Description

Q1) The following attached data files need to be combined into a single SAS data set.
• AO Division.dat
• CORP Division.dat
• EmpData.dat
• FINANCE IT Division.dat
• FLIGHT OPS Division.dat
• HR Division.dat
• Personal.dat
• SALES Division.dat
b) Combine DIVISION data with EmpData to create EmpData_div_merged dataset. Then merge EmpData_div_merged dataset with Personal dataset to create the complete dataset and name it as Air_Emps_Full.
I. Ensure that the Air_Emps_Full dataset does not include the PHONE variable and update the salary variable to be named CurrentSalary. Include an appropriate format for this variable.
II. Assign descriptive labels to the variables using LABEL statement (Hint: Read the online document on LABEL statement available at https://documentation.sas.com, or any other related SAS online document). Comment on the advantage of labelling variables.
d) Produce a PDF showing the first 10 observations with all variables in Air_Emps_Underpaid.

• For employees with at least 35 years of service, the new salary includes:
o A $3,500 at increase
o A 2.5% increase for anyone whose job is at level 2 or level 3 (The number in a job type indicates the level of that job type. E.g., Job type, FLTAT3 is at level 3)
o A 1.5% raise for anyone whose job is at level 1 (e.g. MECH01) o A 2.0% raise for all other job levels
• For employees with at least 25 years of service (but not 35 or more), the new salary includes:
o A $2,000 flat increase o A 2.0% increase for level 2 or 3 jobs o A 1.0% increase for level 1 jobs o A 1.75% increase for all other jobs
• For all others, the new salary includes:
o No flat increase
o A 2.50% increase for level 2 or 3 jobs o A 1.25% increase for level 1 jobs o A 0.75% increase for all other jobs
• If years of service cannot be computed for an employee o New Salary should be missing
o A note should be printed to the log, indicating the employee ID that needs to be investigated
a) Ensure the final data set is sorted by EMPID and that your two new variables (New Salary and Years of Service) have appropriate labels and formats.
b) Without recreating the Air_Emps_Underpaid dataset in Q1, we want to subset it to only include individuals whose new salary would still classify them as underpaid. To do this, using the above conditions, create the New_Salary variable in Air_Emps_Underpaid dataset as well, but only keep records that would still be classified as underpaid even after they got a raise (New salary is below $45,000).
c) Write both datasets to a single PDF using titles and footnotes to differentiate the two data sets (10 observations from each dataset).

Consider the attached three SAS datasets: aprtarget.sas7bdat, maytarget.sas7bdat, and junetarget.sas7bdat
a) Write a data step to concatenate these three SAS data sets and create a new data set called work.q3vienna. Use the RENAME= option to rename any variables necessary to combine the datasets.
c) Now modify the DATA step to create two new variables: TotalTar and TotalRev.
– TotalTar is the total targeted number of economy and first class passengers.
– TotalRev is the total revenue expected from economy and first class passengers.

c) Create a new temporary data set called compare by merging the sort_b and sort_s data sets by the variable FlightID. Subtract CargoRev from CargoTarRev to create a new variable called LostCargoRev.
d) Produce a PDF to print the merged data set compare (print only the variables CargoTarRev, CargoRev, and LostCargoRev) and label the LostCargoRev variable. Format the LostCargoRev variable with a dollar sign and two decimal digits.

Q5) Produce separate PDF documents for each output in part a-c. a) Use an iterative DO loop to plot the following equation:

𝑦=3𝑥2−5𝑥+10

Use values of x from 0 to 10, with an increment of 0.10 .

b) You have daily temperatures for each hour of the day for two cities (Dallas and Houston). The 48 temperature values are strung out in several lines like this:

80 81 82 83 84 84 87 88 89 89
91 93 93 95 96 97 99 95 92 90 88
86 84 80 78 76 77 78
80 81 82 82 86
88 90 92 92 93 96 94 92 90
88 84 82 78 76 74

The first 24 values represent temperatures from Hour 1 to Hour 24 for Dallas and the next 24 values represent temperatures for Hour 1 to Hour 24 for Austin. Using the appropriate DO loops, create a dataset (Temperature) with 48 observations, each observation containing the variables City, Hour, and Temp.

c) You invest $1,000 a year at 4.25% interest, compounded quarterly. How many years will it take to reach $30,000?. Use DO WHILE or DO UNTIL statements to solve this problem.