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Demographic and healthcare statistics

A.Y. 2019/2020

Learning objectives

The purpose of teaching is:

- Apply the computer operating system's knowledge in order to manage data set

- Analyze the demographic and health phenomena using the statistical descriptive and inferential methods

- Apply the computer operating system's knowledge in order to manage data set

- Analyze the demographic and health phenomena using the statistical descriptive and inferential methods

Expected learning outcomes

The student will use the statistical descriptive and inferential methods in order to analyse the community care needs

The student will use the computer operating system to manage data set

The student will use the computer operating system to manage data set

**Lesson period:**
Second semester

**Assessment methods:** Esame

**Assessment result:** voto verbalizzato in trentesimi

Course syllabus and organization

### Single session

Responsible

**Prerequisites for admission**

Required preliminary knowledge: Basic algebra and elements of geometry.

**Assessment methods and Criteria**

Statistics: Written examination with a combination of multiple-choice questions (1 right, 4 wrong answers) and small problems or toy examples with a series of questions to reply. Every question has its mark indicated in the written examination. Students who gain a final mark higher than 30 will receive a mark of 30 cum laude. A calculator and the tables of the probability distributions found in Ariel are necessary to solve the written examination. Students are allowed to fill in a form with all the formulas to keep during the examination. Results will be provided to the students via Ariel.

Informatics: Resolution of a computer exercise using the Excel program (Microsoft). Each correct question is worth about 3 points. The sum of the points corresponds to the final outcome of the test. The test is passed if the sum of the points is greater than or equal to 18.

Informatics: Resolution of a computer exercise using the Excel program (Microsoft). Each correct question is worth about 3 points. The sum of the points corresponds to the final outcome of the test. The test is passed if the sum of the points is greater than or equal to 18.

**Sistemi di elaborazione delle informazioni**

**Course syllabus**

Introduction to the procedures based on hypothesis testing for quantitative or interval variables

Description of the sample using the sample mean and the sample variance

Description of the sample through the distribution and calculation of percentiles

Hypothesis test for the assessment of differences between groups: F-test

Homoscedasticity test

Hypothesis test for the evaluation of the differences between two groups: t-test for unpaired data

Use of the t-test to isolate differences between groups in multiple comparison problems

Hypothesis test for repeated measures: t-test for paired data

Hypothesis test based on the rank sum: the Mann-Whitney test

Introduction to the procedures based on the hypothesis test for two mutually exclusive nominal variables

Description of the sample by the frequency of the two categories

Construction of the contingency table

Hypothesis test to evaluate the differences between groups: χ2 tests

Use of the χ2 test to isolate differences between groups in multiple comparison problems

Hypothesis test for repeated measures: McNemar test

Description of the sample using the sample mean and the sample variance

Description of the sample through the distribution and calculation of percentiles

Hypothesis test for the assessment of differences between groups: F-test

Homoscedasticity test

Hypothesis test for the evaluation of the differences between two groups: t-test for unpaired data

Use of the t-test to isolate differences between groups in multiple comparison problems

Hypothesis test for repeated measures: t-test for paired data

Hypothesis test based on the rank sum: the Mann-Whitney test

Introduction to the procedures based on the hypothesis test for two mutually exclusive nominal variables

Description of the sample by the frequency of the two categories

Construction of the contingency table

Hypothesis test to evaluate the differences between groups: χ2 tests

Use of the χ2 test to isolate differences between groups in multiple comparison problems

Hypothesis test for repeated measures: McNemar test

**Teaching methods**

Teaching through slides and blackboard use with a problem-solving approach; practical sessions of exercises; slides and exercises available from Ariel for additional homeworks.

The lecturers and practical sessions have done in a classroom using computers using the Excel program (Microsoft). Each lesson has the structure of a practical training session: in each lesson one or more problems related to the various topics of the course are solved on the computer. Each practical training session is preceded by the projection of PowerPoint slides related to the theoretical topic of the session.

The lecturers and practical sessions have done in a classroom using computers using the Excel program (Microsoft). Each lesson has the structure of a practical training session: in each lesson one or more problems related to the various topics of the course are solved on the computer. Each practical training session is preceded by the projection of PowerPoint slides related to the theoretical topic of the session.

