Cost Estimation – Sultan LTD
The report topic is on India tea Industry
First i will need a Industry Environmental Analysis “porter’s five forces” based on India tea industry.
Note# the porter’s five forces need to be connected to India tea industry with details and if possible with data or news to prove it. #News and data has to be recent#
Second i need based on Hindustan Unilever limited one of the main player in India tea industry.
The Hindustan Unilever’s Resources and Capabilities in term of Tangible Resources, Intangible Resources and Human resources.
Third i also need a VIRO Framework for Hindustan Unilever.
And last i need Hindustan Unilever core Competencies
At the end of the paper please attached all the linked that is used in the report as i will need it for future explanation to my lecture. Thank you.
| Correlations | ||||||
| Estimatedcost | Workdays | Contractvalue | Noofbidders | Ratioofsuccess | ||
| Estimatedcost | Pearson Correlation | 1 | .626** | .949** | .176 | -.248 |
| Sig. (2-tailed) | .000 | .000 | .311 | .157 | ||
| N | 35 | 35 | 35 | 35 | 34 | |
| Workdays | Pearson Correlation | .626** | 1 | .628** | -.076 | -.197 |
| Sig. (2-tailed) | .000 | .000 | .666 | .264 | ||
| N | 35 | 35 | 35 | 35 | 34 | |
| Contractvalue | Pearson Correlation | .949** | .628** | 1 | .222 | -.284 |
| Sig. (2-tailed) | .000 | .000 | .199 | .103 | ||
| N | 35 | 35 | 35 | 35 | 34 | |
| Noofbidders | Pearson Correlation | .176 | -.076 | .222 | 1 | -.494** |
| Sig. (2-tailed) | .311 | .666 | .199 | .003 | ||
| N | 35 | 35 | 35 | 35 | 34 | |
| Ratioofsuccess | Pearson Correlation | -.248 | -.197 | -.284 | -.494** | 1 |
| Sig. (2-tailed) | .157 | .264 | .103 | .003 | ||
| N | 34 | 34 | 34 | 34 | 34 | |
| **. Correlation is significant at the 0.01 level (2-tailed). |
The correlation matrix implies there is a strong positive correlation between contract value and estimated cost. It also reveals there is a moderate positive correlation between the workdays and the estimated cost. Moreover, there is a weak positive relationship between the estimated cost and the number of bidders. It also reveals that there is a weak negative relationship between the estimated cost and the ratio of success.
Multiple Regression
| Model Summary | ||||||||||||||||||
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | ||||||||||||||
| 1 | .945a | .894 | .879 | 23812.406 | ||||||||||||||
| a. Predictors: (Constant), Ratio of success, Workdays, No of bidders, Contract value 000 | ||||||||||||||||||
| ANOVA | ||||||||||||||||||
| Model | Sum of Squares | df | Mean Square | F | Sig. | |||||||||||||
| 1 | Regression | 138,042,139,705.07 | 4 | 34,510,534,926.27 | 60.862 | .000b | ||||||||||||
| Residual | 16,443,889,706.69 | 29 | 567,030,679.54 | |||||||||||||||
| Total | 154486029411.77 | 33 | ||||||||||||||||
| a. Dependent Variable: Estimatedcost000 | ||||||||||||||||||
| b. Predictors: (Constant), Ratio of success, Workdays, No of bidders, Contract value000 | ||||||||||||||||||
| Coefficients | ||||||||||||||||||
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||||||||||||||
| B | Std. Error | Beta | ||||||||||||||||
| 1 | (Constant) | -12993.433 | 29440.713 | -.441 | .662 | |||||||||||||
| Workdays | 28.250 | 68.390 | .033 | .413 | .683 | |||||||||||||
| Contractvalue000 | .799 | .069 | .934 | 11.647 | .000 | |||||||||||||
| Noofbidders | -457.877 | 1525.094 | -.022 | -.300 | .766 | |||||||||||||
| Ratioofsuccess | 52.916 | 311.644 | .012 | .170 | .866 | |||||||||||||
| a. Dependent Variable: Estimated cost 000 | ||||||||||||||||||
The above analyses present a multiple linear regression of Workdays, contract value, Number of bidders, and the ratio of success against the estimated cost. The Anova table has a p value of 0.000, which is lower than 0.05, which implies that the overall model is a fit. The R squared value is 0.945 implying that 95 percent of the variation in the estimated cost is explained by the variation in Workdays, contract value, No of bidders, and the ratio of success. Ultimately, the only significant predictor for the estimated cost is the contract value because it has a p value of 0.000, which is lower than 0.05.
Simple Linear Regression
| Variables Entered/Removeda | ||||||
| Model | Variables Entered | Variables Removed | Method | |||
| 1 | Contractvalue000b | Enter | ||||
| a. Dependent Variable: Estimated cost 000 | ||||||
| b. All requested variables entered. | ||||||
| Model Summary | ||||||
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | ||
| 1 | .949a | .900 | .897 | 22715.953 | ||
| a. Predictors: (Constant), Contract value 000 | ||||||
| ANOVAa | ||||||
| Model | Sum of Squares | df | Mean Square | F | Sig. | |
| 1 | Regression | 153468663180.366 | 1 | 153468663180.366 | 297.412 | .000b |
| Residual | 17028479676.777 | 33 | 516014535.660 | |||
| Total | 170497142857.143 | 34 | ||||
| a. Dependent Variable: Estimatedcost000 | ||||||
| b. Predictors: (Constant), Contractvalue000 |
| Coefficientsa | ||||||
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||
| B | Std. Error | Beta | ||||
| 1 | (Constant) | -14415.344 | 11924.962 | -1.209 | .235 | |
| Contractvalue000 | .819 | .047 | .949 | 17.246 | .000 | |
| a. Dependent Variable: Estimatedcost000 |
The R squared value for a simple linear regression of the estimated cost against contract value was 0.949 implying that implying that 95 percent of the variation in the estimated cost is explained by the variation in the contract value. The Anova table has a p value of 0.000, which is lower than 0.05, which implies that the overall model is a fit. Ultimately the y intercept is not a significant predictor because it has a p-value of 0.235 which is greater than 0.05. The contract value is significant because it has a p value of 0.000, which is less than 0.05.
The best analysis for the manager is a simple linear regression because only one of the variables is a significant predictor of the estimated costs.
