2. Descriptive statistics and exploratory data analysis. |
- One-dimensional case: frequency distribution. Measures of location (mean, median, mode and quantiles), dispersion (range, interquartile range, standard deviation and variance) and shape (skewness).
- Two-dimensional case: double-entry frequency tables. Correlation. Measures of centralization and dispersion by subgroups.
- One-dimensional and two-dimensional graphical representations.
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3. Introduction to probability theory, random variables and main probability distributions. |
Basic concepts: sample space, events and elementary events, basic rules of probability, main probability theorems, conditional probability and independence, probability distribution.
Probability mass function. Distribution and density function.
Main discrete probability distributions: binomial, multinomial, Poisson.
Main continuous probability distributions: normal, exponential.
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4. Introduction to statistical inference. |
Point estimation: properties of estimators.
Confidence intervals: construction.
Hypothesis testing: main concepts. Types of error. Critical level or p-value.
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6. Regression and correlation |
Simple linear regression model. The regression line. Goodness of fit and residual analysis. Hypothesis tests for the simple linear regression model
Non-linear regression: logarithmic and exponential models.
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