**Teaching Resources**

Pagano - Gauvreau "Principles of Biostatistics", Chapman and Hall/CRC

Bland "An Introduction to Medical Statistics" (English Edition) 4th Edition, Oxford University Press

Elmore - Wild - Nelson - Katz "Jekel's Epidemiology, Biostatistics, Preventive Medicine, and Public Health" Elsevier - Health Sciences Division, 2020 (in press)

S.A. Glantz, Primer on Bio-Statistics, McGraw-Hill. 1997

Microsfot Excel Tutorial, Microsoft

Bland "An Introduction to Medical Statistics" (English Edition) 4th Edition, Oxford University Press

Elmore - Wild - Nelson - Katz "Jekel's Epidemiology, Biostatistics, Preventive Medicine, and Public Health" Elsevier - Health Sciences Division, 2020 (in press)

S.A. Glantz, Primer on Bio-Statistics, McGraw-Hill. 1997

Microsfot Excel Tutorial, Microsoft

**Bioingegneria elettronica ed informatica**

**Course syllabus**

Introduction to the procedures based on hypothesis testing for quantitative or interval variables

Description of the sample using the sample mean and the sample variance

Description of the sample through the distribution and calculation of percentiles

Hypothesis test for the assessment of differences between groups: F-test

Homoscedasticity test

Hypothesis test for the evaluation of the differences between two groups: t-test for unpaired data

Use of the t-test to isolate differences between groups in multiple comparison problems

Hypothesis test for repeated measures: t-test for paired data

Hypothesis test based on the rank sum: the Mann-Whitney test

Introduction to the procedures based on the hypothesis test for two mutually exclusive nominal variables

Description of the sample by the frequency of the two categories

Construction of the contingency table

Hypothesis test to evaluate the differences between groups: χ2 tests

Use of the χ2 test to isolate differences between groups in multiple comparison problems

Hypothesis test for repeated measures: McNemar test

Description of the sample using the sample mean and the sample variance

Description of the sample through the distribution and calculation of percentiles

Hypothesis test for the assessment of differences between groups: F-test

Homoscedasticity test

Hypothesis test for the evaluation of the differences between two groups: t-test for unpaired data

Use of the t-test to isolate differences between groups in multiple comparison problems

Hypothesis test for repeated measures: t-test for paired data

Hypothesis test based on the rank sum: the Mann-Whitney test

Introduction to the procedures based on the hypothesis test for two mutually exclusive nominal variables

Description of the sample by the frequency of the two categories

Construction of the contingency table

Hypothesis test to evaluate the differences between groups: χ2 tests

Use of the χ2 test to isolate differences between groups in multiple comparison problems

Hypothesis test for repeated measures: McNemar test

**Teaching methods**

Teaching through slides and blackboard use with a problem-solving approach; practical sessions of exercises; slides and exercises available from Ariel for additional homeworks.

The lecturers and practical sessions have done in a classroom using computers using the Excel program (Microsoft). Each lesson has the structure of a practical training session: in each lesson one or more problems related to the various topics of the course are solved on the computer. Each practical training session is preceded by the projection of PowerPoint slides related to the theoretical topic of the session.

The lecturers and practical sessions have done in a classroom using computers using the Excel program (Microsoft). Each lesson has the structure of a practical training session: in each lesson one or more problems related to the various topics of the course are solved on the computer. Each practical training session is preceded by the projection of PowerPoint slides related to the theoretical topic of the session.

**Teaching Resources**

Pagano - Gauvreau "Principles of Biostatistics", Chapman and Hall/CRC

Bland "An Introduction to Medical Statistics" (English Edition) 4th Edition, Oxford University Press

Elmore - Wild - Nelson - Katz "Jekel's Epidemiology, Biostatistics, Preventive Medicine, and Public Health" Elsevier - Health Sciences Division, 2020 (in press)

S.A. Glantz, Primer on Bio-Statistics, McGraw-Hill. 1997

Microsfot Excel Tutorial, Microsoft

Bland "An Introduction to Medical Statistics" (English Edition) 4th Edition, Oxford University Press

Elmore - Wild - Nelson - Katz "Jekel's Epidemiology, Biostatistics, Preventive Medicine, and Public Health" Elsevier - Health Sciences Division, 2020 (in press)

