Regression. The salary rate for an individual employee within a particular range for the job will not be reduced by reason of the operation of the salary system. For employees that are on the Skills Progression Pathway Levels 1 – 3, who are unable to maintain demonstrating mastery of advanced skills and undertaking designated responsibility, regression to a lower level, including to the standard salary scale (i.e. step 12 or 15) is an option. Before any form of regression is considered, the employee will have a discussion with their Manager to consider whether a programme of guidance and support will allow the employee to work at the expected level.
Regression. The wage rate for an individual employee within a particular range for the job will not be reduced by reason of the operation of the wages system.
Regression. The graph below shows a plot of base year weekly electricity or gas use against just one product group. (We discuss multiple product groups further down this page.) = + Slope = m = energy used to make one tonne of product Intercept = c = baseload of site A ‘line of best fit’; known as a ‘trendline’ in excel, has been added to the graph. The ‘equation’ for that line has been dispalyed and this gives the y=mx+c formula. The equation means that for one tonne of energy product made it takes ‘m’ kWh of energy to make it. And when no product is being made, the site uses ‘c’ kWh of energy. The R2 value gives an indication of how representative or accurate the line of best fit is. An R2 value above 0.7 is usually considered acceptable. In switching to a NOVEM, this type of analysis on a single product site is not needed (we can just use the already reported kWh/tonne values for that site). We have only provided this explanation to help with understanding how multiple products are handled.
Regression. The Secretary may regress the salary of an employee at Science and Technology Level 8 where they are rated as Not Effective. The salary may not be regressed below Science and Technology Level 7 and takes into account performance progression that would have occurred at the previous level, but for the period at the higher level. G7 Individual Flexibility Arrangements Individual Flexibility Arrangements (IFAs) were previously known as Individual Building Defence Capability Payments (BDCP) arrangements. IFAs provide remuneration and other conditions for employees in addition to normal arrangements in the Agreement. IFAs are designed to attract, develop and retain employees with the required skills, knowledge and experience considered critical to Defence capability. Further information APS People Policy- Individual Flexibility Arrangements
Regression. 17.1 Employees wishing to regress to positions below Institute Manager classifications should express their interest to the relevant Institute Director who will consider the request, along with other such requests, whenever an appropriate vacancy occurs.
Regression. Ortho-K mold retainers attempt to slow or stop the progression of myopia (nearsightedness). Nevertheless, regression of treatment may occur at some point. This may require a redesigning of the retainers to again achieve optimal vision.
Regression. We run the regression model again use the “DriveDistance” as an independent variable to the air travel demand model. The model formed : Y=β0 + β1 x1 + β2 x2 + β3 x3 Where, dependent variable travel Y = frequency; X1 = Drive Distance X2 = Travel Purpose X3 = Ticket fare Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 24.141 1.311 18.415 .000 DriveDistance -.009 .011 -.021 -.860 .390 Travel Purpose -9.622 .716 -.329 -13.433 .000 Ticket fare .001 .000 .117 4.784 .000 Table 6.19: Model results We can see from the model results (see table 6.19) that the significant level of Drive distance to travel frequency is 0.390 still higher than 0.05, so drive distance still not significant to air travel demand .The significant level of travel purpose and ticket fare are kept at the same level 0.000 in previous model. To further research of distance measurement it is necessary to covert the new ferry related variables FerrytimeW, FerryFare and tolls to time proxy ,the method of this issue will be introduced in next section.
Regression. Afterwards, we start to do regression in groups one by one. Results still not good until group 5 appears. Table 6.23: Regression result of total driving distance in group 5 In the Table 6.23 we can see that both goodness of fit and significance reach the standard requirements, significance is 0.1 in the table of Coefficients, less than 0.05. We can say that in the total time interval of 81 and 100.99, distances between home and airport is a significant variable to travel demand. Distance has a positive effect on demand, as distance grows, demand will also increase. Finally, we reach the conclusion that we expected. Group 7 also get a significant result, but not as good as group 5. Distance affects demand significantly when distance is between 126 and 150.99. Now two distance interval were proved to have significant affect to travel demand. Furthermore, we add two more variables which have proved to be significant variables in previous chapter into the regression. Group 7 turns to not significant again except purpose is still a significant variable. In the group 5, distance is significant variable, but the other two variable turn to not significant. Some other variables are tried in the regression, but no more ideal results appear. (See Table 6.24) Table 6.24: Regression Result of three variables in group 5 Service level is consider to be a significant variable to travel demand in previous research, since there is no information about the service level which was provided, we do the assumption to treat frequency of flights to capital city Oslo in these four airports as the measurement of service level. We check the flight table in ANOVA, found that during a work day, there are 9 flights to Oslo from Ålesund airport. In both Molde airport and Ørsta-Volda airport, there are 6 flights to Oslo. Frequency of flights in Kristiansund is the lowest among these four airports, with a total number of 4 flights. We set a new label with number of flights to Oslo according to the destination airport, and then do regression with the variable as service level. The result is service level is not a significant variable in our dataset. (See Table 6.25) Table 6.25: Regression result of service level Besides, due to the variance of population of each district, we are wondering whether there is any relationship between population and demand. Same way as insert service level into the dataset, we create a new label of population in the dataset. Correlate them with travel demand; on...
Regression. A move to a position of less Authority and/or to a lower type of aircraft.
Regression. 1) In the event of a work force reduction in Line of Progression Classifications, the least senior employee on the basis of job seniority in the affected classification will displace the least senior employee in the next lower classification in the Line of progression, provided all factors of ability as determined by the company are considered to be relatively equal. In the event of such a reduction in work force, a reasonable time period for training will be allowed.
