ITA | ENG

References

Teaching Language

Course Content

Suggested readings

Learning Objectives

Prerequisites

Teaching Methods

Further information

Type of Assessment

Course program

Belonging Department

Scienze per l'Economia e l'Impresa

Course Type

Attività formativa monodisciplinare

Scientific Area

SECS-S/01 - STATISTICA

Course year

First year - Second Term

Teaching Term

dal 25/02/2019 al 31/05/2019

Attendance required

No

Credits

9

Type of Evaluation

Voto Finale

Teaching Hours

72

Course Content

Course program

Lectureship

- Last name/s A-C MARCHETTI GIOVANNI MARIA
- Last name/s D-L DREASSI EMANUELA
- Last name/s D-L LUPPARELLI MONIA
- Last name/s M-P BOCCI CHIARA
- Last name/s M-P RAMPICHINI CARLA
- Last name/s Q-Z BARNABANI MARCO

Italian

Italian.

Italian

Italian.

Descriptive statistics: data collection and description. Measures of central tendency and measures of variability or dispersion. Elements of probability, random variables. Introduction to point estimation and interval estimation. Introduction to hypothesis testing.

Descriptive statstics: data collection and description. Measures of central tendency and measures of variability or dispersion. Elements of probability, random variables. Introduction to point estimation and interval estimation. Introduction to hypothesis testing.

Descriptive statistics: data collection and description. Measures of central tendency and measures of variability or dispersion. Elements of probability, random variables. Introduction to point estimation and interval estimation. Introduction to hypothesis testing.

Elements of descriptive statistics. Elements of probability and random variables. Introduction to point estimation theory and confidence intervals. Introduction to the theory of hypothesis testing.

P. Newbold, W.L. Carlson, B. Thorne. Statistica. Second edition. 2014. Pearson / Prentice Hall.

P. Newbold, W.L. Carlson, B. Thorne. Statistica. Second edition. 2014. Pearson/Prentice Hall.

P. Newbold, W.L. Carlson, B. Thorne. Statistica. Second edition. 2014. Pearson / Prentice Hall.

P. Newbold, W.L. Carlson, B. Thorne. Statistica. 2007, Pearson / Prentice Hall.

At the end of this course, in terms of knowledge and understanding students should be able to: have knowledge about basic concepts in statistic; have knowledge of statistical methods for collection, processing and analysis of quantitative data particularly such linked to the field of Economy and Business.

In terms of ability to apply knowledge and understanding, students should be able to: solve decision problems in the economic and business frameworks; assess the role of randomness and variability in different contexts; adopt the most appropriate technique for data analysis; interpret the results and communicate them with the proper language of the discipline; communicate knowledge of statistical ideas effectively.

In terms of ability to apply knowledge and understanding, students should be able to: solve decision problems in the economic and business frameworks; assess the role of randomness and variability in different contexts; adopt the most appropriate technique for data analysis; interpret the results and communicate them with the proper language of the discipline; communicate knowledge of statistical ideas effectively.

At the end of this course, in terms of knowledge and understanding students should be able to:

- have knowledge about basic concepts in statistic;

- have knowledge of statistical methods for collection, processing and analysis of quantitative data particularly such linked to the field of Economy and Business.

In terms of ability to apply knowledge and understanding, students should be able to:

- solve decision problems in the economic and business frameworks;

- assess the role of randomness and variability in different contexts;

- adopt the most appropriate technique for data analysis;

- interpret the results and communicate them with the proper language of the discipline;

- communicate knowledge of statistical ideas effectively.

- have knowledge about basic concepts in statistic;

- have knowledge of statistical methods for collection, processing and analysis of quantitative data particularly such linked to the field of Economy and Business.

In terms of ability to apply knowledge and understanding, students should be able to:

- solve decision problems in the economic and business frameworks;

- assess the role of randomness and variability in different contexts;

- adopt the most appropriate technique for data analysis;

- interpret the results and communicate them with the proper language of the discipline;

- communicate knowledge of statistical ideas effectively.

At the end of this course, in terms of knowledge and understanding students should be able to: have knowledge about basic concepts in statistic; have knowledge of statistical methods for collection, processing and analysis of quantitative data particularly such linked to the field of Economy and Business.

In terms of ability to apply knowledge and understanding, students should be able to: solve decision problems in the economic and business frameworks; assess the role of randomness and variability in different contexts; adopt the most appropriate technique for data analysis; interpret the results and communicate them with the proper language of the discipline; communicate knowledge of statistical ideas effectively.

