olivia trent is a newly hired systems analyst with your group olivia has always felt that questi

Name: olivia trent is a newly hired systems analyst with your group olivia has always felt that questionnaires are a waste now that you will be doing a systems project for ground ride sharing grs 72208
Uploaded: 2023-05-05T03:04:26-08:00
Duration: 4 min 51 s
Channel: Sri K
Description: olivia trent is a newly hired systems analyst with your group olivia has always felt that questionnaires are a waste now that you will be doing a systems project for ground ride sharing grs 72208

Step 1: Define clear objectives for the questionnaire — state exactly what you need to learn abo

Olivia trent is a newly hired systems analyst with your...

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I just need to know HOW to perform what has been asked of me. I have been given a data set (not shown) but need to know what to do for each question and part. Use alpha = .05 for all questions. Refer to your results to answer the assignment questions. Part I: Universal Toys (Assignment Questions 1-5) This dataset contains 5 variables used for this assignment. Label Range of Possible Values ConscT1 1-10 Cog Abil 80-130 Hiring Mgr 1-7 HR Mgr 1-7 Performance 10-50 Run a regression analysis using conscientiousness (ConscT1), cognitive ability (CogAbil), unstructured interview with hiring manager (Hiring Mgr), and structured interview with HR Manager (HR Mgr) to predict performance ratings (Performance) at Universal Toys. Evaluate the results of the overall model. Is it a valid model? How do you know? What is the R-square value and what does it mean? Now evaluate the predictors. Indicate which ones are statistically significant and which ones are not. If any predictors are not significant, drop them and re-run the equation. Calculate the predicted performance for each of the 2 job candidates below, as appropriate. In other words, only use the model if it is valid and only include statistically significant predictors in your model and equation. Candidate ConscT1 CogAbil HiringMgr HRMgr Janice 1 100 3 2 Michael 2 90 6 3 Part II: Bus Drivers (Assignment Questions 6-10) This dataset contains 4 variables used for this assignment. Label Range of Possible Values Risky Beh 1-20 Focus 1-20 Driving Know 0-100 Performance 1-10 Run a regression analysis using risky behavior tendency (Risky Beh), ability to focus (Focus), and bus driving knowledge (Driving Know), to predict performance ratings (Performance) for bus drivers. Evaluate the results of the overall model. Is it a valid model? How do you know? What is the R-square value and what does it mean? Now evaluate the predictors. Indicate which ones are statistically significant and which ones are not. If any predictors are not significant, drop them and re-run the equation. Calculate the predicted performance for each of the 2 job candidates below, as appropriate. In other words, only use the model if it is valid and only include statistically significant predictors in your model and equation. Candidate RiskyBeh Focus Driving Know Robert 3 17 50 Suszanne 10 18 90

Sri K.

3. (15 pts) A company is introducing new software, and wishes to train its employees in its use. In order to determine the best training method a number of employees will be divided into 2 groups,

Md.Daniyal A.

