SQL - Apostila Gabriel
data milho;
input cidade $ estado $ tamanho L_Liquid;
cards;
Primav MT 3.5 1567
Ponta_G PR 2.4 1678
Rondon MT 5.2 1456
Pirai PR 3.2 1768
;
segunda-feira, 26 de junho de 2023
SQL - Apostila Gabriel
Aula 26/6
Pauta:
- Inglés com musica:
APRENDA INGLÊS COM MÚSICA - Love Of My Life- Queen.
- Linguagens Visual Analytic:
https://observablehq.com/@d3/bar-chart-race-explained
- Laboratório mostrar possibilidade de trabalharmos juntos:
https://sites.google.com/usp.br/lab-ia-cd-r-g-gabriel
Enviar os exercícios para o e-mail da disciplina.
E-Mail da Disciplina:
gestao.estat.cert@gmail.com
- Pesquisa de Gustavo - Covid e Qualidade de Vida:
https://forms.gle/sjhVEJbKuserJNLC6
- Programinhas em SQL
- Salário Cientista de Dados (intersecção de 3 conjuntos):
- Pode ser Administrador, Biólogo, Economista, Engenheiro
etc.
-
15.000 Brasil (Minha Filha)
- 32.000 Inglaterra
- PG China 20 bolsas, eu oriento IA - CD - R - G 4.0 e 5.0
- Murilo Garcia dos Santos Pereira - Aprovado
- Melhorar nota assistindo aulas nas férias, segundas das 20:50 às 22:30h, entrega de exercícios e outra prova
- Prova, 10% da nota, semana que vem: 3/6
- Quem não puder faz pela Internet, marcamos horário
- Somente encima do Exercício 1 A, para não estressar. Treinaremos hoje.
-
Avaliação da Disciplina.
Linguagens de Programação - Visual Analytic
Exemplo do que podemos fazer com linguagens de programação de nivel medio
https://observablehq.com/@d3/bar-chart-race-explained
segunda-feira, 19 de junho de 2023
Aula 26/6
Pauta:
- Maria Cecilia Site curso Power BI - Link
- Horario de Consulta Sabado 24/6 das 17:30 Às 18:30
- Normalmente Sabados das 15 Às 16 horas
- Horario de Consultas Quintas das 19 às 20
Exercícios:
Exercícios:
Enviar os exercícios para o e-mail da disciplina.
E-Mail da Disciplina:
gestao.estat.cert@gmail.com
Exercício 1 A – Obrigatório
Satisfação do Cliente com Numero USP em sinais de interrogação (Rodar em SAS - Weka - Libre Office). DL: 22/5/2023
Bu_Unit | Sales | Price | Qu_level | Claims | NPS | PV | Satisfac |
1 | 65,??108 | 97,??22 | 96,??419 | 13,??025 | 98,??11 | 19 | 97,??609 |
2 | 15,8371 | 98,9011 | 98,3871 | 12,34568 | 97,8022 | 29 | 98,91304 |
3 | 8,885232 | 100 | 100 | 11,11111 | 100 | 21 | 100 |
4 | 12,46401 | 98,9011 | 95,16129 | 12,34568 | 96,7033 | 94 | 96,73913 |
5 | 80,66639 | 21,97802 | 19,35484 | 100 | 2,197802 | 34 | 21,73913 |
6 | 32,16783 | 23,07692 | 22,58065 | 97,53086 | 3,296703 | 64 | 23,91304 |
7 | 23,44714 | 24,17582 | 24,19355 | 96,2963 | 2,747253 | 61 | 25 |
8 | 89,96298 | 24,17582 | 19,35484 | 95,06173 | 2,197802 | 25 | 26,08696 |
9 | 31,4274 | 64,83516 | 56,45161 | 50,61728 | 65,93407 | 10 | 65,21739 |
10 | 11,22995 | 65,93407 | 51,6129 | 49,38272 | 71,42857 | 3 | 66,30435 |
11 | 77,45784 | 70,32967 | 53,22581 | 46,91358 | 63,73626 | 56 | 68,47826 |
12 | 23,89963 | 68,13187 | 51,6129 | 45,67901 | 61,53846 | 4 | 67,3913 |
13 | 7,40436 | 86,81319 | 80,64516 | 25,92593 | 90,10989 | 90 | 86,95652 |
