Università Roma Tre

Teaching program
The course aims to provide the student with the basic knowledge (cultural skills) and the statistical, mathematical and computer tools (methodological skills) needed to:
- conduct an experiment,
- process, process and synthesize experimental data,
- assess the uncertainty and confidence intervals of the experimental results,
- evaluate the statistical parameters of a population starting from a sample (inference),
- quantitatively compare data and models through hypothesis rejection tests.

Detailed plan
1. Tools of a spreadsheet (EXCEL, CALC) for the synthesis and presentation of experimental data and statistical calculation.
a. table construction,
b. functions, functions in matrix form,
c. histograms and bar charts,
d. scatter charts.
2. Statistical summary:
to. the main statistical indexes for a set of data also in aggregate form,
b. frequency distributions for a data set,
c. frequency histograms,
d. present the relationship between quantities using scatter plots,
is. evaluate the linear relationship between quantities by evaluating the correlation and / or the parameters of the linear regression.
3. Calculation of probabilities:
to. definitions of probability of an event and symbolism for calculating probabilities,
b. dependent / independent, compatible / incompatible events
c. probability of events: and, or and conditional probabilities,
d. Bayes theorem,
is. model probability distributions (binomial, uniform, Gauss).
4. Evaluation of experimental uncertainties
to. the uncertainty of experimental quantities (data, histogram frequencies, etc.)
b. uncertainties on regression line parameters,
c. uncertainty about linear correlation
d. evaluate the measurement uncertainties on derived quantities
5. Statistical inference
to. Calculate and interpret the confidence intervals for: mean values, distributions, linear correlation, linear regression parameters,
b. hypothesis rejection test (Z, T, and chi ^ 2),
c. test chi ^ 2 for contingency tables or distributions,
d. Calculate a p-value for a hypothesis test,
is. Construct and / or correctly interpret a screening test (Bayes theorem)

M. C. WHITLOCK, D. SCHLUTER - ANALISI STATISTICA DEI DATI BIOLOGICI, ZANICHELLI.
Notes and exercises are available at the web site of the course:
https://sites.google.com/a/personale.uniroma3.it/meneghini-did/statistica

Teaching program
The course aims to provide the student with the basic knowledge (cultural skills) and the statistical, mathematical and computer tools (methodological skills) needed to:
- conduct an experiment,
- process, process and synthesize experimental data,
- assess the uncertainty and confidence intervals of the experimental results,
- evaluate the statistical parameters of a population starting from a sample (inference),
- quantitatively compare data and models through hypothesis rejection tests.

Detailed plan
1. Tools of a spreadsheet (EXCEL, CALC) for the synthesis and presentation of experimental data and statistical calculation.
a. table construction,
b. functions, functions in matrix form,
c. histograms and bar charts,
d. scatter charts.
2. Statistical summary:
to. the main statistical indexes for a set of data also in aggregate form,
b. frequency distributions for a data set,
c. frequency histograms,
d. present the relationship between quantities using scatter plots,
is. evaluate the linear relationship between quantities by evaluating the correlation and / or the parameters of the linear regression.
3. Calculation of probabilities:
to. definitions of probability of an event and symbolism for calculating probabilities,
b. dependent / independent, compatible / incompatible events
c. probability of events: and, or and conditional probabilities,
d. Bayes theorem,
is. model probability distributions (binomial, uniform, Gauss).
4. Evaluation of experimental uncertainties
to. the uncertainty of experimental quantities (data, histogram frequencies, etc.)
b. uncertainties on regression line parameters,
c. uncertainty about linear correlation
d. evaluate the measurement uncertainties on derived quantities
5. Statistical inference
to. Calculate and interpret the confidence intervals for: mean values, distributions, linear correlation, linear regression parameters,
b. hypothesis rejection test (Z, T, and chi ^ 2),
c. test chi ^ 2 for contingency tables or distributions,
d. Calculate a p-value for a hypothesis test,
is. Construct and / or correctly interpret a screening test (Bayes theorem)

M. C. WHITLOCK, D. SCHLUTER - ANALISI STATISTICA DEI DATI BIOLOGICI, ZANICHELLI.
Notes and exercises are available at the web site of the course:
https://sites.google.com/a/personale.uniroma3.it/meneghini-did/statistica