MATH 331 University of Calgary Statistics with SAS Project Statistics project using SAS application here is the info about the project 1. Download SAS on

MATH 331 University of Calgary Statistics with SAS Project Statistics project using SAS application

here is the info about the project

1. Download SAS on your own Computer or you may find in On Campus labs where SAS is available.

2- Once you have access to SAS ( Your own PC or Labs), proceed by exploring how to use the software to run procedures. All files are in SAS folder under module #2.

3- Save your practice work of your exploration sessions in a word file and a pdf a one page proof with your name, course and semsmter, and submit through the link under this item.( in case, you encounter some problems, and you could not access SAS, describe the steps you took and where you encountered these problems on the discussion board. Don’t wait until the last minute.)

Note:

Read the attached document posted under the item SAS and reproduce similar results using your own data. Practice using and running the commands.

Make sure to use the dollar sign $ after entering a nominal variable.

so

just need practice the problem and use different date and put my name on file

I attached so problems

due is 03/07 11pm Maryland US time MATH 331 _SAS
DATA auto ;
INPUT make
DATALINES;
AMC
4099
AMC
4749
AMC
3799
Audi
9690
Audi
6295
BMW
9735
Buick
4816
Buick
7827
Buick
5788
Buick
4453
Buick
5189
Buick 10372
Buick
4082
Cad.
11385
Cad.
14500
Cad.
15906
Chev.
3299
Chev.
5705
Chev.
4504
Chev.
5104
Chev.
3667
Chev.
3955
Datsun 6229
Datsun 4589
Datsun 5079
Datsun 8129
$ price mpg rep78 weight length foreign ;
22
17
22
17
23
25
20
15
18
26
20
16
19
14
14
21
29
16
22
22
24
19
23
35
24
21
3
3
3
5
3
4
3
4
3
3
3
3
3
3
2
3
3
4
3
2
2
3
4
5
4
4
2930
3350
2640
2830
2070
2650
3250
4080
3670
2230
3280
3880
3400
4330
3900
4290
2110
3690
3180
3220
2750
3430
2370
2020
2280
2750
186
173
168
189
174
177
196
222
218
170
200
207
200
221
204
204
163
212
193
200
179
197
170
165
170
184
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
;
RUN;
PROC PRINT DATA=auto(obs=10);
RUN;
PROC MEANS DATA=auto;
RUN;
PROC UNIVARIATE DATA=auto;
VAR PRICE;
RUN;
PROC FREQ DATA=auto;
TABLES rep78 ;
RUN;
PROC FREQ DATA=auto ;
TABLES rep78 * foreign ;
RUN;
TITLE ‘Bar Chart with Discrete Option’;
PROC GCHART DATA=auto;
VBAR rep78/ DISCRETE;
RUN;
PROC CORR DATA=auto ;
VAR price mpg weight length ;
RUN;
PROC REG DATA=auto;
MODEL mpg = weight length foreign ;
RUN;
PROC GLM DATA=auto;
CLASS foreign ;
MODEL mpg = foreign ;
RUN;
You have six values: for (x,y) : (1,1), (1,2),(1,3), (2,1), (2,2), (2,3) so for the
covariance you should compute six values by subtracting from the x-coordinate its
mean and from the y coordinate its mean.
https://support.sas.com/documentation/cdl/en/procstat/63104/HTML/default/viewe
r.htm#procstat_univariate_sect064.htm
Example Computing Confidence Limits for the Mean, Standard Deviation, and
Variance
This example illustrates how to compute confidence limits for the mean, standard deviation, and variance
of a population. A researcher is studying the heights of a certain population of adult females. She has
collected a random sample of heights of 75 females, which are saved in the data set Heights:
data Heights;
label Height = ‘Height (in)’;
input Height @@;
datalines;
64.1 60.9 64.1 64.7 66.7 65.0 63.7 67.4 64.9 63.7
64.0 67.5 62.8 63.9 65.9 62.3 64.1 60.6 68.6 68.6
63.7 63.0 64.7 68.2 66.7 62.8 64.0 64.1 62.1 62.9
62.7 60.9 61.6 64.6 65.7 66.6 66.7 66.0 68.5 64.4
60.5 63.0 60.0 61.6 64.3 60.2 63.5 64.7 66.0 65.1
63.6 62.0 63.6 65.8 66.0 65.4 63.5 66.3 66.2 67.5
65.8 63.1 65.8 64.4 64.0 64.9 65.7 61.0 64.1 65.5
68.6 66.6 65.7 65.1 70.0
;
run;
The following statements produce confidence limits for the mean, standard deviation, and variance of the
population of heights:
title ‘Analysis of Female Heights’;
ods select BasicIntervals;
proc univariate data=Heights cibasic;
var Height;
run;
The CIBASIC option requests confidence limits for the mean, standard deviation, and variance. For
example, Output 4.9.1 shows that the 95% confidence interval for the population mean is
.
The ODS SELECT statement restricts the output to the “BasicIntervals” table; see the section ODS Table
Names.
The confidence limits in Output 4.9.1 assume that the heights are normally distributed, so you should
check this assumption before using these confidence limits. See the section Shapiro-Wilk Statistic for
information about the Shapiro-Wilk test for normality in PROC UNIVARIATE. See Example 4.19 for an
example that uses the test for normality.
Output 4.9.1 Default 95% Confidence Limits
Analysis of Female Heights
The UNIVARIATE Procedure
Variable: Height (Height (in))
Basic Confidence Limits Assuming Normality
Parameter
Mean
Estimate 95% Confidence Limits
64.56667
64.06302
65.07031
Std Deviation
2.18900
1.88608
2.60874
Variance
4.79171
3.55731
6.80552
By default, the confidence limits produced by the CIBASIC option produce
95% confidence intervals. You can request different level confidence limits by
using the ALPHA= option in parentheses after the CIBASIC option. The
following statements produce 90% confidence limits:
title ‘Analysis of Female Heights’;
ods select BasicIntervals;
proc univariate data=Heights cibasic(alpha=.1);
var Height;
run;
The 90% confidence limits are displayed in Output 4.9.2.
Output 4.9.2 90% Confidence Limits
Analysis of Female Heights
The UNIVARIATE Procedure
Variable: Height (Height (in))
Basic Confidence Limits Assuming Normality
Parameter
Estimate 90% Confidence Limits
Mean
64.56667
64.14564
64.98770
Std Deviation
2.18900
1.93114
2.53474
Variance
4.79171
3.72929
6.42492
For the formulas used to compute these limits, see the section Confidence
Limits for Parameters of the Normal Distribution.
A sample program for this example, uniex07.sas, is available in the SAS
Sample Library for Base SAS software.

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