Statistical Significance of the Parameters - Statistics Project Example

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Statistical Significance of the Parameters

Number of building wings has a positive influence upon manhours such that around 5.6 building wings can cause additional one manhour. Operational berthing capacity has a negative impact upon manhours needed such that 14.5 units of operational berthing capacity reduce the 1 manhour required. Number of rooms, are in a direct relationship with manhours such that around 29 rooms create a need of extra 1 manhour.
Since the probability (F-test) is less than the benchmark cutoff point of 0.05 which constitutes that overall model is good enough. R-squared value is around 0.96 which means that all the independent variables predict the manhours needed by around 96%. In other words, 96% variation in the manhours needed is explained by these seven variables included in the model.If the p-values of each parameter are considered, it can be observed that for variables X1, X3, X4 and X5, the p-values are greater than the cutoff significant level of 0.05, therefore, these parameters are not considered as significant in predicting then manhours needed on individual basis. However, by staying in the overall model, they jointly predict the dependent variable of manhours needed. The other three independent parameters i.e. X2, X6 and X7 are statistically significant to predict the manhours needed as their p-values are less 0.05. The consequence of the results of F-statistics is that F-statistics describes that all the parameters jointly have the explanatory power of predicting the dependent variable. ...
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This paper contains data on statistical significance of the parameters. From the above table, it can be noticed that the constant is -148.22 which means these manhours are available to operate an establishment if there is no factor available…
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