Module 1: Types of Statistical Studies and Producing Data
Module 1: Types of Statistical Studies and Producing Data
Module 2: Summarizing Data Graphically and Numerically
Module 2: Summarizing Data Graphically and Numerically
Module 2: Summarizing Data Graphically and Numerically
Module 3: Examining Relationships: Quantitative Data
Module 3: Examining Relationships: Quantitative Data
Module 3: Examining Relationships: Quantitative Data
Module 3: Examining Relationships: Quantitative Data
Module 3: Examining Relationships: Quantitative Data
Module 3: Examining Relationships: Quantitative Data
Module 3: Examining Relationships: Quantitative Data
Module 3: Examining Relationships: Quantitative Data
Module 3: Examining Relationships: Quantitative Data
Module 3: Examining Relationships: Quantitative Data
Module 3: Examining Relationships: Quantitative Data
Module 4: Nonlinear Models
Module 5: Relationships in Categorical Data with Intro to Probability
Module 5: Relationships in Categorical Data with Intro to Probability
Module 5: Relationships in Categorical Data with Intro to Probability
Module 5: Relationships in Categorical Data with Intro to Probability
Module 6: Probability and Probability Distributions
Module 6: Probability and Probability Distributions
Module 6: Probability and Probability Distributions
Module 6: Probability and Probability Distributions
Module 6: Probability and Probability Distributions
Module 6: Probability and Probability Distributions
Module 6: Probability and Probability Distributions
Module 6: Probability and Probability Distributions
Module 6: Probability and Probability Distributions
Module 6: Probability and Probability Distributions
Module 6: Probability and Probability Distributions
Module 6: Probability and Probability Distributions
Module 6: Probability and Probability Distributions
Module 7: Linking Probability to Statistical Inference
Module 7: Linking Probability to Statistical Inference
Module 7: Linking Probability to Statistical Inference
Module 7: Linking Probability to Statistical Inference
Module 7: Linking Probability to Statistical Inference
Module 7: Linking Probability to Statistical Inference
Module 8: Inference for One Proportion
Module 8: Inference for One Proportion
Module 8: Inference for One Proportion
Module 8: Inference for One Proportion
Module 8: Inference for One Proportion
Module 8: Inference for One Proportion
Module 8: Inference for One Proportion
Module 8: Inference for One Proportion
Module 9: Inference for Two Proportions
Module 9: Inference for Two Proportions
Module 9: Inference for Two Proportions
Module 9: Inference for Two Proportions
Module 9: Inference for Two Proportions
Module 9: Inference for Two Proportions
Module 9: Inference for Two Proportions
Module 9: Inference for Two Proportions
Module 10: Inference for Means
Module 10: Inference for Means
Module 10: Inference for Means
Module 10: Inference for Means
Module 10: Inference for Means
Module 10: Inference for Means
Module 10: Inference for Means
Module 10: Inference for Means
Module 10: Inference for Means
Module 10: Inference for Means
Module 10: Inference for Means
Module 10: Inference for Means
Module 10: Inference for Means
Module 11: Chi-Square Tests
Module 11: Chi-Square Tests
Module 11: Chi-Square Tests
Resources: Course Assignments
Resources: Course Assignments
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Module 6: Probability and Probability Distributions
Module 6: Probability and Probability Distributions
Introduction to Continuous Probability Distribution
Introduction to Continuous Probability Distribution
What you’ll learn to do: Use a probability distribution for a continuous random variable to estimate probabilities and identify unusual events.
In the last section, we studied discrete (listable) random variables and their distributions. Now we explore continuous (decimal valued) random variables that can take on values anywhere in an interval. For example, a person’s exact weight without rounding is a continuous random variable. If rounded to the nearest pound, weight is a discrete random variable. Decimal valued numbers arise often in real life, often in measuring things such as weight or length. To best study real life data that has values lying all over an interval, we need to build a solid foundation in continuous probability distributions.
- Concepts in Statistics. Provided by: Open Learning Initiative. Located at: http://oli.cmu.edu. License: CC BY: Attribution