**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.

. CRC Press, Boca Raton, 2021.*Foundations of Statistics for Data Scientists with R and Python*Agresti, A. Franklin, C.

. Pearson, Essex, 2018.*Statistics: The Art and Science of Learnng from Data*Agresti, A. Finlay, B.

. 4th ed., Pearson, New York, 2009.*Statistical Methods for Social Sciences*Lyman, O. Longnecker, M.

. 7th ed., Cengage Learning, Boston, 2016.*An Introduction to Statistical Methods & Data Analysis*