S.A. Glantz, Primer on Bio-Statistics, McGraw-Hill. 1997

Microsfot Excel Tutorial, Microsoft

**Statistica medica**

**Course syllabus**

Reliability of a measure

∙ Reliability and its components

∙ Systematic error and casual error

Variability

Between-subjects and within-subjects variability

Descriptive statistics

∙ Graphs

∙ Location, scale, and shape of a frequency distribution

∙ Measures of location and scale

∙ Accuracy and precision of a measure

∙ Quantiles and reference limits

∙ Correlation and Kappa statistic

Gaussian model

∙ Probability of events on the population within the Gaussian model

∙ How to model the error with a Gaussian model

Screening programs

∙ Events

∙ Probability: concept

∙ Probability of an event: algebra

∙ Basics of probability

∙ Screening programs: why

∙ True and false positives, true and false negatives

∙ Sensitivity and specificity of a diagnostic tool

∙ Positive (negative) predictive values

∙ Likelihood ratio: positive and negative

∙ Pre-test and post-test probabilities

∙ Bayes theorem

Inference

∙ Sampling variability

∙ Population and sample

∙ Estimate of a population parameter with sampling quantities

Sampling distribution

∙ The central limit theorem and the distribution of a sampling quantity

∙ Standard error

Confidence interval

∙ Definition and meaning

∙ Formulas

Hypothesis testing

∙ First type and second type errors and power of a test

∙ Sample size calculation

∙ Clinical statistics and clinical relevance

∙ Hypothesis testing on a population mean

Deterministic and probabilistic models

∙ Deterministic and probabilistic models: differences

∙ Simple linear regression model: interpretation and parameters

∙ Hypothesis testing on the parameters of a simple linear regression model

∙ Reliability and its components

∙ Systematic error and casual error

Variability

Between-subjects and within-subjects variability

Descriptive statistics

∙ Graphs

∙ Location, scale, and shape of a frequency distribution

∙ Measures of location and scale

∙ Accuracy and precision of a measure

∙ Quantiles and reference limits

∙ Correlation and Kappa statistic

Gaussian model

∙ Probability of events on the population within the Gaussian model

∙ How to model the error with a Gaussian model

Screening programs

∙ Events

∙ Probability: concept

∙ Probability of an event: algebra

∙ Basics of probability

∙ Screening programs: why

∙ True and false positives, true and false negatives

∙ Sensitivity and specificity of a diagnostic tool

∙ Positive (negative) predictive values

∙ Likelihood ratio: positive and negative

∙ Pre-test and post-test probabilities

∙ Bayes theorem

Inference

∙ Sampling variability

∙ Population and sample

∙ Estimate of a population parameter with sampling quantities

Sampling distribution

∙ The central limit theorem and the distribution of a sampling quantity

∙ Standard error

Confidence interval

∙ Definition and meaning

∙ Formulas

Hypothesis testing

∙ First type and second type errors and power of a test

∙ Sample size calculation

∙ Clinical statistics and clinical relevance

∙ Hypothesis testing on a population mean

Deterministic and probabilistic models

∙ Deterministic and probabilistic models: differences

∙ Simple linear regression model: interpretation and parameters

∙ Hypothesis testing on the parameters of a simple linear regression model

**Teaching methods**

Teaching through slides and blackboard use with a problem-solving approach; practical sessions of exercises; slides and exercises available from Ariel for additional homeworks.

The lecturers and practical sessions have done in a classroom using computers using the Excel program (Microsoft). Each lesson has the structure of a practical training session: in each lesson one or more problems related to the various topics of the course are solved on the computer. Each practical training session is preceded by the projection of PowerPoint slides related to the theoretical topic of the session.

The lecturers and practical sessions have done in a classroom using computers using the Excel program (Microsoft). Each lesson has the structure of a practical training session: in each lesson one or more problems related to the various topics of the course are solved on the computer. Each practical training session is preceded by the projection of PowerPoint slides related to the theoretical topic of the session.

**Teaching Resources**

Pagano - Gauvreau "Principles of Biostatistics", Chapman and Hall/CRC

Bland "An Introduction to Medical Statistics" (English Edition) 4th Edition, Oxford University Press

Elmore - Wild - Nelson - Katz "Jekel's Epidemiology, Biostatistics, Preventive Medicine, and Public Health" Elsevier - Health Sciences Division, 2020 (in press)

S.A. Glantz, Primer on Bio-Statistics, McGraw-Hill. 1997

Microsfot Excel Tutorial, Microsoft

Bland "An Introduction to Medical Statistics" (English Edition) 4th Edition, Oxford University Press

Elmore - Wild - Nelson - Katz "Jekel's Epidemiology, Biostatistics, Preventive Medicine, and Public Health" Elsevier - Health Sciences Division, 2020 (in press)

S.A. Glantz, Primer on Bio-Statistics, McGraw-Hill. 1997

Microsfot Excel Tutorial, Microsoft

Bioingegneria elettronica ed informatica

ING-INF/06 - ELECTRONIC AND INFORMATICS BIOENGINEERING - University credits: 2

Lessons: 16 hours

Professor:
Porta Alberto

Sistemi di elaborazione delle informazioni

ING-INF/05 - INFORMATION PROCESSING SYSTEMS - University credits: 1

Lessons: 8 hours

Professor:
Porta Alberto

Statistica medica

MED/01 - MEDICAL STATISTICS - University credits: 2

Lessons: 16 hours

Professor:
Edefonti Valeria Carla

Professor(s)

Reception:

For meetings, please write an email.

Campus Cascina Rosa, via A. Vanzetti, 5, 20133 Milano - room number 3 - 4

Reception:

by appointment to be agreed via e-mail

San Donato Milanese - via R. Morandi 30