
HI6006 Competitive Strategy Editing Service
Delivery in day(s): 4
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 (1-p)
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 |
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SUMMARY OUTPUT |
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Regression Statistics |
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Multiple R | 0.842117773 |
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R Square | 0.709162344 |
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Adjusted R Square | 0.69594245 |
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Standard Error | 7.129671393 |
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Observations | 24 |
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ANOVA |
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| df | SS | MS | F | Significance F |
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Regression | 1 | 2726.821684 | 2726.821684 | 53.64357481 | 2.47416E-07 |
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Residual | 22 | 1118.308712 | 50.83221418 |
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Total | 23 | 3845.130396 |
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| Coefficients | Standard Error | t Stat | P-value | 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 | 2.47416E-07 | 0.095732988 | 0.171362151 | 0.095732988 | 0.171362151 |
Cost and Orders |
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SUMMARY OUTPUT |
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Regression Statistics |
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Multiple R | 0.91880399 |
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R Square | 0.844200772 |
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Adjusted R Square | 0.837118989 |
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Standard Error | 5.218273602 |
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Observations | 24 |
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ANOVA |
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| df | SS | MS | F | Significance F |
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Regression | 1 | 3246.062049 | 3246.062049 | 119.2073751 | 2.38511E-10 |
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Residual | 22 | 599.0683465 | 27.23037939 |
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Total | 23 | 3845.130396 |
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| Coefficients | Standard Error | t Stat | P-value | 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 | 2.38511E-10 | 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 |
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SUMMARY OUTPUT |
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Regression Statistics |
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Multiple R | 0.93591442 |
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R Square | 0.875935802 |
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Adjusted R Square | 0.864120164 |
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Standard Error | 4.766165573 |
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Observations | 24 |
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ANOVA |
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| df | SS | MS | F | Significance F |
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Regression | 2 | 3368.087376 | 1684.043688 | 74.1336022 | 3.0429E-10 |
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Residual | 21 | 477.0430196 | 22.71633427 |
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Total | 23 | 3845.130396 |
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| Coefficients | Standard Error | t Stat | P-value | 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 | 2.87239E-05 | 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 |
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P(F<=f) one-tail | 0.274097371 |
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F Critical one-tail | 0.398841227 |
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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 t-Test: 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 |
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Hypothesized Mean Difference | 0 |
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df | 14 |
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t Stat | -4.542789694 |
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P(T<=t) one-tail | 0.000229993 |
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t Critical one-tail | 1.761310136 |
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P(T<=t) two-tail | 0.000459985 |
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t Critical two-tail | 2.144786688 |
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Based on the above table it is identified that the p-value 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