There are four worksheets or tabs in this Excel spreadsheet. Take a few minutes to review the spreadsheet in its entirety. The four worksheet titles and the purpose of each worksheet are as follows.• IT Help Desk Data—contains the data to be analyzed• Data Legend—provides information about the data.• Statistical Analysis—used in conjunction with Excel functions to determine the average number of days the tickets are open, the distribution of the requester’s seniority, the type and severity of the problem, the assigned priority level, and the end user satisfaction level. The missing information may then be filled in as it is calculated.• QR Analysis Essay—provides essay questions to answer after you perform your analysis using Excel and Cognos. Answers should be entered directly inside each box.• Scenario• Your supervisor has been assigned to be the interim manager of the IT Help Desk and needs your help. The previous help desk manager retired after many years in that position and never conducted any analysis of his team’s performance. Coming into this new job, your supervisor would like to gain more insight into the strengths and challenges of her help desk team.• First, you will need to collect data on the help desk tickets. This data will provide insight into the help desk’s performance and give you clues on how to improve the unit. You learn that the help desk has several goals. These goals relate to the measurement of key performance metrics, such as the number of days tickets are open and client satisfaction ratings. You would like to know how well the team members achieved these goals under the previous manager. Analysis of the help desk data will help you understand the team’s performance on these goals.• You plan to analyze the data using standard statistical methods in Microsoft Excel. However, one of your colleagues suggests that you also use a data analytics tool called Cognos Analytics, which could help you uncover hidden information. Cognos Analytics can also create graphic depictions of the key information to help you visualize the data. You decide to use both approaches.• Your task is to conduct data analysis and prepare a final report for your supervisor about your findings. Your report will also include a set of recommended solutions for improving the performance of the help desk.• Once you have reviewed the scenario, review the project overview, approximate time commitment, and competencies that you will be responsible for in this project.In the Excel spreadsheet Technology Template that you have downloaded, review the Statistical Analysis worksheet. You will use the COUNTIF and SUM Function in Excel to prepare the data for future analysis. This will include some simple statistical analysis as well as charts and graphs to present the data. Use Excel formulas to fill in the gray cells under the column labeled “Count” in the five tables in that worksheet.A. Summarize the IT data by presenting categories of data in summary tables and counting them, totaling them, and calculating percentages:o Use the COUNTIF Function to count each item in each table.o Use the SUM Function to total the tables when required.o Calculate percentages for each table as required.Note: Format cells appropriately. Remember to make smart use of reference cells in formulas (avoid typing in numbers or text into formulas—instead, point to other cells) and use relative and absolute cell references to make copying formulas faster and easier. Your supervisor will look for your appropriate use of these tools!B. Complete your analysis based on the results that appear. Answer the following questions by typing into the text box in the “QR Analysis Essay” tab:• Which types of employees are most likely and least likely to open a ticket?• Which types of problems are most and least common?• What can you tell about the satisfaction level?• What can you tell about the number of days a ticket is open?
In Section 2 of the Statistical Analysis tab of the Excel spreadsheet Technology Template, use the appropriate Excel function to complete the table. Calculate the summary statistics of the DaysOpen data on the IT-Help-Desk-Data tab (Column 1). Use the summary statistic Excel functions of =AVERAGE, =MEDIAN, =MODE, =STDEV.S, =VAR.S, =KURT, =SKEW, =MIN, =MAX, =SUM, =COUNT to derive these statistics for the three data categories. Standard error and range should also be calculated.A. The next task will be to provide the input and output. Since you want to have statistics for the DaysOpen data, you will provide the location of the data on the spreadsheet in the input box. Indicate the inclusive cells for the selected categories. To do so, type in the field or capture the column with your mouse and the field will show in the input range. Check the labels box so you have titles for the categories. Then select “New Worksheet Ply,” and your output will be a new sheet on your tab.B. Label your new sheet “Summary Stats” and format the columns for readability.C. Compare your calculations using the data analysis feature to the results you reached in the previous step, which you calculated manually with individual functions. You should not have a large disparity. This tool is handy for quick computations and you will use it again to create your pivot table in the following step!In this step, you will add a new tab, name it “Graphs,” and provide the following graphs in the worksheet:A. pie charts should show the following:o the distributions of requestor’s seniorityo types of problemB. bar charts should show the following:o the distributions of the severity of the problemso priority level assigned.Be sure to select data labels and create legends for each graph.
