Ed161 Start-up Problem Due Jan 7 2000 The High School and Beyond data set (HSB) is provided in the course directory. The student-level file is HSB1.dat and the school-level file is HSB2.dat path: /usr/class/ed260/HSB*.dat or /afs/ir.stanford.edu/class/ed260/HSB*.dat Update 1/5. In addition to the web-access indicated on the main page, the data sets are available in both the ed161 and ed260 class directories. To wit: elaine6.Stanford.EDU% cd /usr/class/ed161 elaine6.Stanford.EDU% ls HSB1.DAT HSB2.DAT WWW elaine6.Stanford.EDU% cd elaine6.Stanford.EDU% cd /usr/class/ed260 elaine6.Stanford.EDU% ls HSB1.DAT HSB2.DAT WWW Level-1 file. The student level-1 file has 7185 cases (rows) and four variables (not including the school ID). In HSB1.dat the columns are School ID minority, an indicator for student ethnicity (1 = minority, 0 = other) female, an indicator for student gender (1 female, 0 = male) ses, (a standardized scale constructed from variables measuring parental education, occupation, and income) mathach, a measure of mathematics achievement In HSB2.dat, which contains 160 schools with 6 variables per school, the columns are School ID size (school enrollment) sector (1 = Catholic, 0 = public) pracad (proportion of students in the academic track) disclim (a scale measuring disciplinary climate) himnty (1 = more than 40% minority enrollment, 0 = less than 40%) meanses (mean of the SES values for the students in this school who are included in the level-1 file; typical of the HLM guys these don't match exactly) For this exercise take the data from first public school and the first catholic school listed in these data sets. 1. For each school construct a descriptive plot of mathematics achievement: a stem-and-leaf, a dot plot, or other plot. Comment. 2. For each school construct a numerical descriptive summary of mathematics achievement (e.g. the Minitab describe command). Comment. 3. Taking the students in the first public school to be a sample from the population of public school students and the students in the first catholic school to be a sample from the population of catholic school students, use an appropriate statistical procedure to compare mathematics achievement in public schools and catholic schools. (e.g. 95% interval estimate for the difference between public schools and catholic schools). 4. Within each school, plot math achievement against SES and obtain the sample correlation coefficient. Which school has a larger value for the slope of a Math on SES regression? -------------- END