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Plot the following data.
Fit a straight-line linear regression (trendline) to the following data. Set the intercept at 0 and display the equation.
Using the regression result, what concentration should be present at time 14.7?
Time
1
3
4
5
6
7
13
14
16
18
20
21
22
23
Conc (mg/L)
9.3
40.3
52.3
46.9
84.9
96.2
153.5
175.3
183.6
162.5
177.9
203.1
211.7
228.6
Time 14.7
Regression equations can be used to smooth noisy data or identify an underlying trend.
Find the best fit non-polynomial regression equation for the following data (highest R2) and use it to calculate smoothed data
Time
1
1.2
1.4
1.6
1.8
2
2.2
2.4
2.6
2.8
3
3.2
3.4
3.6
3.8
4
4.2
4.4
4.6
4.8
5
5.2
5.4
Smoothed
Conc (mg/L) Conc (mg/L)
0.519
0.31
1.509
1.561
1.996
1.234
2.368
2.627
2.331
2.674
2.027
3.146
3.122
2.478
3.413
2.52
2.798
2.974
3.931
3.95
3.249
3.152
4.046
to calculate smoothed data values at each time.
Find the best-fit (and most reasonable) regression through the data below.
Calculated the sum of residual squares (regression error).
Time (m) Velocity (m/s)
0.1
0.01
1
1.93
2
3.26
3
3.82
4
4.27
7
5.03
10
5.72
15
6.14
20
6.99
25
7.33
30
7.02
The table below shows the value of office buildings compared with several criteria.
Use the data below to perform a multivariate analysis.
On average, how much would you expect the value of the building to increase with 1 additionl office?
Using a t-test, are any of the parameter coefficients not significant at the 95% confidence level?
Using an F-test, is the predictive analysis significant at the 95% confidence level?
How much would you expect an office building to be worth that is 27 years old with 2,410 sq ft of floor space, 2 offices and 2
Floor space
Offices
Entrances
Age
$ Value
2,310
2
2
20
141,500
2,333
2
2
12
145,000
2,356
3
1
33
151,000
2,379
3
2
43
150,000
2,402
2
3
53
139,000
2,425
4
2
23
169,000
2,448
2
1
89
126,000
2,471
2
4
34
142,900
2,494
3
3
23
163,000
2,517
4
4
55
169,000
2,540
2
3
22
149,000
Fit a curve exactly through the following points using linear algebra and simultaneous equations.
Solve for the coefficients for the x-terms and the intercept.
x
1
2
3
4
5
y
2.35
3.66
5.11
4.18
7.81
Use Lagrange interpolating polynomials to interpolate the value of 16.4.
for the following four points--use all the points .
Interpolated Value
x
y
x
y
16.4
11
3.52
15
2.65
18
2.56
25
3.78
...

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Pelase find answer.Thank you.

Let the equation be

Y=ax4 +bx3 +cx2 +dx+e

x

1

2

3

4

5

y

2.35

3.66

5.11

4.18

7.81

Now, x=1, y=2.35=>2.35=a+b+c+d+e ----------------1

When, x=2, y=3.66, => 3.66=16a+8b+4c+2d+e -------2

When, x=3, y=5.11, => 5.11=81a+27b+9c+3d+e -------3

When, x=4, y=4.18, => 4.18=256a+64b+16c+4d+e -------4

When, x=5, y=7.81, => 7.81=625a+125b+25c+5d+e -------5

Solve the above five equation, we have

a= 0.3942,b=-4.3167,c=16.386,d=-23.228,e=13.16

Thus the equation is

Y= 0.3942x4 -4.3167x3 +16.386x2 -23.288x+13.16

Plot the following data.

Fit a straight-line linear regression (trendline) to the following data. Set the intercept at 0 and display the equation.

Using the regression result, what concentration should be present at time 14.7?

Time

1

3

4

5

6

7

13

14

16

18

20

21

22

23

Conc (mg/L)

9.3

40.3

52.3

46.9

84.9

96.2

153.5

175.3

183.6

162.5

177.9

203.1

211.7

228.6

Time 14.7

Concentration

149.29

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.992211

R Square

0.984484

Adjusted R Square

0.907561

Standard Error

19.15744

Observations

14

ANOVA

df

Regression

Residual

Total

Intercept

Time

1

13

14

SS

MS

302717.2515 302717.3

4771.098453 367.0076

307488.35

Coefficients

0

10.15581

Sta...

Review

Anonymous

Tutor went the extra mile to help me with this essay. Citations were a bit shaky but I appreciated how well he handled APA styles and how ok he was to change them even though I didnt specify. Got a B+ which is believable and acceptable.

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