ENS6152 Steel Design Assignment Help
Delivery in day(s): 3
Cost 
Sales 
Orders 
52.95 
386 
4015 
71.66 
446 
3806 
85.58 
512 
5309 
63.69 
401 
4262 
72.81 
457 
4296 
68.44 
458 
4097 
52.46 
301 
3213 
70.77 
484 
4809 
82.03 
517 
5237 
74.39 
503 
4732 
70.84 
535 
4413 
54.08 
353 
2921 
62.98 
372 
3977 
72.3 
328 
4428 
58.99 
408 
3964 
79.38 
491 
4582 
94.44 
527 
5582 
59.74 
444 
3450 
90.5 
623 
5079 
93.24 
596 
5735 
69.33 
463 
4269 
53.71 
389 
3708 
89.18 
547 
5387 
66.8 
415 
4161 
Cost 
Sales 
Orders 

Mean 
71.26 
456.50 
4,393.00 
Standard Deviation 
12.93 
81.53 
737.08 
Standard deviation of all orders above 4,000
Sample proportion
Orders above 4000 
4015 
5309 
4262 
4296 
4097 
4809 
5237 
4732 
4413 
4428 
4582 
5582 
5079 
5735 
4269 
5387 
4161 
Mean 
4,729.00 
Standard deviation 
556.61 
Question c
Confidence intervals
95% for distribution cost
Since the total value is less than 30, t distribution is used to measure the confidence interval
i) 
Cost 
Mean 
71.26 
Standard Deviation 
12.93 
Sqrt n 
4.90 
t 
2.07 
CI 

Upper limit 
76.72 
Lower limit 
65.80 
99% confidence interva on sales
ii) 
Sales 
Mean 
456.50 
Standard Deviation 
81.53 
Sqrt n 
4.90 
t 
2.81 
CI 

Upper limit 
503.22 
Lower limit 
409.78 
90% confidence interval on orders exceed 4000
iii) 
Orders 
Mean 
4,729.00 
Standard Deviation 
556.61 
Sqrt n 
4.90 
t 
1.75 
CI 

Upper limit 
4,927.36 
Lower limit 
4,530.64 
Point estimate
Mean 
4,729.00 
Standard Deviation 
556.61 
Normal dist 
4,015.67 
N = (z/M)^2 x p (1p)
Z 
1.96 
population 
0.1 
Margin of error 
222.69 
Sample Size 
7.02 
Null hypothesis: The mean is equal to 65
Alternate hypothesis: The mean is not equal to 65
x 
71.26 
Standard Deviation 
12.93 
Mu 
65 
n 
24 
t dist 
2.37 
t table value 
2.07 
Since t distribution is greater than t table value the null hypothesis is rejected and alternate hypithesis is accepted
Null hypothesis: 30% orders received are less than 4000
Alternate hypothesis: 30% orders received are not less than 4000
Mean 
4,393.00 
SD 
737.08 
Mu 
4000 
n 
24 
t dist 
2.61 
p value 
0.007828 
The result is significant at p < .05.
Cost 
52.95 
71.66 
85.58 
63.69 
72.81 
68.44 
52.46 
70.77 
82.03 
74.39 
70.84 
54.08 
Null hypothesis: The mean is equal to 65
Alternate hypothesis: The mean is not equal to 65
Mean 
68.31 
SD 
10.78 
Mu 
65 
n 
12 
t dist 
1.06 
t table value 
3.11 
Since t distribution is less than t table value the null hypothesis is accepted
Part C
Cost and Sales 








SUMMARY OUTPUT 





Regression Statistics 


Multiple R 
0.842117773 


R Square 
0.709162344 


Adjusted R Square 
0.69594245 


Standard Error 
7.129671393 


Observations 
24 





ANOVA 



df 
SS 
MS 
F 
Significance F 


Regression 
1 
2726.821684 
2726.821684 
53.64357481 
2.47416E07 


Residual 
22 
1118.308712 
50.83221418 


Total 
23 
3845.130396 









Coefficients 
Standard Error 
t Stat 
Pvalue 
Lower 95% 
Upper 95% 
Lower 95.0% 
Upper 95.0% 
Intercept 
10.29761784 
8.449998094 
1.218653274 
0.235882952 
7.226605545 
27.82184123 
7.226605545 
27.82184123 
Sales 
0.13354757 
0.018233798 
7.324177415 
2.47416E07 
0.095732988 
0.171362151 
0.095732988 
0.171362151 
Cost and Orders 








