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.
Course Content - Last names D-L
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.
Course Content - Last names M-P
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.
Course Content - Last names Q-Z
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.
P. Newbold, W.L. Carlson, B. Thorne. Statistica. Second edition. 2014. Pearson/Prentice Hall.
Learning Objectives - Last names A-C
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.
Learning Objectives - Last names D-L
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.
Learning Objectives - Last names M-P
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.
Learning Objectives - Last names Q-Z
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.
Prerequisites - Last names A-C
None
Prerequisites - Last names D-L
None.
Prerequisites - Last names M-P
None
Prerequisites - Last names Q-Z
None.
Teaching Methods - Last names A-C
Frontal lessons
Teaching Methods - Last names D-L
Frontal lessons.
Teaching Methods - Last names M-P
Frontal lessons
Teaching Methods - Last names Q-Z
Frontal lessons.
Further information - Last names A-C
Moodle e-learning platform: http://e-l.unifi.it/
Further information - Last names D-L
Teaching material available on the e-learning platform of the university: http://e-l.unifi.it/
Further information - Last names M-P
Moodle e-learning platform: http://e-l.unifi.it/
Further information - Last names Q-Z
Teaching material available on the e-learning platform of the university: http://e-l.unifi.it/
Type of Assessment - Last names A-C
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.
Type of Assessment - Last names D-L
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.
Type of Assessment - Last names M-P
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.
Type of Assessment - Last names Q-Z
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.
Course program - Last names A-C
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.
Course program - Last names D-L
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.
Course program - Last names M-P
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.
Course program - Last names Q-Z
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.