Introduction to Statistics - Lecture 1

Instructor

• Professor Jenna Hirsch (Dr. Hirsch)

Lecture Focus

• Introduction to basic statistics and vocabulary.
• Understanding real-world applications of statistics.

Everyday Use of Statistics

• Weather Predictions: Evaluating probabilities (e.g., 40% vs. 70% chance of rain).
• Pregnancy Tests: Understanding effectiveness (99% accuracy) and why multiple tests are used.
• Medication Decisions: Evaluating risk (e.g., 30% chance of side effects).
• Light Bulbs: Understanding average lifespan and cost vs. benefit.

Key Concepts

• Statistics is used in decision-making and understanding real-world data.
• Two main branches of statistics:
  • Descriptive Statistics
  • Inferential Statistics

Descriptive Statistics

• Describes what's happening in a population or data set.
• Patterns and summaries of data.
• Data collection, organization, and summarization.
  • Graphs/Pictures: Visualizing data.
  • Numerical Values: Summarizing data.
• Focuses only on the data studied.

Inferential Statistics

• Makes generalizations about a larger population from a sample.
• Predicts trends and examines relationships between variables.

Data Types

• Qualitative Data (Categorical)
  • Characteristics that cannot be measured numerically, e.g., color, type.
• Quantitative Data (Numerical)
  • Measurable characteristics expressed in numbers.

Quantitative Data Subtypes

• Discrete Data
  • Countable data (e.g., number of students).
• Continuous Data
  • Measurable data that can have an infinite number of values (e.g., height, time).

Examples

• Qualitative vs. Quantitative: Religion (qualitative), Height (quantitative).
• Discrete vs. Continuous: Number of delays (discrete), Fuel gallons (continuous).

Conclusion

• Understanding data and how it is categorized is fundamental to studying statistics.
• The first half of the semester will focus on descriptive statistics, while the second half will cover inferential statistics.

Preparation

• Familiarize with the differences between data types and subtypes.
• Practice identifying whether data is qualitative or quantitative, discrete or continuous.

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End of Lecture Notes