Using MS Excel, create a new workbook with the following (Save as w7_firstname_lastname.xlsx or w7_firstname_lastname.xls).
As an administrator, you recorded the requests from different locations (i.e. a few states are listed) in the sheet below, you may create your own sheet. You need to explain the collected data. In the table below, you have the requests against the locations.
Software Installation requestsSoftware Update requestsHardware Installation requestsWV231215MD1385VA1746DC1462NY741FL292111
You can use any data gathering for your choice
Create at least 5 statistical functions and graphs relating to the data.
From the data discuss the trend (your conclusion, what does this data mean for your planning), You can use the question of “What if… then”
Introduction to What-if Analysis
Submit your week 7 work in w7_firstname_lastname.xlsx or w7_firstname_lastname.xls
Requirements
Points
Data Layout
20
Statistical functions and graph
50
Excel functions can also be used to perform mathematical computations in addition to formulae. A mathematical procedure is applied to a set of cells on a worksheet by statistical functions. The SUM function, for instance, is used to add the values in a set of cells. When using a mathematical procedure on a set of cells, functions are more effective than formulae. You would need to input each cell position to the formula individually if you wanted to use a formula to add the values in a range of cells.
k of burnout in teachers, as well as probable variances in these categories based on gender and teaching experience. Gender found no systematic correlations, whereas teaching experience had a curvilinear association with GPK, a negative linear relationship with self-efficacy, and no significant relationship with burnout, according to path analysis. GPK was found to be a negative predictor of teacher burnout both directly and indirectly through its positive relationship with teaching self-efficacy, according to mediation studies. In these analyses, only teaching specific self-efficacy, not general self-efficacy, served as a mediator; consequently, the discovered predictive effects are specific to instructors’ professional competence. (Lauermann et al., 2016).
The present research measures in a group of 374 Italian teachers—curricular and specialist support teachers—the relationship between self-perceived instructional competence, self-efficacy, and burnout. The current study, which took place between April and December 2020, is the second phase of a bigger study that took place between November 2018 and October 2019, and was reproduced during COVID-19. Participants completed an anamnestic questionnaire, the Assessment Teaching Scale, and the Maslach Burnout Inventory in both phases of research; an ad hoc questionnaire (to measure teaching practices) and the Teacher Sense of Self Efficacy Scale was added in the second phase. Personal accomplishment appears to be a predictor of emotional, socio-relational, and didactic competences before and during the pandemic, as confirmed by the data; elevated personal accomplishment appears to be a predictor of emotional, socio-relational, and didactic competences before and during the pandemic (Pellerone et al., 2021).
The goal of this study is to identify burnout levels in a sample of high school teachers that worked during the COVID-19 pandemic, with the goal of evaluating the relationship between burnout levels, trait emotional intelligence, and socioemotional competences (Autonomy, Regulation, Prosocial Behaviour and Empathy). A total of 430 high school teachers from various regions of Spain were included i