Performance Lawn Equipment Database.xlsx. Requirements: In your spreadsheet file (which you will submit after finishing all the analyses), copy the 2014 Customer Survey, On-Time Delivery, Defects After Delivery, and Employee Retention worksheets from the data file Performance Lawn Equipment Database.xlsx and named them accordingly. Perform necessary data analyses, statistical tests, and regression analysis to answer the following questions. Note: for each question, you should conduct analyses within the corresponding sample data worksheet, and you should provide your verbal analysis and conclusion within the same sample data worksheet. That is, analyses and answers to question 1 should be inside the 2014 Customer Survey worksheet, analyses and answers to question 2 should be inside the On-Time Delivery worksheet, etc. To facilitate grading, make your analyses and answers (immediately) noticeable and properly formatted within each worksheet. Are there significant differences in ratings of specific product/service attributes in 2014 Customer Survey Worksheet?. What test will you use and what is your conclusion from the test? In the worksheet On-Time Delivery, has the proportion of on-time deliveries in 2014 significantly improved since 2010? What test will you use and what is your conclusion from the test? Have the data in the worksheet Defects After Delivery changed significantly over the past 5 years? You can compare 2014 data with 2010 data to answer the question of whether defects have changed significantly over the years. What test will you use and what is your conclusion from the test? Are there differences in employee retention due to gender, college graduation status, or whether the employee is from the local area in the data worksheet Employee Retention? You only need to test if there are differences in employee retention between genders, but you need to do the test for equality of variances first. Based on the result of the equality of variances test, what test will you use and what is your conclusion from the test? Read the first paragraph of the case on page 272 regarding the Defects After Delivery data. In a new worksheet (of the same Excel file), firstenter sample databy copying from the Defects After Delivery worksheet the monthly defects data from January 2010 to August 2011, i.e., these 20 monthly defects sample data will be used for regression analysis to predict what might have happened had the supplier initiative not been implemented. Second,do a scatterplotof the sample data andadd a trendlineto verify if linear model can fit the data.Discuss what you find. Next, use Data Analysis Regression tool to conduct a simple linear regression analysis. Please interpret the regression analysis output and check regression assumptions specified on page 248. That is, you need to discuss the regression statics and ANOVA table, interpret hypothesis tests for regression coefficients and confidence intervals for regression coefficients, and discuss residual plot and histogram of standard residuals. What can you say about this simple linear regression model and what do you propose to improve the prediction model? What is your conclusion regarding the question what might have happened had the supplier initiative not been implemented? (5 points) Read the second paragraph of the case on page 272 regarding Employee Retention.Do three scatterplotswith trendlinesto verify if there is a linear relationship between YearsPLE and age, YearsPLE and YrsEducation, and YearsPLE and College GPA. What are your conclusions? Next, you need to create a multiple linear regression model to determine the influence of age, YrsEducation, and College GPA on employee retention (YearsPLE). You need to first check for possible multicollinearity (check the correlation matrix). You need to interpret the regression analysis output (following example 8.12), i.e., you need to discuss the regression statics and ANOVA table, interpret hypothesis tests for regression coefficients and confidence intervals for regression coefficients, and discuss residual plot and histogram of standard residuals. Last, you need to identify the best regression model using XLMiner (following examples 8.19 and updated screenshots in chapter 8 PowerPoint). Briefly discuss the identified best model and its outputs. (6 points)