Let X and Y be independent random variables from the bivariate normal distribution where X ~ N(μ1= 15,σ1^2= 5) and Y ~ N(μ2= 10,σ2^2= 3). Generate 10000 random numbers for X and Y using the Box Muller transformation. -Print a histogram for both X and Y and supply the appropriate descriptive statistics to verify your requirements. -Print a Contour and Surface plot in SAS.   I don't know how to begin. I can't seem to find anything helpful for Box Muller Transformations coding.    ... View more

I'm given these two equations:                        PDF: f(x) =1/(π(1+x^2))    -∞ ≤ x ≤ ∞                   CDF: F(x) =(1/2) + arctan(x)/π  -∞ ≤ x ≤ ∞ and I am asked to generate 10,000 random numbers using the Inversion Method for Cauchy Distribution. Here is the code I have started: PROC IML;        n=10000;       myran = j(n,1, 0);        uniform = uniform(myran);       exponential = (tan(uniform*pi-(pi/2))); CREATE uniform2 FROM uniform[colname={'x'}]; APPEND FROM uniform; CREATE exp2 FROM exponential[colname={'y'}]; APPEND FROM exponential; QUIT;   but my log says: "ERROR: (execution) Matrix has not been set to a value." after the exponential statement.  how do I correct for this? I'm not sure how to account for the -∞ ≤ x ≤ ∞ part.  subsequently, I am asked to create a subset of the 10000 Cauchy Dist. of random numbers where -10<x<10   ... View more

Hi all. So I have this problem and I have no idea where to begin:  Five bodies of water were measured for strontium levels.  The five bodies of water were:  Var1=Grayson’s Pond, Var2=Beaver Lake, Var3=Angler’s Cove, Var4=Appletree Lake, Var5=Rock River. Determine if the average strontium levels differ by the body of water.  Thirty water specimens were used (6 at each location) and the strontium level measured.  The data set is Strontium.txt  (attached). Here it is from notepad: Var1 Var2 Var3 Var4 Var5 28.2 39.6 46.3 41 56.3 33.2 40.8 42.1 44.1 54.1 36.4 37.9 43.5 46.4 59.4 34.6 37.1 48.8 40.2 62.7 29.1 43.6 43.7 38.6 60 31 42.4 40.1 36.3 57.3   Perform the appropriate data manipulation step for analysis. Perform an ANOVA on these data. Check the assumptions of the hypothesis tests.  State whether each assumption is met and provide support for your statements. Consider your check of the ANOVA assumptions in Part 2). Choose the nonparametric ANOVA procedure that you feel is more appropriate, based on the assumption you consider to be most tenuous.  Re-analyze the data using this nonparametric test and compare the results with the results of the parametric ANOVA you performed in Part 1). I know I would need to add formats/labels but I just have no idea how to begin. Anything will help!! I just need some output to analyze!!! ... View more

I have this data set here were the first x is sales, x2=profits, and x3=assets. Measurements are in millions of dollars.   Company                   X1        X2         X3 General Motors    126,974  4,224  173,297 Ford                       96,933  3,835  160,893 Exxon                    86,656  3,510    83,219 IBM                        63,438  3,758   77,734 General Electric    55,264  3,939  128,344 Mobil                     50,976  1,809    39,080 Philip Morris          39,069  2,946    38,528 Chrysler                36,156     359    51,038 Du Pont                35,209   2,480    34,715   I need to import the data manually and then I am asked to manually find correlation matrix using only matrix manipulation. I assume I somehow I use proc IML. If so, I'd have to create 3 matrices, correct? I don't know where to start.   ... View more

So here's a data set:    This is a set of observations on 27 people. Several things were noted. The first variable is the hospital in which the data was collected, the second variable is the gender of the person, then a measure of the amount of iron in the blood called hemoglobin (Haemo), then packed cell volume (Pcv), white blood cell count (Wbc), the number of lymphocytes (Lympho), neutrophil (Neutro), and serum lead concentration (Lead). hosp gender haemo pcv wbc lympho neutro lead A M 13.4 39 4100 14 25 17 B f 14.6 46 5000 15 30 20 a F 13.5 42 4500 19 21 18 A M 15 46 4600 23 16 18 b m 14.6 44 5100 17  . 19 B m 14 44 4900 20 24 19 b f 16.4 49 4300 21 17 18 A f 14.8 44 4400 16 26 29 a F 15.2 46 4100 27 13 27 B M 15.5 48 8400 34 42 36 A M 15.2 47  . 26 27 22 B F 16.9 50 5100 28 17 23 a F 14.8 44 4700 24 20 23 A M 16.2 45 5600 26 25 19 b M 14.7 43 4000 23 13 17 A F 14.7 42 3400 9 22 13 B m 16.5 45 5400 18 32 17 B f 15.4 45 6900 28 36 24 B M 15.1 45 4600  . 29 17 a M 14.2 46 4200 14 25 28 b F 15.9 46 5200 8 34 16 B f 16 47 4700 25 14 18 A F 17.4 50 8600 37  . 17 A M 14.3 43 5500 20 31 19 B F 14.8 44 4200 15 24 19 a f 15.5  . 4900 17 27 16 a m 26.5 .  5200 19 32 21 1) the missing data is noted with a period (.). How do write a statement for this? I would import data as a .csv, after would I write the missing data statement, then run it? I also need to account for the upper case letters vs lower case in the first two columns. Would I write a if-else statement? or would I just need to proc format or proc label? and what order should it be in? here's my start: data Hospital; infile 'D:\hospital.csv' delimiter= ',' firstobs=2; input hosp $ gender $ haemo pcv wbc lympho neutro lead; run;   ... View more

The data set attached has 31 variables; each variable is the response to a quiz item and is coded 1=true and 2=false. Missing data are coded as either 0 or 9. The first 17 variables are responses to a pretest. After, a posttest was given.  Only the first 14 items on the pretest were repeated on the posttest, so there are a total of 31 variables (17 pretest repsonse, 14 post). One attachment is the .txt and the other is a word doc so you can see the layout slightly better. 1) How do I read the data into SAS and create a permanent SAS data set?  2) The correct answers are:  1. True  2. True 3. False 4. False 5. True 6. True 7. False 8. False 9. False 10. True 11. True 12. True 13. True 14. True 15. True 16. True 17. True Create two new variables, one for the proportion of correct responses on the pretest and one for the proportion of correct responses on the posttest. Missing values should be considered an incorrect answer.  3) Conduct an appropriate summary statistics to determine whether scores on the post test or pretest were higher.    Here's what I attempted: LIBNAME MyLib "D:\Statistical Data Management/MyLib"; data mylib.DataHW4; infile 'D:\Statistical Data Management\MyLib\Homework\Assignment #4\DataHW4.txt' delimiter = '09'x; input ID 1-12 PreTest 59-75; run; data mylib.DataHW4PreTest; SET mylib.dataHW4; array X[1] PreTest; do i=59 to 75; when(1) answer = 'true'; when(2) answer = 'false'; end; output; end; run;   that didn't get me far, not even past number 1. Any help is appreciated! ... View more