Statistical Significance of the Parameters Statistics Project

Statistical Significance of the Parameters - Statistics Project Example 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. Statistical Significance of the Parameters 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/ Question 2 H0: ?1 = ?2 = ?3 = ?4 = ?5 = ?6 = ?7 = 0 H1: At least one of the coefficients is not equal to 0 which would suggest that the model has explanatory power. F-statistics = 60.173 Therefore the hypothesis acceptance region is [0,F 8-1,25-8 ]=[0, F 7,17] From the F-statistics table the acceptance region is [0 and 2.61] Since F-statistics computed lies outside this region which is 60.173, therefore the hypothesis can be rejected as the all the parameters can jointly predict the manhours needed. This thing can also be proved by p-values of F-statistics which is less than the threshold of 0.05. R-squared tells about the goodness-of-fit of the model which is around 0.96. Therefore, 96% of the variation in the manhours, can be explained by all seven... 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 tha t all the parameters jointly have the explanatory power of predicting the dependent variable.