What are you going to learn?
Content
Recognize important differences between descriptive and inferential statistics; distinguish between different types of variables and data; summarize, organize, tabulate and graph statistical data; read and understand statistical data present in various forms of the media; find and analyze measures of center and variation for quantitative data. Apply normal distributions in solving real-world problems involving percentages and percentiles; understand the importance of sampling distributions and the Central Limit Theorem. Determine if a correlation exists between two variables and interpret the strength of the correlation; use regression equations in order to predict the value of one variable given the value of another. Apply the basic concepts of probability theory.
Chapter 1. Statistics and the Scientific Method
Introduction
Why to Study Statistics?
Introduction
Observational Studies, Sampling Design
Experimental Studies, Design of Experiments
Elements of Statistics
Chapter 2. Data Collection
Chapter 3. Data Description
Introduction
Graphical Methods
Measures of Central Tendency
Measures of Variability
Tthe Boxplot
Sample Statistics and Population Parameters
Software Systems
Chapter 4. Probability Distributions
Introduction
Discrete and Continuous Probability Distributions
The Normal Probabbility Dstribution
Sampling Distribution of Sample Means
Chapter 5. Statistical Inference: Estimation
Point and Interval Estimation
Confidence Interval for a Proportion
Confidence Interval for a Mean
Choice of Sample Size
Confidence Intervals for Median and Other Parameters
Chapter 6. Statistical Inference: Significance Tests
The Five Parts of Significance Test
Significance Test for a Mean
Significance Test for a Proportion
Decisions and Types of Errors in Tests
Limitations of Significance Tests
Calculating P (Type II Error)
Small-Sample Test for a Proportion - The Binomial Distribution
Chapter 7. Comparison of Two Groups
Introduction
Categorical Data: Comparing Two Proportions
Quatitative Data: Comparing Two Means
Conditional Dstribution Fuctions
Chapter 8. Linear Regression and Correlation
Linear Relationships
Least Squares Prediction Equation
The Linear Regression Model
Measuring Linear Association: The Correlation
Inferences for the Sloope and Correlation
Model Assumptions and Violations
Chapter 9. Comparing Groups: Analysis of Variance (ANOVA)
Compairing Several Means: The Analysis of Variance F Test
Multiple Comparisons of Means
Performing ANOVA by Regression Modeling
Two Way ANOVA
Two Way ANOVA and Regression
Chapter 10. Simulation
Introduction
General Techniques
Simulating Discrete Distributions
Variance Reduction Techniques
Bibliography
Agresti, A. Kateri, M. Foundations of Statistics for Data Scientists with R and Python. CRC Press, Boca Raton, 2021.
Agresti, A. Franklin, C. Statistics: The Art and Science of Learnng from Data. Pearson, Essex, 2018.
Agresti, A. Finlay, B. Statistical Methods for Social Sciences. 4th ed., Pearson, New York, 2009.
Lyman, O. Longnecker, M. An Introduction to Statistical Methods & Data Analysis. 7th ed., Cengage Learning, Boston, 2016.