14 | 0,287947 | 87,91209 | 79,03226 | 24,69136 | 85,71429 | 48 | 85,86957 |
15 | 83,42246 | 87,91209 | 77,41935 | 22,22222 | 90,10989 | 78 | 88,04348 |
16 | 100 | 86,81319 | 75,80645 | 25,92593 | 84,61538 | 88 | 84,78261 |
|
@RELATION Customer
@ATTRIBUTE U_Neg REAL
@ATTRIBUTE Vendas REAL
@ATTRIBUTE Preco REAL
@ATTRIBUTE Niv_Qual REAL
@ATTRIBUTE Reclama REAL
@ATTRIBUTE NPS REAL
@ATTRIBUTE Satisf REAL
@DATA
1,65.??107775,97.??21978,96.??419355,13.??024691,98.??10989,97.??608696
2,15.83710407,98.9010989,98.38709677,12.34567901,97.8021978,98.91304348
3,8.885232415,100,100,11.11111111,100,100
4,12.46400658,98.9010989,95.16129032,12.34567901,96.7032967,96.73913043
5,80.66639243,21.97802198,19.35483871,100,2.197802198,21.73913043
6,32.16783217,23.07692308,22.58064516,97.5308642,3.296703297,23.91304348
7,23.44714109,24.17582418,24.19354839,96.2962963,2.747252747,25
8,89.9629782,24.17582418,19.35483871,95.0617284,2.197802198,26.08695652
9,31.42739613,64.83516484,56.4516129,50.61728395,65.93406593,65.2173913
10,11.22994652,65.93406593,51.61290323,49.38271605,71.42857143,66.30434783
11,77.45783628,70.32967033,53.22580645,46.91358025,63.73626374,68.47826087
12,23.89962978,68.13186813,51.61290323,45.67901235,61.53846154,67.39130435
13,7.404360346,86.81318681,80.64516129,25.92592593,90.10989011,86.95652174
14,0.287947347,87.91208791,79.03225806,24.69135802,85.71428571,85.86956522
15,83.42245989,87.91208791,77.41935484,22.22222222,90.10989011,88.04347826
16,100,86.81318681,75.80645161,25.92592593,84.61538462,84.7826087
Data Customer;
Input Bu_Unit Sales Price Qu_level Claims NPS Satisfac;
Cards;
1 65.??107775 97.??21978 96.??419355 13.??024691 98.??10989 97.??608696
2 15.83710407 98.9010989 98.38709677 12.34567901 97.8021978 98.91304348
3 8.885232415 100 100 11.11111111 100 100
4 12.46400658 98.9010989 95.16129032 12.34567901 96.7032967 96.73913043
5 80.66639243 21.97802198 19.35483871 100 2.197802198 21.73913043
6 32.16783217 23.07692308 22.58064516 97.5308642 3.296703297 23.91304348
7 23.44714109 24.17582418 24.19354839 96.2962963 2.747252747 25
8 89.9629782 24.17582418 19.35483871 95.0617284 2.197802198 26.08695652
9 31.42739613 64.83516484 56.4516129 50.61728395 65.93406593 65.2173913
10 11.22994652 65.93406593 51.61290323 49.38271605 71.42857143 66.30434783
11 77.45783628 70.32967033 53.22580645 46.91358025 63.73626374 68.47826087
12 23.89962978 68.13186813 51.61290323 45.67901235 61.53846154 67.39130435
13 7.404360346 86.81318681 80.64516129 25.92592593 90.10989011 86.95652174
14 0.287947347 87.91208791 79.03225806 24.69135802 85.71428571 85.86956522
15 83.42245989 87.91208791 77.41935484 22.22222222 90.10989011 88.04347826
16 100 86.81318681 75.80645161 25.92592593 84.61538462 84.7826087
;
proc print; run;
/* Input Bu_Unit Sales Price Qu_level Claims NPS Satisfac; */
proc reg;
model Satisfac = Sales Price Qu_level Claims NPS;
Run;
proc robustreg;
model Satisfac = Sales Price Qu_level Claims NPS;
Run;
Exercício 1 B – Facultativo, opcional. Criar um exemplo proprio de voces de ML S para Predição.