Source: Used with permission from Microsoft.In the next step, you will use IBM Cognos Analytics to perform data discovery.This step will introduce you to a powerful, state-of-the-art data analytics tool, IBM’s Cognos Analytics. This dynamic tool supports quantitative reasoning.Click the following resources to learn about Cognos Analytics and to complete this part of the project:• Review Cognos Anayltics to learn more about how it can be effectively applied to data.• Use the Getting Started with Cognos Analytics document to create a free trial account with Cognos Analytics, log in to your Cognos Analytics account, import the IT Help Desk Data into Cognos Analytics, and review the Cognos Analytics tutorials.Finally, you will analyze the IT Help Desk Data using Cognos Analytics. Use the knowledge and techniques that you learned about Cognos Analytics to analyze the data set. Then answer the following questions and type your answers in on the “QR Analysis Essay” tab.• How is the help desk department performing?• Which specific ITOwner (help desk technician) is a high performer? Which ITOwner is the lowest performer?• What relevant information about the help desk did you uncover from your analysis?• What recommendations do you have for the help desk?
The current research intends to explore the impacts of relational separation, saw look and outward appearance on individuals’ look conduct in social collaboration. Alongside this essential target, the impacts of social nervousness on singular contrasts in look conduct were concentrated also. There are a few primary discoveries. Right off the bat, members invested more energy in direct look when the symbol was standing close or demonstrating direct look, while outward appearances didn’t instigate any huge impacts. The eye area is known to give an abundance of data in social association (Letourneau and Mitchell, 2011) and this is bolstered by the present investigation. Contrasted and other facial zones, it was discovered that members arranged their look to the symbol’s eye locale more regularly than face or mouth. In addition, the impacts of relational separation and the symbol’s look gave off an impression of being bigger in members’ look that focusing on the eye area also. As to auxiliary goal, it was discovered that excitement just roused members with HSA to look less at the symbol’s mouth. Past writing saw that individuals discovered both over-proxemic relational separation and danger related outward appearances stimulating, particularly when these signs were went with saw direct look (Ioannou et al., 2014; Schrammel et al., 2009). By and by, there were conflicting social discoveries for look responses. The current investigation seems to help the translation maintained by feeling acknowledgment contemplates, expressing that undermining social upgrades would stand out. In spite of the fact that it was normal that members may hold direct look notwithstanding the symbol’s look abhorrence in conversational setting, the outcomes didn’t meet the desire. Longer immediate look length might be identified with improved consideration in compromising circumstances. On the other hand, members may show more straightforward look as they feel the social commitment to show equal closeness. Feeling acknowledgment concentrates frequently discover individuals looking at undermining outward appearances quicker and that’s just the beginning (Eisenbarth and Alpers, 2011; Wells et al., 2016). So also, members in the present examination arranged more to the symbol in exciting conditions. At the point when the symbol was standing close, member may feel like their own space was being attacked. As a self-related signal, the symbol’s immediate look can raise the feeling of inconvenience also (Ioannou et al., 2014), since members could have the sentiment of being inside the attentional spotlight. Albeit a few examinations proposed that apparent direct look alone was deficient to evoke excitement (Binetti et al., 2015; Helminen, 2017), this appears not to be the situation in the ebb and flow investigate. This is potentially in light of the fact that the symbol kept up direct look all through the discourse conveyance. As substantiated by the past investigations, delayed direct look could demonstrate potential strength and social fitness (Doherty-Sneddon and Phelps, 2005; Hamilton, 2016). Both over-proxemic relational separation and delayed direct look are scaring to individuals, and they can subsequently prompt expanded feeling of danger and consideration improvement in connection. Notwithstanding encouraging recognition, individuals additionally seem to experience issues in withdrawing from compromising upgrades (Koster et al., 2004). This may conceivably clarify the more drawn out direct look span saw in the current investigation. From a transformative point of view, organic readiness empowers people to identify and concentrate on conceivably compromising upgrades to expand the opportunity of endurance (Sussman et al., 2016). Driven by improved mindfulness, look can be utilized to focus on sources or signs of dangers in the earth. In the conversational errand, symbols were the significant social targets and given the greater part of the data in communication. The majority of the feeling acknowledgment examines have indicated that individuals’ consideration is to a great extent committed to the most demonstrative or striking district of danger related improvements (Schurgin et al., 2014). Reliable with this, members looked longer at symbol’s face, particularly the eye district, when the feeling of danger expanded. Eyes are significant somewhat in light of the fact that they can show one’s visual consideration in space (Kolkmeier, 2015). By seeing symbol’s eye area, members might pick up data to figure out where the danger is