In terms of ability to apply knowledge and understanding, students should be able to: solve decision problems in the economic and business frameworks; assess the role of randomness and variability in different contexts; adopt the most appropriate technique for data analysis; interpret the results and communicate them with the proper language of the discipline; communicate knowledge of statistical ideas effectively.

At the end of this course, in terms of knowledge and understanding students should be able to: have knowledge about basic concepts in statistic; have knowledge of statistical methods for collection, processing and analysis of quantitative data particularly such linked to the field of Economy and Business.

In terms of ability to apply knowledge and understanding, students should be able to: solve decision problems in the economic and business frameworks; assess the role of randomness and variability in different contexts; adopt the most appropriate technique for data analysis; interpret the results and communicate them with the proper language of the discipline; communicate knowledge of statistical ideas effectively.

In terms of ability to apply knowledge and understanding, students should be able to: solve decision problems in the economic and business frameworks; assess the role of randomness and variability in different contexts; adopt the most appropriate technique for data analysis; interpret the results and communicate them with the proper language of the discipline; communicate knowledge of statistical ideas effectively.

None

None.

None

none.

Frontal lessons

Frontal lessons.

Frontal lessons

Classroom lessons.

Moodle e-learning platform: http://e-l.unifi.it/

Teaching material available on the e-learning platform of the university: http://e-l.unifi.it/

Moodle e-learning platform: http://e-l.unifi.it/

e-learning Moodle

Written and oral examination. The admission to the oral exam requires to pass the written exam. The final grade is obtained as an average of the written and oral examinations.

The written test consists of 20 questions/exercises based on the whole exam program. The oral test verifies student communication skills, their ability to apply methods and their ability to adopt the proper language of the discipline.

The written test consists of 20 questions/exercises based on the whole exam program. The oral test verifies student communication skills, their ability to apply methods and their ability to adopt the proper language of the discipline.

Written and oral examination. The admission to the oral exam requires to pass the written exam. The final grade is obtained as an average of the written and oral examinations.

The written test consists of 20 questions/exercises based on the whole exam program.

The oral test verifies student communication skills, their ability to apply methods and their ability to adopt the proper language of the discipline.

The written test consists of 20 questions/exercises based on the whole exam program.

The oral test verifies student communication skills, their ability to apply methods and their ability to adopt the proper language of the discipline.

Written and oral examination. The admission to the oral exam requires to pass the written exam. The final grade is obtained as an average of the written and oral examinations.

The written test consists of 20 questions/exercises based on the whole exam program. The oral test verifies student communication skills, their ability to apply methods and their ability to adopt the proper language of the discipline.

The written test consists of 20 questions/exercises based on the whole exam program. The oral test verifies student communication skills, their ability to apply methods and their ability to adopt the proper language of the discipline.

The written test consists of 20 questions/exercises based on the whole exam program. The oral test verifies student communication skills, their ability to apply methods and their ability to adopt the proper language of the discipline.

Definitions (population, statistical units, sample, variables, methods). Graphs and tables: classification of variables, frequency tables, graphics, cumulative frequencies, bivariate tables. Measures of central tendency: arithmetic mean, median, mode, geometric mean. Variability: range, interquartile range, quartiles, quantiles, box-plots, variance, standard deviation, coefficient of variation. Relationship between variables: covariance and correlation and linear regression. Random experiments. The probability and its axioms, rules of probability. Bivariate probability, Bayes' theorem. Probability distributions and discrete random variables.

Properties of discrete random variables. Binomial distribution. Joint distribution of two discrete random variables. Continuous random variables. Expected values of continuous random variables. Uniform distribution. Normal distribution. Approximation to the binomial distribution with the normal distribution. Joint distribution of two continuous random variables. Sampling from a population. Distribution of the sample mean and the sample proportion.

Properties of the estimators. Confidence intervals for the mean: variance known, variance unknown. Confidence intervals for proportions (large samples). Determination of the sample size. Hypothesis testing on a single population.

Properties of discrete random variables. Binomial distribution. Joint distribution of two discrete random variables. Continuous random variables. Expected values of continuous random variables. Uniform distribution. Normal distribution. Approximation to the binomial distribution with the normal distribution. Joint distribution of two continuous random variables. Sampling from a population. Distribution of the sample mean and the sample proportion.

Properties of the estimators. Confidence intervals for the mean: variance known, variance unknown. Confidence intervals for proportions (large samples). Determination of the sample size. Hypothesis testing on a single population.