. DL: 29/5/2023
Exercício 2 A – Obrigatório. Data Crunching e ML Não Superv. para Clasificação
UML - Cluster Analysis - ANOVA
UML - Unsupervised Machine Learning: Machine Learning Não Supervisionado
Dados:
- Categoria: Variável Classificatória
- IMC: Primeira Variável Preditora - Indice de Massa Corporal
- Movim: Segunda Variável Preditora - Movimentação caminhando ou correndo por semana (Km)
- KCal: Quilocalorias consumidas por dia
Categ | IMC | Movim | KCal |
ATL | 20,? | 60,? | 32?? |
ATL | 21,3 | 54,8 | 3100 |
ATL | 19,3 | 49,6 | 2800 |
ATL | 21,1 | 52,3 | 3300 |
SEMI | 22,4 | 14,9 | 2600 |
SEMI | 21,9 | 17,8 | 2700 |
SEMI | 23,8 | 18,6 | 3200 |
SEMI | 24,1 | 15,1 | 3300 |
SEDE | 27,3 | 2,5 | 2700 |
SEDE | 23,4 | 4,3 | 2300 |
SEDE | 25,2 | 2,3 | 2600 |
SEDE | 26,4 | 2,6 | 3200 |
PROF | 26,2 | 4,1 | 2600 |
PROF | 24,2 | 2,1 | 2700 |
PROF | 25,4 | 1,9 | 2650 |
Obter as Médias por Data Crunching - Pivot Table
Obtaining Cluster Analysis
data People;
input Categ $ BMI Movm kcal;
cards;
AT 20.5 54.4 3100
PR 25.3 2.7 2650
SE 25.6 2.9 2700
SEM 23.1 16.6 2950
;
proc cluster outtree = Dendrog method = average;
var BMI Movm kcal;
id Categ;
run;
PROC TREE DATA = Dendrog;
RUN;
Program to do ANOVA
data People;
/* BMI: body mass index --> Índice de M. Corporal = Peso / (Altura * Altura)
Movm: Movement (Km por semana)
KCal : Kilocalories (ingeridas por dia)
ATL: Athletes
SEMI: Semi-athletes
SEDE: Sedentary
PROF: Professor
*/
input Categ $ BMI Movm Kcal;
cards;
ATL 20.? 60.? 32??
ATL 21.3 54.8 3100
ATL 19.3 49.6 2800
ATL 21.1 52.3 3300
SEMI 22.4 14.9 2600
SEMI 21.9 17.8 2700
SEMI 23.8 18.6 3200
SEMI 24.1 15.1 3300
SEDE 27.3 2.5 2700
SEDE 23.4 4.3 2300
SEDE 25.2 2.3 2600
SEDE 26.4 2.6 3200
PROF 26.2 4.1 2600
PROF 24.2 2.1 2700
PROF 25.4 1.9 2650
;
Proc ANOVA;
Class Categ;
Model BMI Movm Kcal = Categ;
Means Categ / Duncan Lines;
Run;
Exercício 2 B – Optativo. Elabore um exemplo analogo ao apresentado no Exercício 2 A
Exercício 3 A – Optativo. Testar os clusters utilizando MANOVA
Program to do MANOVA
data imc_dat;
input cat $ imc corr kcal;
cards;
DADOS
;
proc print;
run;
proc glm;
class cat;
model imc corr kcal = cat;
contrast " Atl e Semiat Vs Seden e Prof" cat 1 -1 -1 1;
contrast " Professor Vs Sedentario" cat 0 1 -1 0;
contrast " Atleta Vs Semiatleta" cat -1 0 0 1;
manova h=_all_ / printe printh;
contrast " Atl e Semiat Vs Seden e Prof" cat 1 -1 -1 1;
contrast " Professor Vs Sedentario" cat 0 1 -1 0;
contrast " Atleta Vs Semiatleta" cat -1 0 0 1;
run;
Elaboração de Contraste:
AT PR SE SEM
1 -1 -1 1 Atleta e Semiatleta Vs Professor e Sedentario
0 1 -1 0 Professor Vs Sedentario
1 0 0 -1 Atleta Vs Semiatleta
3 -1 -1 -1 Atleta Vs Outras Categorias
Exercício 3 B – Optativo. Elabore um exemplo analogo ao apresentado no Exercício 3 A
