Find the compound interest on 50000 for 9 month at 8 %
1 answer:
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Check the procedures in the picture attached.
1. Since we are using direct substitution, the only thing we need to do is replace
by 
(the quantity is approaching to) in our quantity to get:
We can conclude that the correct answer is B) -6
2. Again, since we are using direct substitution, th only thing we need to do is replace by 
(the quantity x is approaching to) to get:
We can conclude that the correct answer is D) 31
3. Direct substitution again, so we are going to replace in our quantity by 
to get:
And since cosine of is 0, and any quantity multiplied by zero is zero:
We can conclude that the correct answer is A) 0
4. Here we need to so some algebra first. Notice that if we use direct substitution, the denominator will be zero, and we cannot divide by zero. So lets massage our quantity first:
Now, we can use direct substitution to get:
We can conclude that the correct answer is B) 4
1. Since we are using direct substitution, the only thing we need to do is replace
We can conclude that the correct answer is B) -6
2. Again, since we are using direct substitution, th only thing we need to do is replace
We can conclude that the correct answer is D) 31
3. Direct substitution again, so we are going to replace
And since cosine of
We can conclude that the correct answer is A) 0
4. Here we need to so some algebra first. Notice that if we use direct substitution, the denominator will be zero, and we cannot divide by zero. So lets massage our quantity first:
Now, we can use direct substitution to get:
We can conclude that the correct answer is B) 4
Answer:
(a) See below
(b) r = 0.9879
(c) y = -12.629 + 0.0654x
(d) See below
(e) No.
Step-by-step explanation:
(a) Plot the data
I used Excel to plot your data and got the graph in Fig 1 below.
(b) Correlation coefficient
One formula for the correlation coefficient is
The calculation is not difficult, but it is tedious.
(i) Calculate the intermediate numbers
We can display them in a table.
<u> x </u> <u> y </u> <u> xy </u> <u> x² </u> <u> y² </u>
36 0.22 7.92 1296 0.05
67 0.62 42.21 4489 0.40
93 1.00 93.00 20164 3.46
433 11.8 5699.4 233289 139.24
887 29.3 25989.1 786769 858.49
1785 82.0 146370 3186225 6724
2797 163.0 455911 7823209 26569
<u>3675 </u> <u> 248.0 </u> <u> 911400 </u> <u>13505625</u> <u> 61504 </u>
9965 537.81 1545776.75 25569715 95799.63
(ii) Calculate the correlation coefficient
(c) Regression line
The equation for the regression line is
y = a + bx where
(d) Residuals
Insert the values of x into the regression equation to get the estimated values of y.
Then take the difference between the actual and estimated values to get the residuals.
<u> x </u> <u> y </u> <u>Estimated</u> <u>Residual </u>
36 0.22 -10 10
67 0.62 -8 9
93 1.00 -7 8
142 1.86 -3 5
433 11.8 19 - 7
887 29.3 45 -16
1785 82.0 104 -22
2797 163.0 170 - 7
3675 248.0 228 20
(e) Suitability of regression line
A linear model would have the residuals scattered randomly above and below a horizontal line.
Instead, they appear to lie along a parabola (Fig. 2).
This suggests that linear regression is not a good model for the data.