found. As the relational separation became over-proxemic, the symbol could be the wellspring of danger to members. Henceforth, it would be significant for members to know whether they were the objectives of symbol’s forceful methodology by investigating symbol’s eyes. What’s more, the eye area likewise to a great extent encourages face recognition (Gilad et al., 2009). In compromising circumstances, it is vital for individuals to accumulate data effectively. Subsequently, members would will in general become familiar with the symbol’s character by investigating their eyes when the feeling of danger expanded. On the other hand, the outcomes can be deciphered as far as social commitment. Rather than forcing danger, close relational separation and saw direct look may advance the feeling of social commitment showed by the symbols. Regarding the Intimacy Equilibrium model (Argyle and Dean, 1965), it was normal that members may turn away their look to keep up the proper degree of closeness as the symbol rudely drew closer. All things considered, the outcomes appear to be conflicting with this. Studies on relational separation frequently embrace Hall’s model to characterize agreeable and awkward physical methodology, and a few of them offer help for the Intimacy Equilibrium model (Bailenson et al., 2003; Ioannou et al., 2014). Be that as it may, the vast majority of the “intelligent situations” in these examinations essentially have experimenter strolling towards members, as well as the other way around. The momentum look into shows that the models may not have a similar degree of legitimacy in conversational setting. In spite of the fact that the separation of “close” condition in the present investigation falls into the zone of private separation characterized in Hall’s model (Bailenson et al., 2001), it may not be as nosy true to form. In addition, the opposite relationship of proxemic relational separation and shared look in keeping up proper closeness may not be effectively material in conversational collaboration. One of the significant contrasts between the past and current settings is the feeling of social commitment, which individuals ought to likely get themselves all the more socially associated with conversational communication. In contrast to the past writing, the conversational setting in the present examination makes a situation for the symbol and member to participate in at the same time. The limit of wrong closeness can be higher in such situation, and henceforth the proxemic relational separation may not end up being as nosy true to form. Like physical nearness, looking at interactant’s face signals closeness and social commitment in conversational communication also (Rossano, 2012). While proxemic relational separation advances closeness, symbol’s immediate look can show that member is being inside the attentional spotlight. In spite of the fact that writing has seen the propensity for audience members to hold direct look in spite of speakers’ look repugnance (Hamilton, 2016), the outcomes don’t seem to help this. All in all individuals will in general show direct look in cooperation to gather data and impart closeness (Cummins, 2012), and one’s commitment may cultivate comparable degree of interactant’s commitment. At the point when the symbol was demonstrating deflected look or remaining far away, the feeling of social association among symbol and member may decrease. Correspondence is considered as a significant social standard in collaboration (Qualls and Corbett, 2016). At the point when symbol exhibits an elevated level of social commitment in the cooperation, members may feel the social commitment to show more straightforward look as reaction. Contrasted and relational separation, the impacts of apparent look on individuals’ look responses appear to be progressively explicit. It was discovered that members looked more at symbol’s head when he was standing close, however not when he was indicating direct look. These are comparable with the discoveries in Kolmeier’s work (2015). At the point when members were participating in discussion with symbols, Kolmeier estimated members’ look bearing dependent on their head direction and found no noteworthy impact. Inexact look bearing estimation was recognized as a constraint in his work, and Kolmeier questioned whether the significant impacts of apparent look in conversational setting were ignored. The ebb and flow inquire about utilized eye-following strategy with high precision and tended to this restriction. As talked about, it is proposed that speaker’s look heading does impacts audience’s apparent closeness or danger. Given the saliency of the eye locale in social connection, this can clarify why the impact of symbol’s look is sufficiently enormous to be noticeable just when the examination is restricted to members’ immediate look term. It appears that relational separation I nfluenced look conduct to a bigger degree than symbol’s look. In any case, it is additionally conceivable that the distinction might be basically because of the expanded zone in member’s visual space which involved by symbol’s head in “close” conditions. In spite of the fact that it is hard to decipher the distinctions with exact hypothetical ramifications, the saliency of the eye district in social collaboration is obviously illustrated. Not just the eye district, the present investigation shows that mouth is additionally a significant prompt in conversational communication contrasted with other facial regions. Members looked all the more frequently at symbol’s mouth when he was standing close or indicating direct look. This is conceivably identified with the saliency of mouth in various media view of discourse, which was exhibited in different examinations also (Bail>GET ANSWER Let’s block ads! (Why?)