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.

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