Description of parameters and model Sample Clauses

Description of parameters and model. The statistical technique (ANOVA) is used to find out the factors for which significant difference exists over different levels/values of factors. ANOVA is a method used to test for significant differences between means. Factors used for analysis in this section consist of the 40 important variant families mentioned in Appendix A and in addition to that product unit id, vehicle segment, current country, responsible department and region. These factors serve as independent variables for the ANOVA analysis whereas the dependent variable is money value of claims (claim amount). A database is created out of the claims from the vehicles which have finished their warranty. Specifically, vehicles which were delivered between January 2009 and December 2013 are taken into consideration here. Scania basic warranty of vehicles is 1 year, so it can be assured that most of the vehicles from December 2013 would have finished their warranty by now. The null hypothesis for our study would be that there is no significant difference between various values of the input variables. The value of Significance (Sig.) provides a way to accept or reject the null hypothesis. Taking a Sig. level of 0.05, we would accept the hypothesis for the cases where Xxx. value is greater than 0.05 whereas for values less than 0.05, the null hypothesis would have to be rejected. Rejection of null hypothesis would imply that there is a significant difference between various values of the independent variables. The F ratio in the output is the ratio of the mean sum of square values. In case of null hypothesis being true, F ratio would have a value close to 1.0. A large F ratio implies that the variation among group means is more than what can be expected to happen by chance. So, a large F ratio would imply that the null hypothesis is wrong. It needs to be mentioned here that not all the factors included in this study are mutually independent. In fact, several of them affect the inclusion and level of other factors. As an illustration, selection of a certain level of chassis height in the factor C0272 would constrain the level of the factor C0548 – roof height.
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