You begin with $90 in your savings account and your friend begins
2 answers:
Answer:
F(f) = 15t + 35 represents the total amount of savings your friend would make in t weeks.
F(d) = 10t + 90 represents the total amount of saving you, darian, would make in t weeks.
When you graph the equations, plugging in different values for t, you can see that the graphs intersect at (11,200). This means that at 11 weeks, both you and your friend have the same amount of money saved up, $200. They will not have the same amount of money in 10 weeks.
Answer:
<h3>a.Step-by-step explanation:
If you begin with $90 and add $10 each week, then we express it as
Where refers to weeks and
refers to the total amount of money in you savings account.
On the other hand, your friend begins with $35 and deposits $15 each week, the equation that expresses your friend's savings is
Therefore, the sytem of linear equation that models this situation is
The graph attached shows this system. There you can notice that the solution is (11, 200), which means in 11 weeks both will have the same amount saved.
Therefore, your friend is wrong, because after 10 weeks you won't have the same amount.
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Answer:
a)
b) Figure attached
c)
Step-by-step explanation:
We assume that th data is this one:
x: 30, 30, 30, 50, 50, 50, 70,70, 70,90,90,90
y: 38, 43, 29, 32, 26, 33, 19, 27, 23, 14, 19, 21.
a) Find the least-squares line appropriate for this data.
For this case we need to calculate the slope with the following formula:
Where:
So we can find the sums like this:
With these we can find the sums:
And the slope would be:
Nowe we can find the means for x and y like this:
And we can find the intercept using this:
So the line would be given by:
b) Plot the points and graph the line as a check on your calculations.
For this case we can use excel and we got the figure attached as the result.
c) Calculate S^2
In oder to calculate S^2 we need to calculate the MSE, or the mean square error. And is given by this formula:
The degred of freedom for the error are given by:
We can calculate:
And now we can calculate the sum of squares for the regression given by:
We have that SST= SSR+SSE, and then SSE=SST-SSR= 792-601.67=190.33[/tex]
So then :
