# 程序代写代做代考 ____________________Name

____________________Name

_______________________Name

Fall 2018

5. (6 pts) The program, cowdata.for.prog1.sas reads the raw data file, cowdata.dat. In this data set, there is a grouping variable, where 1 and 2 represent two groups of cows, one healthy and one sick. The weight of the cows is measured at five equally spaced occasions. Sort the data by group and look at the means to see if you can tell which group is which. Note that less healthy cows tend to gain less weight over time.

Now, in a separate program, take this wide data set and rewrite this as a long data set. In doing this, create two time variables,
TIME 0 1 2 3 4
and
TIMESQ 0 1 4 9 16.
These are linear and quadratic polynomials based on the time variable and could be used to fit a straight line, a quadratic curve or a combination of both.
After you create the long data, use PROC PRINT to look at the data and show that it is correct.

Use the following commands to look at the data to plot the data and compare groups.

proc sgplot ;
reg x=time y=logwt/group=grp;
run;

6. (6 pts) I would like you to take the file potroy.corr.to.class.sas and generate three the same three types of correlations we looked at in the R program: Pearson Product Moment Correlation, Kendall τs, and Spearman ρs .

You can get these in PROC CORR using
PROC CORR PEARSON KENDALL SPEARMAN;

You can output data sets with each type of correlation using
PROC CORR PEARSON KENDALL SPEARMAN OUTP=POTROYP OUTK=POTROYK OUTS=POTROYS;

I would like you generate the correlations as above which will create three new output data sets. Then I would like you to create a new data set which includes all three types of correlation coefficients.

If you look at any of the output files they will look like:

Obs _TYPE_ _NAME_ dep1 dep2 dep3 dep4

1 MEAN 22.1852 23.1667 24.6481 26.0926
2 STD 2.4343 2.1573 2.8176 2.7667
3 N 27.0000 27.0000 27.0000 27.0000
4 CORR dep1 1.0000 0.6256 0.7108 0.5998
5 CORR dep2 0.6256 1.0000 0.6349 0.7593
6 CORR dep3 0.7108 0.6349 1.0000 0.7950
7 CORR dep4 0.5998 0.7593 0.7950 1.0000

For example, these are the Pearsons. Notice that the names of the variables and the names of the rows do not help matters. As a result, you will want to do something to indicate that these correlations are Pearsons. There are a number of different ways to do this. You will also have to consider that will allow you to merge the three data sets,

You should set each of the three types of matrices in a different DATA step using the SET command. You should then rename the variables (e.g. dep1 becomes pearson1). Once you create the three new data sets, you will need to merge the files. Each file has the same descriptive statistics. You can either delete the descriptive statistics, or you can keep the descriptive statistics in one of the three data sets and add that to the merged data set. Once you merge the data sets, use PROC PRINT to show that you now have the three sets of correlations in a single SAS data set.

Next, these data were broken down by GENDER. I would like you create a table that includes the above statistics broken down by GENDER. You can either put this into a single table or create two separate tables. Use PROC PRINT to print out the table.

7. (6 pts) I have included the data set, new.cowdata.dat. Write a SAS program to read in the data. Make sure to use the correct informat when necessary. For the input statement make sure that you are reading the data out of the correct columns. Probably, the simplest way to check column locations is to open the data in a text reader and use your cursor to identify the columns. Note that I’ve changed the column locations from the previous version of this data set. After you read in the data, use PROC PRINT to show that you have read in the data correctly.

For the R questions, I would like you to turn in both the R script (e.g. a .R file) and the output (either a copy of the console or the results of sink()). You can also put everything into a Word file if you prefer. Make sure that I enough to be able to look at your program and what the program does.

For the SAS questions, turn in the program and the output.