Exercícios Teóricos

 

 

Exercícios Teóricos

 

Enviar por Favor para o E-mail da Disciplina:

gestao.estat.cert@gmail.com

Colocar seu nome e numero e tipo (teórico ou pratico) no assunto do e-mail.

 

O SAS (linguagem de computação) e o Weka (principal programa para ensino de IA do mundo).

 Exercício Teórico 1 - Elaborar 10 slides sobre o Sistema SAS, importância para sua profissão no mercado de trabalho. Impacto em BI, IA e CD.

                Dead Line = 29/9/2022

Exercício Teórico 2 - Slides (12 slides, fonte do título maximo32, e do corpo do slide máximo 28) Importância da Ciência de Dados, Inteligência Artificial, Machine Learning, Deep Learning, Data Mining, Big Data e Text Mining para voce no mercado de trabalho. Ou seja para aumentar empregabilidade e competitividade do economista. Foco em habilidades de mercado que nos levarão para a situação mercadológica denominada BOS ==> Hipercompetitividade - Hiperinovação e Inovação Disruptiva.

                 Dead Line = 6/10/2022

Exercício Teórico 3 - Elaborar 8 slides sobre Inteligência Artificial e Robotica Aplicada para seu mercado de trabalho futuro.

Dead Line = 13/10/2022

Exercício Teórico 4 - Slides (12 slides, fonte do título maximo32, e do corpo do slide máximo 28). Importância da Certificação Internacional da Qualidade e dos Sistemas de Gestão mais importantes do Mundo para voce no mercado de trabalho. Ou seja para aumentar sua empregabilidade e competitividade. Foco em habilidades de mercado que nos levarão para a situação mercadológica denominada BOS ==> Hipercompetitividade - Hiperinovação e Inovação Disruptiva. Asistir a 5 videos e > 100 slides que estão nas ponstgens de Insights para Seminarios.

                 Dead Line = 20/10/2022

Ainda por definir

Exercício Teórico 4 - Slides (12 slides, fonte do título maximo32, e do corpo do slide máximo 28) Importância da Hiperinovação, Inovação Disruptiva e Hipercompetitividade  para o economista no mercado de trabalho. Apresente algumas ferramentas.

    Dead Line = 2 semanas è 5/10/2021

Exercício Teórico 5 - Elaborar 4 slides sobre Empregabilidade e Hipercompetitividade.

Dead Line: 19/10.

 

 

 

Exercício Teórico 6 - Elaborar 10 slides sobre Empreendedorismo no Mundo Capitalista.

Dead Line: 26/10.

Exercícios Práticos

 Exercícios Práticos

A nota dos exercícios práticos, teóricos e seminário são 80% da nota final. A prova final somente é 20 % da nota final.

 

Enviar por Favor para o E-mail da Disciplina:

gestao.estat.cert@gmail.com

 

O SAS (linguagem de computação, principal habilidade de mercado para os tomadores de decisão em empresas do Brasil, versão gratuita excelente) e o Weka (principal programa para ensino de IA do mundo).

Exercício 1 Pratico: 

Invente um exemplo (se inventar, envie uma copia para gasarrie@usp.br) para aplicar ML Supervisionado para Classificação ou troque os sinais de interrogação pelos últimos dígitos do seu numero USP  no arquivo do Dinheiro Falsificado, dead line: 29/9/2022 . 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

215.4,130,129.9,8.5,9.7,141.4,FALSE

214.9,129.4,129.5,8.2,9.9,141.5,FALSE

214.5,129.5,129.3,7.4,10.7,141.5,FALSE

214.7,129.6,129.5,8.3,10,142,FALSE

215.6,129.9,129.9,9,9.5,141.7,FALSE

215,130.4,130.3,9.1,10.2,141.1,FALSE

214.4,129.7,129.5,8,10.3,141.2,FALSE

215.1,130,129.8,9.1,10.2,141.5,FALSE

214.7,130,129.4,7.8,10,141.2,FALSE

214.4,130.1,130.3,9.7,11.7,139.8,TRUE

214.9,130.5,130.2,11,11.5,139.5,TRUE

214.9,130.3,130.1,8.7,11.7,140.2,TRUE

215,130.4,130.6,9.9,10.9,140.3,TRUE

214.7,130.2,130.3,11.8,10.9,139.7,TRUE

215,130.2,130.2,10.6,10.7,139.9,TRUE

215.3,130.3,130.1,9.3,12.1,140.2,TRUE

214.8,130.1,130.4,9.8,11.5,139.9,TRUE

215,130.2,129.9,10,11.9,139.4,TRUE

215.2,130.6,130.8,10.4,11.2,140.3,TRUE

215.2,130.4,130.3,8,11.5,139.2,TRUE

215.1,130.5,130.3,10.6,11.5,140.1,TRUE

215.4,130.7,131.1,9.7,11.8,140.6,TRUE

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

214.7,130.6,130.1,11.8,10.5,139.8,TRUE

214.7,130.4,130.1,12.1,10.4,139.9,TRUE

214.8,130.5,130.2,11,11,140,TRUE

214.4,130.2,129.9,10.1,12,139.2,TRUE

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

214.6,130.2,130.4,10.5,11.8,139.7,TRUE

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 2 Pratico: 

Invente um exemplo (se inventar, envie uma copia para gasarrie@usp,br) para aplicar ML Supervisionado para Predição ou Causas e Efeito ou Regressão. Tambem pode trocar dos sinais de interrogação pelos últimos dígitos do seu numero USP  no arquivo da Satisfação do Cliente, rode no SAS e no Weka. Dead line: 6/10/2022:

Arquivo de Dados para o Weka

@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.??107775,97.8021978,96.77419355,13.???24691,98.9010989,9?.?2608696

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

Programa e dados para o SAS do Exercício 2

Data Customer;

Input Bu_Unit  Sales  Price Qu_level Claims NPS Satisfac;

Cards;

1 65.??107775 97.8021978 96.77419355 13.???24691 98.9010989 9?.?2608696

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;

UML - Cluster Analysis - ANOVA and MANOVA

  UML - Cluster Analysis - ANOVA and                                     MANOVA

UML - Unsupervised Machine Learning: Machine Learning Não Supervisionado

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 20.2 60.7 3200

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;

Arithmetic Averages of Categories

Categ BMI Movm Kcal

AT 20.5 54.4 3100

PR 25.3 2.7 2650

SE 25.6 2.9 2700

SEM 23.1 16.6 2950

Program to do MANOVA

data imc_dat;

input cat $ imc corr kcal;

cards;

ATL 20.2 60.7 3200

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 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