Exercício 4 A – Obrigatório
ML Supervisionado para Classificação: troque os sinais de interrogação pelos últimos dígitos do seu numero USP no arquivo do Dinheiro Falsificado, Dead Line: 5/6/2023 . Rode uma rede neural com 1-2-3 camadas de neurônios:
@RELATION banco
@ATTRIBUTE Length REAL
@ATTRIBUTE Left REAL
@ATTRIBUTE Right REAL
@ATTRIBUTE Bottom REAL
@ATTRIBUTE Top REAL
@ATTRIBUTE Diagonal REAL
@ATTRIBUTE Class {FALSE,TRUE}
@DATA
214.?,13?,13?.?,9,9.?,141,FALSE
214.6,129.7,129.7,8.1,9.5,141.7,FALSE
214.8,129.7,129.7,8.7,9.6,142.2,FALSE
214.8,129.7,129.6,7.5,10.4,142,FALSE
215,129.6,129.7,10.4,7.7,141.8,FALSE
215.7,130.8,130.5,9,10.1,141.4,FALSE
215.5,129.5,129.7,7.9,9.6,141.6,FALSE
214.5,129.6,129.2,7.2,10.7,141.7,FALSE
214.9,129.4,129.7,8.2,11,141.9,FALSE
215.2,130.4,130.3,9.2,10,140.7,FALSE
215.3,130.4,130.3,7.9,11.7,141.8,FALSE
215.1,129.5,129.6,7.7,10.5,142.2,FALSE
215.2,130.8,129.6,7.9,10.8,141.4,FALSE
214.7,129.7,129.7,7.7,10.9,141.7,FALSE
215.1,129.9,129.7,7.7,10.8,141.8,FALSE
214.5,129.8,129.8,9.3,8.5,141.6,FALSE
214.6,129.9,130.1,8.2,9.8,141.7,FALSE
215,129.9,129.7,9,9,141.9,FALSE
215.2,129.6,129.6,7.4,11.5,141.5,FALSE
214.7,130.2,129.9,8.6,10,141.9,FALSE
215,129.9,129.3,8.4,10,141.4,FALSE
215.6,130.5,130,8.1,10.3,141.6,FALSE
215.3,130.6,130,8.4,10.8,141.5,FALSE
215.7,130.2,130,8.7,10,141.6,FALSE
215.1,129.7,129.9,7.4,10.8,141.1,FALSE
215.3,130.4,130.4,8,11,142.3,FALSE
215.5,130.2,130.1,8.9,9.8,142.4,FALSE
215.1,130.3,130.3,9.8,9.5,141.9,FALSE
215.1,130,130,7.4,10.5,141.8,FALSE
214.8,129.7,129.3,8.3,9,142,FALSE
215.2,130.1,129.8,7.9,10.7,141.8,FALSE
214.8,129.7,129.7,8.6,9.1,142.3,FALSE
215,130,129.6,7.7,10.5,140.7,FALSE
215.6,130.4,130.1,8.4,10.3,141,FALSE
215.9,130.4,130,8.9,10.6,141.4,FALSE
214.6,130.2,130.2,9.4,9.7,141.8,FALSE
215.5,130.3,130,8.4,9.7,141.8,FALSE
215.3,129.9,129.4,7.9,10,142,FALSE
215.3,130.3,130.1,8.5,9.3,142.1,FALSE
213.9,130.3,129,8.1,9.7,141.3,FALSE
214.4,129.8,129.2,8.9,9.4,142.3,FALSE
214.8,130.1,129.6,8.8,9.9,140.9,FALSE
214.9,129.6,129.4,9.3,9,141.7,FALSE
214.9,130.4,129.7,9,9.8,140.9,FALSE
214.8,129.4,129.1,8.2,10.2,141,FALSE
214.3,129.5,129.4,8.3,10.2,141.8,FALSE
214.8,129.9,129.7,8.3,10.2,141.5,FALSE
214.8,129.9,129.7,7.3,10.9,142,FALSE
214.6,129.7,129.8,7.9,10.3,141.1,FALSE
214.5,129,129.6,7.8,9.8,142,FALSE
214.6,129.8,129.4,7.2,10,141.3,FALSE
215.3,130.6,130,9.5,9.7,141.1,FALSE
214.5,130.1,130,7.8,10.9,140.9,FALSE
215.4,130.2,130.2,7.6,10.9,141.6,FALSE
214.5,129.4,129.5,7.9,10,141.4,FALSE
215.2,129.7,129.4,9.2,9.4,142,FALSE
215.7,130,129.4,9.2,10.4,141.2,FALSE
215,129.6,129.4,8.8,9,141.1,FALSE
215.1,130.1,129.9,7.9,11,141.3,FALSE
215.1,130,129.8,8.2,10.3,141.4,FALSE
215.1,129.6,129.3,8.3,9.9,141.6,FALSE