SUMMARY OUTPUT 





Regression Statistics 


Multiple R 
0.91880399 


R Square 
0.844200772 


Adjusted R Square 
0.837118989 


Standard Error 
5.218273602 


Observations 
24 





ANOVA 



df 
SS 
MS 
F 
Significance F 


Regression 
1 
3246.062049 
3246.062049 
119.2073751 
2.38511E10 


Residual 
22 
599.0683465 
27.23037939 


Total 
23 
3845.130396 









Coefficients 
Standard Error 
t Stat 
Pvalue 
Lower 95% 
Upper 95% 
Lower 95.0% 
Upper 95.0% 
Intercept 
0.457625305 
6.571882688 
0.069633821 
0.945114194 
13.17162514 
14.08687576 
13.17162514 
14.08687576 
Orders 
0.016117564 
0.001476209 
10.918213 
2.38511E10 
0.013056094 
0.019179034 
0.013056094 
0.019179034 
Based on the above outputs it is identified that the value of R square betwee cost and sales is 0.7091 or 70.91% whereas the value is 0.8442 or 84.42% for cost and orders. The value of R square intends to specify the goodness of the fit of the model, the maximum value of R square is 1 or 100%, so higher the value of R square better is the goodness of fit to the model. So it can be stated that cost and orders shows a better association in the model
Multiple regression
Cost  Sales and Orders 








SUMMARY OUTPUT 

















Regression Statistics 








Multiple R 
0.93591442 







R Square 
0.875935802 







Adjusted R Square 
0.864120164 







Standard Error 
4.766165573 







Observations 
24 
















ANOVA 









df 
SS 
MS 
F 
Significance F 



Regression 
2 
3368.087376 
1684.043688 
74.1336022 
3.0429E10 



Residual 
21 
477.0430196 
22.71633427 





Total 
23 
3845.130396 
















Coefficients 
Standard Error 
t Stat 
Pvalue 
Lower 95% 
Upper 95% 
Lower 95.0% 
Upper 95.0% 
Intercept 
2.728246583 
6.157879754 
0.443049668 
0.662260247 
15.53425853 
10.07776536 
15.53425853 
10.07776536 
Sales 
0.047113872 
0.02032792 
2.317692762 
0.030643769 
0.004839649 
0.089388095 
0.004839649 
0.089388095 
Orders 
0.011946926 
0.002248569 
5.313123092 
2.87239E05 
0.00727077 
0.016623082 
0.00727077 
0.016623082 
By using multiple regression where in the sales and orders are considered as independent variables ad the dependent variable is cost. The model states that the R square is 0.8759 or 87.59% which is a better fit when compared with individual models which was specified in question a. Also the significance value is 0.00 which shows that there is a significant relationship between the independent variables and the dependent variable.
Based on the regression coefficients the regression equation can be stated as
Y (Cost) = Constant + X1 (Sales) + X2 (Orders)
Y (Cost) = 2.73 + 0.047 (Sales) + 0.0119 (Orders)
Based on the above it is noted that orders possess a significant influence on the cost when compared with sales
F test
Null hypothesis: There is no significant variance of the waiting time between the two branches
Alternate hypothesis: There is a significant variance of the waiting time between the two branches

CBD 
Suburban 
Mean 
4.286666667 
6.873571429 
Variance 
2.682995238 
3.73004011 
Observations 
15 
14 
df 
14 
13 
F 
0.719293938 

P(F<=f) onetail 
0.274097371 

F Critical onetail 
0.398841227 

Based on the above table it is identified that the p value is 0.274 which is more than the sig value of 0.05 (5%) so the null hypothesis is accepted. Hence it is stated that There is no significant variance of the waiting time between the two branches
Based on the overall analysis it is identified that the tTest: Paired Two Sample for Means can be used
Question c
Null hypothesis: There is no difference in the mean waiting time between the two branches
Alternatehypothesis: There is a difference in the mean waiting time between the two branches

CBD 
Suburban 
Mean 
4.286666667 
7.114666667 
Variance 
2.682995238 
4.335512381 
Observations 
15 
15 
Pearson Correlation 
0.176721009 

Hypothesized Mean Difference 
0 

df 
14 

t Stat 
4.542789694 

P(T<=t) onetail 
0.000229993 

t Critical onetail 
1.761310136 

P(T<=t) twotail 
0.000459985 

t Critical twotail 
2.144786688 

Based on the above table it is identified that the pvalue is 0.00 which is less than the sig value of 0.05 (5%) so the null hypothesis is rejected and alternate hypothesis is accepted. Hence, There is a difference in the mean waiting time between the two branches.
2. Freedman, David (2010). Statistics. 4th Edition. Cengage Publishing
3. Sincich, T. Terry (2012). Statistics. 12th Edition.
4. Triola (2014). Essentials of Statistics. 5th Edition. McGraw Hill
5. Witte, S. Robert. (2010). Statistics. 5th Edition. McGraw Hill