LIR 832: Computer Problem set #1How to do computer problem sets:1. Problem sets can be done and turned in as a group of two.2. If you are not able to make progress on a step in a problem set in 15 minutes and arebecoming frustrated, take a break. You aren’t going to solve it by hitting your headagainst the problem time and time again. Take a break, let your mind stew over theproblem, talk with someone else about it.3. The content of the homework is important and should be readily accessible to the grader.Show your work (typically this will be the log file from MINITAB), answer the questionsand compose any graphs carefully. For example, the proper labeling and structuring ofgraphs is important, graphs should convey information in a compact and readilyaccessible form.3. Although neatness is encouraged, this class is about substance more than presentation. Itis important to show your work so that I can review any errors and make suggestions onhow to correct them However, neatly laying out the graph on a page and making any textflow around it, a la corporate reports, is not important to this class.Computer Problem Set #1:Learning to Use MinitabUse the 20 percent subsample for managers and professionals for this assignment. This canbe downloaded from the course datasets and links web page.We will be working with three variables from the managers and professionals data set:uhour1 (usual hours on the main job), gender and race. You will want to download and print outthe Unicon Codebook. This codebook has detailed information on all of the variables in the dataset. You will need this to understand and answer the questions.1. Before we can begin working with data, we need to learn about the variables and understandhow we can use them. Review the codebook for uhour1, gender and race and answer thefollowing questions.a. What values can gender take on? Is gender a qualitative or quantitative variable? What values does race take on? Is it qualitative or quantitative? Explain youranswer.b. Although both of these variables are numeric, it may be more useful to turn them intotext (name) variables. The recode function is under Data. Data <Code < numeric to text(Note that you can either give the “new” column a name or specify acolumn location. If you do the first, MINITAB will toss the new columnsomewhere out to the right. If you do the second, you can place the newcolumn closer to your data, but you then need to go into the worksheet andput a name in) Recode each variable to a text variable with the following categories:Gender: (call this Sex)ManWomanRace (call this Racetxt)WhiteAfrican-AmericanNative AmericanAsianYou may be creative about the actual text assigned to each outcome. Which numeric values are associated with each outcome ?c. Now consider uhour1. What values can uhour take on? Is it qualitative orquantitative? If we took an average of this variable as it is currently constructed,would the average be meaningful? Explain. Use the graphing capabilities of MINITAB to make a histogram of uhour1and indicate where the difficulty is in this data. (Graph < histogram)To obtain a meaningful variable, we need to recode the data so that thevariable hour designation is not averaged into the data. Lets create a newvariable, call this UHOUR1rc (for UHOUR1 recode)Data < Code < numeric to numericMake the new variable name uhour1rcCode the -4 value as *Leave everything else blankTaking a look at the data file, what happens to the -4 value in the newvariable? What happens to all of the other values? Why is this useful?2. Calculate descriptive statistics and a summary graph for Uhour1rc. Stat < Basic Statistics < Display Descriptive Statistics < Uhour1rcStat < Basic Statistics < Graphical Summary < Uhour1rcWhat have you learned about usual hours of work from the descriptive statistics and thegraphical summary? Now create a graphical summary of uhour1. What are the differencesbetween uhour1rc and hour1? What are the strengths and weaknesses of the recoded uhour1variable?3. Can you calculate descriptive statistics for Sex or for Racetxt (the text versions of theGender and Hispanic variables)? What about for Gender and Race? Are the results of theRace and Gender variables useful? Explain.4. There are two approaches to obtaining useful information about qualitative variables. First,although we cannot calculate descriptive statistics on qualitative data, we can get frequencydistributions on the outcomes. We do this with the Table command in MINITAB.a. To use the table command to obtain frequency distributions on SexStatistics < Tables < Tally Individual Variables (put Sex into the variable box). Tryboth the counts and total percent commands. Then repeat this with the recoded racevariable. What is the distribution of managers and professionals by race.b. Another approach is to working with qualitative data is to recode the variables intoindicator variables and compute descriptive statistics. Indicator variables showwhether a particular condition exists or doesn’t exist. To do this with the Gendervariable, use the recode command.Data < Code < numeric to numeric (remember you need to designate a newvariable)Recode the value 1 to 0 (man)Recode the value 2 to 1 (woman)Call this new variable female. Now run descriptive statistics on this newvariable. What is the interpretation of the mean? How does the meancompare to the statistics you generated with the Table command?5. Now calculate descriptive statistics for uhour1rc and split it by gender. To do this, after youdesignate uhour1rc as your variable, make the by variable Sex. Do these results suggest adifference in typical hours of work between men and women? Explain. Split the sample byrace using the same approach. Is there evidence of a difference in typical hours of work byrace?6. Lets move ahead and test several hypotheses about usual hours of work.a. Using the full sample, test the hypothesis that, the workforce works, on average, 40 hoursper week? To do this, Statistics < basic statistics < one sample tSet the test mean to 40.You also need to set whether this is a one or two tailed test. You can set thisunder “option”. You set the greater than, not equal to, less than so that it isconsistent with the alternative hypotheses. In this case, our null is that, onaverage, managers and professionals work 40 hour per week; the alternative isthat they do not work 40 hours. MINITAB has you