Definitions (population, statistical units, sample, variables, methods). Graphs and tables: classification of variables, frequency tables, graphics, cumulative frequencies, bivariate tables. Measures of central tendency: arithmetic mean, median, mode, geometric mean. Variability: range, interquartile range, quartiles, quantiles, box-plots, variance, standard deviation, coefficient of variation. Relationship between variables: covariance and correlation and linear regression. Random experiments. The probability and its axioms, rules of probability. Bivariate probability, Bayes' theorem. Probability distributions and discrete random variables.

Properties of discrete random variables. Binomial distribution. Joint distribution of two discrete random variables. Continuous random variables. Expected values of continuous random variables. Uniform distribution. Normal distribution. Approximation to the binomial distribution with the normal distribution. Joint distribution of two continuous random variables. Sampling from a population. Distribution of the sample mean and the sample proportion.

Properties of the estimators. Confidence intervals for the mean: variance known, variance unknown. Confidence intervals for proportions (large samples). Determination of the sample size. Hypothesis testing on a single population.

Properties of discrete random variables. Binomial distribution. Joint distribution of two discrete random variables. Continuous random variables. Expected values of continuous random variables. Uniform distribution. Normal distribution. Approximation to the binomial distribution with the normal distribution. Joint distribution of two continuous random variables. Sampling from a population. Distribution of the sample mean and the sample proportion.

Properties of the estimators. Confidence intervals for the mean: variance known, variance unknown. Confidence intervals for proportions (large samples). Determination of the sample size. Hypothesis testing on a single population.

Definitions (population, statistical units, sample, variables, methods). Graphs and tables: classification of variables, frequency tables, graphics, cumulative frequencies, bivariate tables. Measures of central tendency: arithmetic mean, median, mode, geometric mean. Variability: range, interquartile range, quartiles, quantiles, box-plots, variance, standard deviation, coefficient of variation. Relationship between variables: covariance and correlation and linear regression. Random experiments. The probability and its axioms, rules of probability. Bivariate probability, Bayes' theorem. Probability distributions and discrete random variables.

Properties of discrete random variables. Binomial distribution. Joint distribution of two discrete random variables. Continuous random variables. Expected values of continuous random variables. Uniform distribution. Normal distribution. Approximation to the binomial distribution with the normal distribution. Joint distribution of two continuous random variables. Sampling from a population. Distribution of the sample mean and the sample proportion.

Properties of the estimators. Confidence intervals for the mean: variance known, variance unknown. Confidence intervals for proportions (large samples). Determination of the sample size. Hypothesis testing on a single population.

Properties of discrete random variables. Binomial distribution. Joint distribution of two discrete random variables. Continuous random variables. Expected values of continuous random variables. Uniform distribution. Normal distribution. Approximation to the binomial distribution with the normal distribution. Joint distribution of two continuous random variables. Sampling from a population. Distribution of the sample mean and the sample proportion.

Properties of the estimators. Confidence intervals for the mean: variance known, variance unknown. Confidence intervals for proportions (large samples). Determination of the sample size. Hypothesis testing on a single population.

Definitions (population, statistical units, sample, variables, methods). Graphs and tables: classification of variables, frequency tables, graphics, cumulative frequencies, bivariate tables. Measures of central tendency: arithmetic mean, median, mode, geometric mean. Variability: range, interquartile range, quartiles, quantiles, box-plots, variance, standard deviation, coefficient of variation. Relationship between variables: covariance and correlation and linear regression. Random experiments. The probability and its axioms, rules of probability. Bivariate probability, Bayes' theorem. Probability distributions and discrete random variables.

Properties of discrete random variables. Binomial distribution. Hypergeometric distribution. Joint distribution of two discrete random variables. Continuous random variables. Expected values of continuous random variables. Uniform distribution. Normal distribution. Approximation to the binomial distribution with the normal distribution. Joint distribution of two continuous random variables. Sampling from a population. Distribution of the sample mean and the sample proportion.

Properties of the estimators. Confidence intervals for the mean: variance known, variance unknown. Confidence intervals for proportions (large samples). Determination of the sample size. Hypothesis testing on a single population.

Properties of discrete random variables. Binomial distribution. Hypergeometric distribution. Joint distribution of two discrete random variables. Continuous random variables. Expected values of continuous random variables. Uniform distribution. Normal distribution. Approximation to the binomial distribution with the normal distribution. Joint distribution of two continuous random variables. Sampling from a population. Distribution of the sample mean and the sample proportion.

Properties of the estimators. Confidence intervals for the mean: variance known, variance unknown. Confidence intervals for proportions (large samples). Determination of the sample size. Hypothesis testing on a single population.