Nguồn ngoài
Inferential Statistics and Probability - A Holistic Approach
Holistic or Eastern tradition analysis is less concerned with the component parts of a problem, mechanism or phenomenon but rather with how this system operates as a whole, including its surrounding environment. I believe it is possible to teach the concept of Inferential Statistics holistically. This course material is a result of that inspiration, and it was designed to supplement, not replace, a traditional course textbook or workbook.
- Tác giả
- Maurice A. Geraghty
- Nhà xuất bản
- De Anza College
- Nguồn
- LibreTexts
- Giấy phép
- CC BY-SA
- Ngôn ngữ
- EN
- Danh mục
- Khoa học cơ bản
Mục lục· 135 mục
- 01: Introduction
- 1.01: A Classroom Story and an Inspiration
- 1.02: The Blind Man and the Elephant
- 1.03: What can go Wrong in Research Two Stories
- 02: Displaying and Analyzing Data with Graphs
- 2.01: Introduction and Examples
- 2.02: Types of Data
- 2.03: Levels of Data
- 2.04: Graphs of Categorical Data
- 2.05: Graphs of Numeric Data
- 2.5.01: Stem and Leaf Plots
- 2.5.02: Dot Plots
- 2.5.03: Grouping Numeric Data
- 2.5.04: Histograms
- 2.5.05: Cumulative Frequency and Relative Frequency
- 2.5.06: Using Ogives to find Percentiles
- 03: Descriptive Statistics
- 3.01: Measures of Central Tendency
- 3.02: Measures of Variability
- 3.03: Measures of Relative Standing
- 3.04: Box Plots (Box and Whisker Plot)
- 3.05: Working with Outliers
- 3.06: Bivariate Data
- 3.6.01: Graphing Bivariate Data with Scatterplots
- 3.6.02: Correlation Coefficient
- 3.6.03: Correlation vs. Causation
- 04: . Populations and Sampling
- 4.01: Populations and Samples
- 4.02: The Statistical Process
- 4.03: Types of Studies
- 4.04: Sampling Techniques
- 4.05: Bias in Statistical Studies
- 05: Probability
- 5.01: What is Probability
- 5.02: Types of Probability
- 5.03: How to Calculate Classical Probability
- 5.04: Rule of Complement
- 5.05: Joint Probability and Additive Rule
- 5.06: Conditional Probability
- 5.07: Contingency (Twoway) Tables
- 5.08: Multiplicative Rule and Tree Diagrams
- 5.09: Independence
- 5.10: Changing the Conditionality and Bayesian Statistics
- 06: Discrete Random Variables
- 6.01: What is a Random Variable
- 6.02: What is a Discrete Random Variable
- 6.03: Probability Distribution Function (PDF) for Discrete Random Variables
- 6.04: Expected Value and Variance of a Discrete Probability Distribution Function
- 6.05: Bernoulli Distribution
- 6.06: Binomial Distribution
- 6.07: Geometric Distribution
- 6.08: Poisson Distribution
- 07: Continuous Random Variables
- 7.01: What is a Continuous Random Variable
- 7.02: Exponential Distribution
- 7.03: Uniform Distribution
- 7.04: Normal Distribution
- 08: The Central Limit Theorem
- 8.01: The Central Limit Theorem for Sample Means
- 8.02: The Central Limit Theorem for Sample Proportions
- 09: Point Estimation and Confidence Intervals
- 9.01: Inferential Statistics
- 9.02: Point Estimation
- 9.03: Confidence Intervals
- 10: One Population Hypothesis Testing
- 10.01: Procedures of Hypotheses Testing and the Scientific Method
- 10.02: Formulate General Research Questions
- 10.03: Design Research Hypotheses and Experiment
- 10.04: Collect and Analyze Experimental Data
- 10.05: Report Conclusions in Nonstatistical Language
- 10.06: Test of Mean vs. Hypothesized Value A Complete Example
- 10.07: Type II Error and Statistical Power
- 10.08: New Models for One Population Inference Similar Procedures
- 10.09: The pvalue- Misconceptions and Proper Usage
- 11: Two Populations Inference
- 11.01: Independent vs. Dependent Sampling
- 11.02: Independent Sampling Models
- 11.03: Dependent Sampling Matched Pairs ttest
- 11.04: Independent Sampling Comparing Two Population Variances or Standard Deviations
- 11.05: Comparing Two Proportions
- 12: Chisquare Tests for Categorical Data
- 12.01: Chisquare Goodnessoffit Test
- 12.02: Chisquare Test of Independence
- 13: One Factor Analysis of Variance (ANOVA)
- 13.01: Comparing means from more than two Independent Populations
- 13.02: The Logic of ANOVA How Comparing Variances Test for a Difference in Means.
- 13.03: The One Factor ANOVA Model
- 13.04: Factorial Design an Insight to other ANOVA Procedures
- 13.05: Understanding the ANOVA Table
- 13.06: Posthoc Analysis Tukeys Honestly Significant Difference (HSD) Test85
- 14: Correlation and Linear Regression
- 14.01: Bivariate Data and Scatterplots Review
- 14.02: The Simple Linear Regression Model
- 14.03: Estimating the Regression Model with the LeastSquare Line
- 14.04: Hypothesis Test for Simple Linear Regression
- 14.05: Estimating ( sigma) the standard error of the residuals
- 14.06: ( r2) The Correlation of Determination
- 14.07: Prediction
- 14.08: Extrapolation
- 14.09: Residual Analysis
- 15: Appendix
- 15.01: Glossary of Statistical Terms used in Inference
- 15.02: Homework Problems
- 15.2.01: Chapter 2 Homework
- 15.2.02: Chapter 3 Homework
- 15.2.03: Chapter 4 Homework
- 15.2.04: Chapter 5 Homework
- 15.2.05: Chapter 6 Homework
- 15.2.06: Chapter 7 Homework
- 15.2.07: Chapter 8 Homework
- 15.2.08: Chapter 9 Homework
- 15.2.09: Chapter 10 Homework
- 15.2.10: Chapter 11 Homework
- 15.2.11: Chapter 12 Homework
- 15.2.12: Chapter 13 Homework
- 15.2.13: Chapter 14 Homework
- 15.03: MINITAB Labs
- 15.3.01: Chapter 2 Lab
- 15.3.02: Chapter 3 Lab
- 15.3.03: Chapter 4 Lab
- 15.3.04: Chapter 5 Lab
- 15.3.05: Chapter 6 Lab
- 15.3.06: Chapter 7 Lab
- 15.3.07: Chapter 8 Lab
- 15.3.08: Chapter 9 Lab
- 15.3.09: Chapter 10 Lab
- 15.3.10: Chapter 11 Lab
- 15.3.11: Chapter 12 Lab
- 15.3.12: Chapter 13 Lab
- 15.3.13: Chapter 14 Lab
- 15.04: Flash Animations
- 15.05: PowerPoint Slides
- 15.06: Notes and Sources
- 15.07: Why Creative Commons Attribution License
- 15.08: Acknowledgments