Algebra 2 - residual^2 or something ?

In summary, the conversation discusses the construction of equations for predicting the number of failed parts on an assembly line based on the number of days. The participants also discuss finding the residual and creating a table to show the predicted values and residual for each equation. Additionally, there is a correction made to one of the equations and the conversation ends with the participants feeling confident about their understanding of the topic.
  • #1
offtheleft
131
1
i have a function [tex]y=f(x)[/tex] with three points;
[tex]A (1, 40)[/tex]
[tex]B (3, 80)[/tex]
[tex]C (5, 90)[/tex]

the [tex]x[/tex] model represents days. and the [tex]y[/tex] model represents failed parts on an assembly line.

the question is: how many failed parts will have been identified [tex]1[/tex] week from now?

I have no problem with doing the work, getting the slopes, figuring out all the algebraic gymnastics and getting the equations.

heres what i have:
[tex]A\rightarrow B, y=20x-20[/tex]

[tex]A\rightarrow C, y=\frac{25}{2}x+\frac{55}{2}[/tex]

[tex]B\rightarrow C, y=\frac{77}{5}x+26[/tex]


I actually think my equation from [tex]B\rightarrow C [/tex] could be incorrect.

now, the part that is getting me is I am trying to get the residual? i need to get some medication because my ADHD is seriously out of control. its getting worse as i get older.

i need to construct some table... this is what i have for the first one:

real obverved measures residual
[tex]x,... y,... y_{1}=20x-20,... (y-y_{1})^2[/tex]
[tex]1,... 40, ...40, .....0[/tex]
[tex]3,... 80, ...80, .....0[/tex]
[tex]5,... 90, ...120, ...900[/tex]


real obverved measures residual
[tex]x,... y,... y_{2}=\frac{25}{2}x+\frac{55}{2},... (y-y_{1})^2[/tex]
[tex]1,[/tex]
[tex]2,[/tex]
[tex]3,[/tex]

not quite sure how to do all of this. :(
 
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  • #2
You might want to check your equation for A to C as well as from B to C...

I am assuming of course you want to write an equation that goes through both those points in y = mx +b form.

Other than that I can't help you because I don't really understand the question. I know you want to predict what y is when x is 7, but there are many methods of doing this and I am not sure which one you are supposed to be doing.
 
  • #3
Here are my Equations:[tex]A\rightarrow B, y=20x-20[/tex]

[tex]A\rightarrow C, y=\frac{25}{2}x+\frac{55}{2}[/tex]

[tex]B\rightarrow C, y=5x+65[/tex]

the first two are correct because we did them in class. the third i just corrected, there was a minor mistake in my arithmetic that knocked everything off the rocker.

now, i have to do something with the [tex]y[/tex] points [tex] A(1,40) B(3,80) C(5,90) [/tex]

what i have to do for this table

I have each point
 
Last edited:
  • #4
I think A -> B is incorrect!

y = 20x - 20

If this is supposed to go through the point A(1,40) then when I put 1 in for x I should get y = 40.y = 20(1) - 20 = 20 - 20 = 0

So y = 20x - 20 does not go through the point (1,40) it goes through (1, 0)
It's probably just a typo from you notes from your class, but the correct equation should be y = 20x + 20
 
  • #5
Diffy said:
I think A -> B is incorrect!

y = 20x - 20

If this is supposed to go through the point A(1,40) then when I put 1 in for x I should get y = 40.


y = 20(1) - 20 = 20 - 20 = 0

So y = 20x - 20 does not go through the point (1,40) it goes through (1, 0)



It's probably just a typo from you notes from your class, but the correct equation should be y = 20x + 20

YOURE RIGHT! i actually got the answer early in the class which was [tex]y=20x+20[/tex] i don't know why its copied incorrectly thereafter

"[tex]y = 20(1) - 20 = 20 - 20 = 0[/tex]"

how would i construct a table just as that for the other two equations... but, there was three parts to each. one for the Y value for each point.
 
  • #6
I'm sorry can you elaborate on the table you are supposed to create, I don't really understand that part.
 
  • #7
real obverved measures residual
[tex]x,... y,... y_{1}=20x+20,... (y-y_{1})^2[/tex]
[tex]1,... 40, ...40, .....0[/tex]
[tex]3,... 80, ...80, .....0[/tex]
[tex]5,... 90, ...120, ...900[/tex]

i think i finally understand it. because i had the equation copied wrong it all came out wrong, the numbers didnt match up and it threw me off.

[tex]
y_{1}=20(1)+20=40[/tex]

and the residual thing. i take the real [tex]y[/tex] which is [tex]40[/tex] and subtract it from the new [tex]y[/tex] which is [tex]40[/tex]: [tex]
y_{1}=20(1)+20=40[/tex]

[tex](40-40)^2=0[/tex]

let me try and set this up

my line: [tex]y=20(1)+20=40, (40-40)^2=0[/tex]

[tex]y=20(3)+20=80, (80-80)^2=0[/tex]

[tex]y=20(5)+20=120, (120-90)^2=900[/tex]
 
  • #8
[tex]
B\rightarrow C, y=5x+65[/tex]

[tex]y=5(1)+65=70, (70-40)^2=900[/tex]

[tex]y=5(3)+65=80, (80-80)^2=0[/tex]

[tex]y=5(5)+65=90, (90-90)^2=0[/tex]now, i have to figure out where i went wrong with the second line.. from A\rightarrow C.

i don't have my notebook with me at the moment though, ugh.
 
  • #9
just worked it out and the second equation is correct

[tex]
A\rightarrow C, y=\frac{25}{2}x+\frac{55}{2}[/tex][tex]y=\frac{25}{2}(1)+\frac{55}{2}=40, (40-40)^2=0[/tex]

[tex]y=\frac{25}{2}(3)+\frac{55}{2}=65, (80-65)^2=225[/tex]

[tex]y=\frac{25}{2}(5)+\frac{55}{2}=90, (90-90)^2=0[/tex]thanks for all the help! i think i may be able to hold it down from here, until class on tuesday :p
 

1. What is the purpose of residual^2 in Algebra 2?

Residual^2, or residual squared, is a term used in regression analysis to measure the difference between the actual data points and the predicted values from a regression line. It is used to evaluate the accuracy of the regression model and to identify any patterns or trends in the data that may not be captured by the model.

2. How is residual^2 calculated?

Residual^2 is calculated by taking the difference between the actual data points and the predicted values, squaring each difference, and then summing up all the squared differences. This value is then used to evaluate the goodness of fit of the regression model.

3. What do positive and negative values of residual^2 indicate?

A positive value of residual^2 indicates that the predicted values are higher than the actual data points, while a negative value indicates that the predicted values are lower than the actual data points. Both positive and negative values can show how well the regression line fits the data, but they do not necessarily indicate the overall quality of the model.

4. Can residual^2 be used for non-linear regression?

No, residual^2 is only used for linear regression models. For non-linear regression, a different measure of goodness of fit, such as the sum of squared errors, is used to evaluate the model.

5. How is residual^2 related to correlation coefficient?

The square root of residual^2 is equal to the correlation coefficient (r) for a linear regression model. This means that residual^2 can also be used to measure the strength and direction of the relationship between the independent and dependent variables in a regression model.

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