part 1 Infographic Assignment How do you interpret the effect of significant c

part 1
Infographic Assignment
How do you interpret the effect of significant coefficients?
How are the distribution of the observed residuals of the constructed model tested for normality?
How can the regression model be used for prediction?
Visually communicate your responses to the questions above by developing an infographic.
part 2
Multiple Regression Activity
Although natural gas is currently inexpensive and nuclear power currently (and perhaps deservedly) does not have a good reputation, it is possible that more nuclear power plants will be constructed in the future. Table 4 presents data concerning the construction costs of light water reactor (LWR) nuclear power plants. The dependent variable, C, construction cost, is expressed in millions of dollars, adjusted to a 1976 base. Preliminary analysis of the data and economic theory indicate that variation in cost increases as cost increases. This suggests transforming cost by taking its natural logarithm.
S Power plant capacity in MWe
N Cumulative number of power plants built by the contractor
Build a multiple regression model to predict ln(C) by taking S and N or their natural logarithms as the independent variables. Make sure to check for multicollinearity.
Use residual analysis and R2 to check your model.
State which variables are important in predicting the cost of constructing an LWR plant, and
State a prediction equation that can be used to predict ln(C).
Table 4
Data Concerning Construction of Light Water Reactors
Plant
C
S
N
1
460.05
687
14
2
452.99
1,065
1
3
443.22
1,065
1
4
652.32
1,065
12
5
642.23
1,065
12
6
345.39
514
3
7
272.37
822
5
8
317.21
457
1
9
457.12
822
5
10
690.19
792
2
11
350.63
560
3
12
402.59
790
6
13
412.18
530
2
14
495.58
1,050
7
15
394.36
850
16
16
423.32
778
3
17
712.27
845
17
18
289.66
530
2
19
881.24
1,090
1
20
490.88
1,050
8
21
567.79
913
15
22
665.99
828
20
23
621.45
786
18
24
608.8
821
3
25
473.64
538
19
26
697.14
1,130
21
27
207.51
745
8
28
288.48
821
7
29
284.88
886
11
30
280.36
886
11
31
217.38
745
8
32
270.71
886
11