STATISTICS A - L

1. Knowledge and understanding: The course provides basic concepts in statistics (summary statistics, probability calculus, statistical inference, linear statistical modelling). These essential tools of statistics theory are applied for data analysis in business and economics.
2. Applying knowledge and understanding: The student has to be able to perform statistical analyses of data in business and economics, using both descriptive and inferential statistical tools, as well as linear regression models.
3. Making judgements: The student has to be able to select the appropriate statistical tools to analyse data and draw conclusions based on the results of suitable statistical analyses.
4. Communication skills: The student is expected to learn the technical language needed to understand/write properly a statistical report in the area of economics and business.
5. Learning skills: Ability to understand the logic of the statistical reasoning

Statistics, Data, and Statistical Thinking. The Science of Statistics. Types of Statistical Applications in Business. Fundamental Elements of Statistics. Types of Data. Collecting Data: Sampling and Related Issues. Critical Thinking with Statistics. Using Technology: Accessing and Listing Data; Random Sampling. Methods for Describing Sets of Data. Describing Qualitative Data. Graphical Methods for Describing Quantitative Data. Numerical Measures of Central Tendency. Numerical Measures of Variability. Using the Mean and Standard Deviation to Describe Data. Numerical Measures of Relative Standing. Graphing Bivariate Relationships.

Probability. Events, Sample Spaces, and Probability. Unions and Intersections. Complementary Events. The Additive Rule and Mutually Exclusive Events. Conditional Probability. The Multiplicative Rule and Independent Events. Bayes’s Rule. Combinations and Permutations. Random Variables and Probability Distributions. Two Types of Random Variables. Discrete Random Variables. Probability Distributions for Discrete Random Variables. The Binomial Distribution. Other Discrete Distributions: Poisson and Hypergeometric.

Continuous Random Variables. Probability Distributions for Continuous Random Variables. The Normal Distribution. Descriptive Methods for Assessing Normality. Other Continuous Distributions: Uniform and Exponential.

Sampling Distributions. The Concept of Sampling Distribution. Properties of Sampling Distributions: Unbiasedness and Minimum Variance. The Sampling Distribution of the Sample Mean and the Central Limit Theorem. The Sampling Distribution of the Sample Proportion

Identifying and Estimating the Target Parameter. Confidence Interval for Population Mean: Normal (z) Statistic. Confidence Interval for Population Mean: Student’s t-Statistic. Large-Sample Confidence Interval forPopulation Proportion. Determining the Sample Size. Finite Population Correction for Simple Random Sampling. Confidence Interval for Population Variance. Inferences Based on Single Sample: Estimation with Confidence Intervals

Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses. Identifying the Target Parameter. Comparing Two Population Means: Independent Sampling. Comparing Two Population Means: Paired Difference Experiments. Comparing Two Population Proportions: Independent Sampling. Determining the Required Sample Size. Comparing Two Population Variances: Independent Sampling

Inferences Based on Single Sample: Tests of Hypotheses. The Elements ofTest of Hypothesis. Formulating Hypotheses and Setting Up the Rejection Region. Observed Significance Levels: p-Values. Test of Hypothesis about Population Mean: Normal (z) Statistic. Test of Hypothesis aboutPopulation Mean: Student’s t-Statistic. Large-Sample Test of Hypothesis aboutPopulation Proportion. Test of Hypothesis aboutPopulation Variance. Calculating Type II Error Probabilities: More about

Point estimation. Properties of estimators. Methods of estimation: substitution principles, method of least squares, maximum likelihood estimates. Confidence estimation. Confidence level. Confidence bounds for means, variances, proportions.

Hypothesis testing. Null hypotheses and alternative hypotheses. Test rules. Significance level. Power of a test. Statistical tests for means, variances, proportions; comparison of means, variances, proportions.

Categorical Data Analysis. Categorical Data and the Multinomial Experiment. Testing Category Probabilities: One-Way Table. Testing Category Probabilities: Two-Way (Contingency) Table

In Italian: Paul Newbold, William L. Carlson, Betty Thorne, Statistica 2/Ed., Pearson, 2010.

or in English: Paul Newbold, William L. Carlson, Betty Thorne, Statistics for Business and Economics, Prentice Hall, 2009