Pre-sessionals - Stats 1
Contact
- Name: Domenico Mergoni
- Email: d.mergoni -at- lse.ac.uk
- Work: London School of Economics
Tip
Internet is a great resource. Use it. Some resources I like:
Introduction to Basic Statistics
Introduction to Statistics
- What is Statistics and its Importance
- Define statistics as the study of data collection, analysis, and interpretation.
- Explain the importance of statistics in decision-making and research.
- Example: Discuss how statistics are used in medical research.
- Descriptive vs. Inferential Statistics
- Differentiate between descriptive and inferential statistics.
- Emphasize the role of each in understanding and drawing conclusions from data.
- Exercise: Provide scenarios and determine whether descriptive or inferential statistics would be used.
- Types of Data: Categorical and Numerical
- Explain the distinction between categorical and numerical data.
- Provide examples of each type of data.
- Exercise: Classify given data sets as categorical or numerical.
- What is Statistics and its Importance
Measures of Central Tendency
- Mean, Median, and Mode
- Define and explain the concepts of mean, median, and mode.
- Illustrate how to calculate each measure.
- Example: Calculate mean, median, and mode for a set of exam scores.
- Exercise: Compute mean, median, and mode for different data sets.
- Calculating and Interpreting Each Measure
- Discuss the interpretation of mean, median, and mode in terms of centrality.
- Highlight scenarios where each measure is useful.
- Exercise: Analyze the implications of outliers on measures of central tendency.
- Real-World Examples
- Provide real-world examples where mean, median, and mode are applied.
- Discuss their relevance in various contexts, such as finance or education.
- Exercise: Analyze a dataset from a real-world scenario and calculate central tendency measures.
- Mean, Median, and Mode
Measures of Dispersion
- Range, Variance, and Standard Deviation
- Define and explain the concepts of range, variance, and standard deviation.
- Demonstrate how to calculate each measure.
- Example: Calculate the range, variance, and standard deviation for a data set.
- Exercise: Compute range, variance, and standard deviation for different datasets.
- Interpreting Variability
- Discuss the importance of measures of dispersion in understanding data spread.
- Explain how variability affects the interpretation of central tendency measures.
- Exercise: Compare and contrast datasets with different measures of dispersion.
- Variance and Standard Deviation for Populations vs. Samples
- Explain the difference between calculating variance and standard deviation for populations and samples.
- Discuss when to use the population formula versus the sample formula.
- Example: Calculate the population and sample variance and standard deviation for a dataset.
- Range, Variance, and Standard Deviation
Quantiles and Percentiles
- Definition of Quantiles and Percentiles
- Define quantiles and percentiles as measures of position in a dataset.
- Explain how they divide data into equal parts.
- Exercise: Calculate the quartiles and percentiles for a dataset.
- Calculation and Interpretation
- Discuss how to calculate quantiles and percentiles using order statistics.
- Interpret the meaning of specific quantiles and percentiles.
- Example: Calculate the interquartile range and 75th percentile for a dataset.
- Exercise: Calculate and interpret quantiles and percentiles for various datasets.
- Box Plots and Their Use in Visualizing Quantiles
- Explain how box plots represent the five-number summary and outliers.
- Discuss the components of a box plot (whiskers, box, median, outliers).
- Example: Create a box plot for a dataset and analyze its features.
- Exercise: Construct box plots for given datasets and identify characteristics.
- Definition of Quantiles and Percentiles
Conclusion and Recap
- Summarize the key concepts covered in the lecture.
Q&A Session
Reference: Statistics for Business, 2nd edition. R. A. Stine, D. Foster