How Do I Solve a Quadratic Regression for a Helicopter Jump Trajectory?

  • Thread starter curiousguy
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In summary, the problem is trying to find the quadratic equation to solve for when the person should jump out of the helicopter and hit the target. The problem is that the quadratic equation won't work because the initial vertical velocity is zero.
  • #1
curiousguy
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I have been trying to figure this out for 2 weeks.
A helicopter is begins its horizontal motion 2500 ft from the center of a targe 30 ft above the target traveling at 40mph. The target ( a pile of boxes) is stacked 15 ft high off the ground. At the appropriate time, a person is to jump out of the helicopter and hit the target. For the purpose of this problem, my professor says do not consider the horizontal speed of the jumper. I am to find the quad formula and parabola that states 1) when the person should jump to hit the target, 2) the speed of the jumper upon impact with the target. HELP! I am soooo stuck. I cannot get the quad formula for this! I did convert mph to ft per sec. Is it rational to use 16 ftpersec^2 as "g", acceleration due to gravity? When I use it, and graph the parabola it doesn't come close to hitting the target. When I try to graph the parabola from a couple of coordinates I know, it won't do it. HELP!
 
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  • #2
Why can't everyone just use the metric system?
Your first problem is that g is not -16ft/s^2 but more like
-32.19ft/s^2... try that
 
  • #3
The professor has us using the falling body formula=- 1/2 32ft/s^2 + Init.Velocity + Init. Height(y intercept) That's why the -16 is there. I have narrowed down the problem I am having... its the initial velocity. Since I have been instructed to not take into account the horizontal movement of 40mph (of 58.57 ft/sec), then the parabola I come out with makes NO sense. It traces the maximum of the parabola to approx 90 ft, meaning when the guy jumps out of the helicopter, he first climbs up to 90 ft in the air, before gravity takes over and pulls him back down. Well, I know that's not right. I have spending the past 2 wks throwing stuff and jumping off of stuff just to figure out this problem! Oh, if only my grade weren't so important to me. But it is. Do you know what I am supposed to do about initial velocity? If I try to put in "0"x, it comes out to no solution. Arg. Thank you for the suggestion.. keep them coming.
 
  • #4
The initial vertical velocity is zero since the helicopter is moving only in the horizontal direction.

cookiemonster
 

Related to How Do I Solve a Quadratic Regression for a Helicopter Jump Trajectory?

1. What is a quadratic regression problem?

A quadratic regression problem is a mathematical method used to find the relationship between two variables, where one variable is a function of the other variable squared. It is often used to model real-world situations where there is a curved relationship between the variables.

2. How is a quadratic regression problem solved?

A quadratic regression problem is solved by using a process called least squares regression. This involves finding the line of best fit that minimizes the sum of the squared differences between the observed data points and the predicted values from the regression equation.

3. What type of data is suitable for a quadratic regression problem?

Quadratic regression is suitable for data that shows a curved relationship between two variables. This could include data from experiments, surveys, or other real-world situations where the variables are not linearly related.

4. What are the limitations of using quadratic regression?

One limitation of using quadratic regression is that it can only model relationships that are quadratic in nature. If the true relationship between the variables is not quadratic, the results from the regression analysis may be misleading. Additionally, quadratic regression assumes that the relationship between the variables is the same across the entire range of the data, which may not always be the case.

5. How is the accuracy of a quadratic regression model evaluated?

The accuracy of a quadratic regression model can be evaluated by calculating the coefficient of determination, also known as R-squared. This measures the proportion of the variation in the dependent variable that can be explained by the independent variable(s) in the model. A higher R-squared value indicates a better fit for the quadratic regression model.

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