215.3,129.7,129.4,7.5,10.5,141.5,FALSE
215.4,129.8,129.4,8,10.6,141.5,FALSE
214.5,130,129.5,8,10.8,141.4,FALSE
215,130,129.8,8.6,10.6,141.5,FALSE
215.2,130.6,130,8.8,10.6,140.8,FALSE
214.6,129.5,129.2,7.7,10.3,141.3,FALSE
214.8,129.7,129.3,9.1,9.5,141.5,FALSE
215.1,129.6,129.8,8.6,9.8,141.8,FALSE
214.9,130.2,130.2,8,11.2,139.6,FALSE
213.8,129.8,129.5,8.4,11.1,140.9,FALSE
215.2,129.9,129.5,8.2,10.3,141.4,FALSE
215,129.6,130.2,8.7,10,141.2,FALSE
214.4,129.9,129.6,7.5,10.5,141.8,FALSE
215.2,129.9,129.7,7.2,10.6,142.1,FALSE
214.1,129.6,129.3,7.6,10.7,141.7,FALSE
214.9,129.9,130.1,8.8,10,141.2,FALSE
214.6,129.8,129.4,7.4,10.6,141,FALSE
215.2,130.5,129.8,7.9,10.9,140.9,FALSE
214.6,129.9,129.4,7.9,10,141.8,FALSE
215.1,129.7,129.7,8.6,10.3,140.6,FALSE
214.9,129.8,129.6,7.5,10.3,141,FALSE
215.2,129.7,129.1,9,9.7,141.9,FALSE
215.2,130.1,129.9,7.9,10.8,141.3,FALSE
215.4,130.7,130.2,9,11.1,141.2,FALSE
215.1,129.9,129.6,8.9,10.2,141.5,FALSE
215.2,129.9,129.7,8.7,9.5,141.6,FALSE
215,129.6,129.2,8.4,10.2,142.1,FALSE
214.9,130.3,129.9,7.4,11.2,141.5,FALSE
215,129.9,129.7,8,10.5,142,FALSE
214.7,129.7,129.3,8.6,9.6,141.6,FALSE
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215.2,130.6,130.8,10.4,11.2,140.3,TRUE
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214.9,130.4,129.9,11.4,11,139.9,TRUE
215.1,130.3,130,10.6,10.8,139.7,TRUE
215.5,130.4,130,8.2,11.2,139.2,TRUE
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214.8,130.3,130.4,10.1,12.1,139.6,TRUE
215.1,130.6,130.3,12.3,10.2,139.6,TRUE
215.3,130.8,131.1,11.6,10.6,140.2,TRUE
215.1,130.7,130.4,10.5,11.2,139.7,TRUE
214.7,130.5,130.5,9.9,10.3,140.1,TRUE
214.9,130,130.3,10.2,11.4,139.6,TRUE
215,130.4,130.4,9.4,11.6,140.2,TRUE
215.5,130.7,130.3,10.2,11.8,140,TRUE
215.1,130.2,130.2,10.1,11.3,140.3,TRUE
214.5,130.2,130.6,9.8,12.1,139.9,TRUE
214.3,130.2,130,10.7,10.5,139.8,TRUE
214.5,130.2,129.8,12.3,11.2,139.2,TRUE
214.9,130.5,130.2,10.6,11.5,139.9,TRUE
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214.2,130,130.2,11,11.2,139.5,TRUE
214.8,130.1,130.1,11.9,11.1,139.5,TRUE
214.6,129.8,130.2,10.7,11.1,139.4,TRUE
214.9,130.7,130.3,9.3,11.2,138.3,TRUE
214.6,130.4,130.4,11.3,10.8,139.8,TRUE
214.5,130.5,130.2,11.8,10.2,139.6,TRUE
214.8,130.2,130.3,10,11.9,139.3,TRUE
214.7,130,129.4,10.2,11,139.2,TRUE
214.6,130.2,130.4,11.2,10.7,139.9,TRUE
215,130.5,130.4,10.6,11.1,139.9,TRUE
214.5,129.8,129.8,11.4,10,139.3,TRUE
214.9,130.6,130.4,11.9,10.5,139.8,TRUE
215,130.5,130.4,11.4,10.7,139.9,TRUE
215.3,130.6,130.3,9.3,11.3,138.1,TRUE
214.7,130.2,130.1,10.7,11,139.4,TRUE
214.9,129.9,130,9.9,12.3,139.4,TRUE
214.9,130.3,129.9,11.9,10.6,139.8,TRUE
214.6,129.9,129.7,11.9,10.1,139,TRUE
214.6,129.7,129.3,10.4,11,139.3,TRUE