Demand for systems analysts in the consulting industry is greater than ever. Graduates with a combination of business and computer knowledge—some even from liberal arts programs—are getting great offers from consulting companies. Once these people are hired, they frequently switch from one company to another as competing companies lure them away with even better offers. One consulting company, D&Y, has collected data on a sample of systems analysts with undergraduate degrees they hired several years ago. The data are in the file C08_02.xlsx. The variables are as follows: Starting Salary: employee’s starting salary at D&Y On Road Pct: percentage of time employee has spent on the road with clients State Univ: whether the employee graduated from State University (D&Y’s principal source of recruits) CIS Degree: whether the employee majored in Computer Information Systems (CIS) or a similar computer-related area Stayed 3 Years: whether the employee stayed at least three years with D&Y Tenure: tenure of employee at D&Y (months) if he or she moved before three years D&Y is trying to learn everything it can about retention of these valuable employees. You can help by solving the following problems and then, based on your analysis, presenting a report to D&Y. 1. Although starting salaries are in a fairly narrow band, D&Y wonders whether they have anything to do with retention. b. Calculate a 95% confidence interval for the mean starting salary of all employees who stay at least three years with D&Y. Do the same for those who leave before three years. Then calculate a 95% confidence interval for the difference between these means. c. Among all employees whose starting salary is below the median ($37,750), calculate a 95% confidence interval for the proportion who stay with D&Y for at least three years. Do the same for the employees with starting salaries above the median. Then calculate a 95% confidence interval for the difference between these proportions. 2. D&Y wonders whether the percentage of time on the road might influence who stays and who leaves. Repeat the previous problem, but now do the analysis in terms of percentage of time on the road rather than starting salary. (The median percentage of time on the road is 54%.) 3. Find a 95% confidence interval for the mean tenure (in months) of all employees who leave D&Y within three years of being hired. Why is it not possible with the given data to calculate a confidence interval for the mean tenure at D&Y among all systems analysts hired by D&Y? 4. State University’s students, particularly those in its nationally acclaimed CIS area, have traditionally been among the best of D&Y’s recruits. But are they relatively hard to retain? Calculate one or more relevant confidence intervals to help you make an argument one way or the other. Employee Starting Salary On Road Pct State Univ CIS Degree Stayed 3 Years Tenure 1 38900 57% Yes Yes No 11 2 42300 68% Yes Yes No 5 3 39800 75% No Yes No 21 4 35700 29% No Yes Yes 5 5 40400 71% No Yes No 19 6 36400 62% No Yes No 18 7 37300 41% Yes No Yes 8 8 36600 37% No Yes No 17 9 34600 65% No Yes No 20 10 42300 53% Yes Yes No 16 11 37800 73% No Yes Yes 12 12 39700 60% No Yes No 12 13 36100 56% Yes Yes Yes 14 14 36100 42% Yes Yes Yes 15 15 33800 35% No Yes Yes 16 16 38900 36% No Yes Yes 17 17 35500 50% No Yes No 15 18 37600 58% No Yes Yes 19 19 38100 78% Yes Yes Yes 20 20 38300 16% No No Yes 21 21 34600 29% No Yes Yes 22 22 36300 26% No Yes Yes 23 23 37200 59% Yes Yes No 14 24 37700 55% Yes Yes Yes 25 25 36600 45% No Yes No 13 26 41000 81% Yes Yes No 9 27 40800 111% Yes Yes No 7 28 36700 56% Yes Yes Yes 29 29 37700 56% No Yes No 22 30 37700 42% No Yes No 11 31 39300 69% Yes Yes No 16 32 37500 50% No Yes No 18 33 40000 69% Yes Yes No 9 34 41500 64% Yes No No 5 35 39600 72% No Yes No 18 36 36900 42% No Yes Yes 37 37 39500 62% No Yes No 5 38 37800 20% No Yes No 23 39 38600 57% No Yes No 8 40 40600 70% Yes Yes Yes 41 41 41600 63% Yes Yes Yes 42 42 40800 44% No No Yes 43 43 36200 68% No Yes No 31 44 39600 59% No Yes No 10 45 36100 56% Yes Yes Yes 46 46 36100 42% No Yes Yes 47 47 39500 53% Yes Yes No 17 48 39100 38% No Yes Yes 49 49 40300 53% Yes Yes Yes 50 50 38800 78% Yes Yes Yes 51 51 39400 34% Yes Yes No 27 52 38400 23% No Yes Yes 53 53 35200 33% No Yes Yes 54 54 36100 24% No No Yes 55 55 37100 44% No Yes Yes 56 56 37200 64% Yes Yes No 23 57 36800 46% Yes No Yes 58 58 36500 27% No No Yes 59 59 35900 29% No No Yes 60 60 37900 76% Yes Yes No 14 61 37900 58% No Yes No 26 62 37900 28% No Yes No 14 63 36700 37% No Yes Yes 64 64 37400 49% No Yes No 19 65 38200 62% Yes Yes Yes 66 66 36600 51% Yes Yes No 7

Madhur L.

Transcript

00:01 Hello everyone, let us look into the question.

00:04 So here we have five parts.

00:06 So we will answer the questions one by one.

00:09 So for answer for the first question is we have to run the regression analysis for the given data using the data analysis then regression excel command.

00:28 In excel we are doing.

00:31 Go to data in the top ribbon, then from that go to data analytics then regression.

00:42 Then we have to select the variables.

00:45 So choose as y the variable y represents the performance and x1.

00:56 So here we have multiple variables.

00:58 So x1 is constant t1 and x2 cogable, x3 hiring manager, x4 hr manager.

01:15 These are all independent variables.

01:17 Okay, so by performing by selecting and entering the variables, then we will get the regression analysis.

01:28 The answer for the second question is for model evaluation check if in anova table of excel regression output, we have to select significance here.

01:50 If it is less than 0 .05, so conclude that the model is valid...

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