Introductory Statistics / Alandra Kahl.

Format:

Book

Author/Creator:

Kahl, Alandra, author.

Language:

English

Subjects (All):

Statistics.

Physical Description:

1 online resource (180 pages)

Edition:

First edition.

Place of Publication:

Singapore : Bentham Science Publishers, [2023]

Summary:

This textbook is a primer for students on statistics. It covers basic statistical operations, an introduction to probability, distributions and regression. The book is divided into a series of 10 chapters covering a basic introduction to common topics for beginners. The goal of the book is to provide sufficient understanding of how to organize and summarize datasets through descriptive and inferential statistics for good decision-making. A chapter on ethics also informs readers about best practices for using statistics in research and analysis. Topics covered:1. Introduction to Statistics2. Summarizing and Graphing3. Basic Concepts of Probability4. Discrete Random Variables5. Continuous Random Variables6. Sampling Distributions7. Estimation8. Hypothesis Testing9. Correlation and Regression10. Ethics.

Contents:

Cover

Title

Copyright

End User License Agreement

Contents

Preface

CONSENT FOR PUBLICATION

CONFLICT OF INTEREST

ACKNOWLEDGEMENT

Introduction to Statistics

INTRODUCTION

DATA TYPES

Sample Data

CONCLUSION

Summarizing and Graphing

FREQUENCY DISTRIBUTIONS AND HISTOGRAMS

GRAPHS

Basic Concepts of Probability

SAMPLES EVENTS AND THEIR PROBABILITIES

Sample Spaces

Event

Examples

Example 1

Example 2

Example 3

Example 4

EXPERIMENT

Definition:

Example 5

PROBABILITY

COMPLEMENTS, INTERSECTIONS, AND UNIONS

Complement

Probability Rule for Complements

Intersection of Events

Probability Rule for Mutually Exclusive Events

Example

Union of Events

Additive Rule of Probability

CONDITIONAL PROBABILITY AND INDEPENDENT OCCURRENCES

Conditional Probability

Independent Events

Principles

Discrete Random Variables

Random Variables

Understanding Random Variables

Types of Random Variables

Example of Random Variable

Example:

Examples of Probability Distributions for Discrete Random Variables (DRV)

Example # 1

Example # 2

Example # 3

Variance of Discrete Random Variables

Characteristics and Notations

Binominal Distribution

Understanding Binominal Distribution

Analyzing Binominal Distribution

Criteria for Binominal Distribution

Examples of Binominal Distributions.

Trial 1

Trial 2

Trial 3

Cumulative Binominal Probability

Negative Binominal Distribution

Notations

The Mean of Negative Binominal Distribution

Continuous Random Variables

Probability Distribution of Continuous Random Variable

Properties

Probability Density Functions

Cumulative Distribution Functions

Examples of Probability Distribution of Continuous Random Variable

The Normal Distribution

Understanding Normal Distribution

Kurtosis and Skewness

Central Limit Theorem

Sample Mean

Convergence to Normal Distribution

The Standard Normal Distribution

The Standard Normal Distribution Vs. Normal Distribution

Standardizing Normal Distribution

How to Calculate Z-score

Example of Finding Z -score

To Find Probability using The Normal Standard Distribution

P values and Z-Tests

How to Use Z-Table

Example: Using Z distribution to Find Probability

Areas of Tails of Distribution

Tails of Standard Normal Distribution

Sampling Distributions

THE MEAN AND STANDARD DEVIATION (SD) OF THE SAMPLE MEAN

The Sampling Distribution of the Sample Mean

The Central Limit Theorem

Solution [44]

Normally Distributed Populations

Standard Deviation of x¯ (Standard Error) [44]

Z-Score of the Sample Mean [44]

The Sample Proportion

Sample Proportions in a Small Population:

The Sampling Distribution of the Sample Proportion

Estimation

Construction of Confidence Intervals

Interval Estimate vs. Point Estimate

Intervals of Confidence

Confidence Level.

The Error Margin

Estimator

Interval vs. Point Estimator

Types of Estimators

WHAT IS STANDARD ERROR (SDE)?

Standard Deviation (SD) of Sample Estimates

Standard Error (SE) of Sample Estimates

Margin of Error

How to Calculate the Error Margin

What is the Critical Value and How Do I Find it?

What is a Confidence Interval, and How Does It Work?

Confidence Intervals and How to Interpret Them

Data Requirements for Confidence Interval

What is a Confidence Interval, and How Do I Make One?

Bias and Error

MSE stands for Mean Squared Error

Sample Size and Estimates

Determining the Sample Size is Necessary to Estimate the Population Mean

Large Sample Estimation of a Population Mean

Large Sample 100 (1 - α) % Confidence Interval for a Population Mean

Small Sample Estimation of a Population Mean

Small Sample 100 (1 -α) % Confidence Interval for a Population Mean [53]

Determining Sample Size Required to Estimate Population Proportion (p)

Estimating the Target Parameter: Point Estimation

Maximum Likelihood

Linear Least Squares (LLS)

Estimating the Target Parameter: Interval Estimation

The t Distribution

Estimating a Population Proportion

Using Confidence Intervals to Determine the Population Proportion

The "Plus Four" Confidence Interval

Calculating the Sample Size n

Sample Size Considerations

The Cost of Collecting Samples

Pre-existing knowledge

Variability That is Inherent

Determination of the Sample Size

Proportions of Samples Taken

Hypothesis Testing

Z-Test

T-Test

Correlation and Regression

CORRELATION

REGRESSION

Ethics

ETHICS.

CONCLUSION

References

Subject Index

Back Cover.

Notes:

Includes bibliographical references.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

9789815123135

9815123130

OCLC:

1379465603