214.5,130.1,130.1,12.1,10.3,139.4,TRUE
214.5,130.3,130,11,11.5,139.5,TRUE
215.1,130,130.3,11.6,10.5,139.7,TRUE
214.2,129.7,129.6,10.3,11.4,139.5,TRUE
214.4,130.1,130,11.3,10.7,139.2,TRUE
214.8,130.4,130.6,12.5,10,139.3,TRUE
214.6,130.6,130.1,8.1,12.1,137.9,TRUE
215.6,130.1,129.7,7.4,12.2,138.4,TRUE
214.9,130.5,130.1,9.9,10.2,138.1,TRUE
214.6,130.1,130,11.5,10.6,139.5,TRUE
214.7,130.1,130.2,11.6,10.9,139.1,TRUE
214.3,130.3,130,11.4,10.5,139.8,TRUE
215.1,130.3,130.6,10.3,12,139.7,TRUE
216.3,130.7,130.4,10,10.1,138.8,TRUE
215.6,130.4,130.1,9.6,11.2,138.6,TRUE
214.8,129.9,129.8,9.6,12,139.6,TRUE
214.9,130,129.9,11.4,10.9,139.7,TRUE
213.9,130.7,130.5,8.7,11.5,137.8,TRUE
214.2,130.6,130.4,12,10.2,139.6,TRUE
214.8,130.5,130.3,11.8,10.5,139.4,TRUE
214.8,129.6,130,10.4,11.6,139.2,TRUE
214.8,130.1,130,11.4,10.5,139.6,TRUE
214.9,130.4,130.2,11.9,10.7,139,TRUE
214.3,130.1,130.1,11.6,10.5,139.7,TRUE
214.5,130.4,130,9.9,12,139.6,TRUE
214.8,130.5,130.3,10.2,12.1,139.1,TRUE
214.5,130.2,130.4,8.2,11.8,137.8,TRUE
215,130.4,130.1,11.4,10.7,139.1,TRUE
214.8,130.6,130.6,8,11.4,138.7,TRUE
215,130.5,130.1,11,11.4,139.3,TRUE
214.6,130.5,130.4,10.1,11.4,139.3,TRUE
214.7,130.2,130.1,10.7,11.1,139.5,TRUE
214.7,130.4,130,11.5,10.7,139.4,TRUE
214.5,130.4,130,8,12.2,138.5,TRUE
214.8,130,129.7,11.4,10.6,139.2,TRUE
214.8,129.9,130.2,9.6,11.9,139.4,TRUE
214.6,130.3,130.2,12.7,9.1,139.2,TRUE
215.1,130.2,129.8,10.2,12,139.4,TRUE
215.4,130.5,130.6,8.8,11,138.6,TRUE
214.7,130.3,130.2,10.8,11.1,139.2,TRUE
215,130.5,130.3,9.6,11,138.5,TRUE
214.9,130.3,130.5,11.6,10.6,139.8,TRUE
215,130.4,130.3,9.9,12.1,139.6,TRUE
215.1,130.3,129.9,10.3,11.5,139.7,TRUE
214.8,130.3,130.4,10.6,11.1,140,TRUE
214.7,130.7,130.8,11.2,11.2,139.4,TRUE
214.3,129.9,129.9,10.2,11.5,139.6,TRUE
Exercício 4 B – Optativo. Elabore um exemplo analogo ao apresentado no Exercício 4 A
Exercício 5 A – Resolva o problema utilizando ML Não Supervisionado para Redução de Dimenção - Principal Componentes Analysis com Boplot
data pca_1;
input Categ $ IMC Movim KCal Colesterol Ac_Urico;
cards;
ATL 20.?1666667 53.?6666667 30?6.5 11?.26 5.???666667
PROF 25.16 2.64 2655 202.346 4.158
SEDE 25.33333333 3.066666667 2625 191.4966667 5.246666667
SEMI 22.88333333 16.83333333 2900 145.7366667 4.641666667
;
proc print; run;
/* input Categ $ IMC Movim KCal Colesterol Ac_Urico; */
title "PCA - Biplots";
title "Melhor fazer com Medias ou Medianas ou Trimedias";
proc prinqual plots=(MDPref)
/* project onto Prin1 and Prin2 */
; /* use COV scaling */
transform identity(IMC Movim Colesterol); /* identity transform */
id Categ;
ods select MDPrefPlot;
run;
Exercício 5 B – Optativo. Elabore um exemplo analogo ao apresentado no Exercício 5 A
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