4x+24=squareroot of (7x+36)*squareroot of (x+21)

squaring on both sides

(4x+24)^2=(7x+36)*(x+21)

=>16x^2+576+192x=7x^2+183x+756

=>9x^2 +9x-180=0

=>x^2+x-20=0

==>x=4,-5

x cannot be -5

so x is 4

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I want to implement a Kalman Filter for predicting x/y positions. I have a sensor which gives me the current position (noisy). Now I want to smooth and predict the position. Thus Kalman Filter came to my mind.

How do I have to design the filter taking the time in regards? I want to predict the next state which is ahead of the upcoming measurement. Moreover since i do not have a velocity, I'd like to estimate the velocity by the kalman as well.

State := [xpos,ypos,xvelocity,yvelocity]

Measurement := [xpos,ypos]

ControlInput := predictionTime

I would run the following algorithm, whenever i get a measurement:

1. Measure

2. Update kalman gain

3. Predict

4. Get State estimate

Now, when i use a predictionTime which is newer than the next measurement, the next measurement does not fit to the predicted state.

Is there a strategy to solve this issue? My predicted state and my next measurement do not fit, how could I fix this?

How do I have to design the filter taking the time in regards? I want to predict the next state which is ahead of the upcoming measurement. Moreover since i do not have a velocity, I'd like to estimate the velocity by the kalman as well.

State := [xpos,ypos,xvelocity,yvelocity]

Measurement := [xpos,ypos]

ControlInput := predictionTime

I would run the following algorithm, whenever i get a measurement:

1. Measure

2. Update kalman gain

3. Predict

4. Get State estimate

Now, when i use a predictionTime which is newer than the next measurement, the next measurement does not fit to the predicted state.

Is there a strategy to solve this issue? My predicted state and my next measurement do not fit, how could I fix this?

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A. nominal

B. ordinal

C. interval

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The lowest level of measurement for which the mean can be computed is the

A. nominal level of measurement.

B. ordinal level of measurement.

C. interval level of measurement.

D. ratio level of measurement.

A. nominal

B. ordinal

C. interval

D. ratio

The lowest level of measurement for which the mean can be computed is the

A. nominal level of measurement.

B. ordinal level of measurement.

C. interval level of measurement.

D. ratio level of measurement.

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