This is a quantitative methods subject. Any thorough answers will be rated immediately with a like:)

The traffic controller at Sydney's Kingsford Smith International Airport believes that the main reason for delayed aircraft departures are external to the airport management - namely, late arrivals and mechanical failures. In a report to management (which is concerned about possible internal causes of delays), the traffic controller decides to test his assertion using data collected for a random sample of 20 days on the following variables: Delay = number of aircrafts departing later than scheduled a day; Late = number of aircrafts arriving later than scheduled a day; Problem = number of mechanical failures a day. Using this data, he estimates a linear regression model with R and obtains the following printout. Note that several details are deliberately hidden on this printout. Call : 1m(formula = Delay ~ Late + Problem) Residuals: Min 1Q Median 3Q Max -1.9531 -0. 4509 0.1375 0. 5800 1. 6710 Coefficients: Estimate Std. Error t value Pr(>|t!) (Intercept) 0. 3496 -1. 630 0. 121448 Late 1. 1616 11. 191 2.91e-09 *# # Problem 0. 7858 0. 1560 0. 000101 *#* Signif. codes: 0 ' *** ' 0.001 '* * ' 0.01 '#' 0.05 . .' 0.1 . ' 1 Residual standard error: 0.927 on 17 degrees of freedom Multiple R-squared: 0.9487, Adjusted R-squared: F-statistic: 157.1 on and DF, p-value: (a) Calculate the missing details, i.e. i. the estimate of the intercept, ii. the std. error of Late, iii. the t value of Problem, the adjusted R-squared statistic, iv. the numerator degrees of freedom and the denominator degrees of freedom of the F-statistic. (b) Test the overall significance of the model at the 1% significance level. What are the hypotheses, the statistical decision and the conclusion? Be precise. (c) What do the unadjusted and adjusted coefficients of determination tell you about the fit of the estimated model to the data? Briefly explain the difference between these statistics. (d) Do the slope estimates have the signs you would expect? Why or why not? Develop and test appropriate hypotheses concerning the slope coefficients using t-tests at the 1% significance level. What do you conclude? (e) Carefully explain the meanings of the estimated slope coefficients. Does the y-intercept estimate have a logical interpretation? Why or why not? If it does, how do you interpret it