Statistics with R
portes grátis
Statistics with R
A Beginner's Guide
Stinerock, Robert
Sage Publications Ltd
11/2022
448
Dura
Inglês
9781529753530
15 a 20 dias
Descrição não disponível.
Chapter 1: Introduction and R Instructions
Basic Terminology
Data: Qualitative or Quantitative
Data: Cross-Sectional or Longitudinal
Descriptive Statistics
Probability
Statistics: Estimation and Inference
Chapter 2: Descriptive Statistics: Tabular and Graphical Methods
Methods of Summarizing and Displaying Qualitative Data
Methods of Summarizing and Displaying Quantitative Data
Cross Tabulations and Scatter Plots
Chapter 3: Descriptive Statistics: Numerical Methods
Measures of Central Tendency
Measures of Location
Exploratory Data Analysis: The Box Plot Display
Measures of Variability
The z-Score: A Measure of Relative Location
Measures of Association: The Bivariate Case
The Geometric Mean
Chapter 4: Introduction to Probability
Some Important Definitions
Counting Rules
Assigning Probabilities
Events and Probabilities
Probabilities of Unions and Intersections of Events
Conditional Probability
Bayes' Theorem and Events
Chapter 5: Discrete Probability Distributions
The Discrete Uniform Probability Distribution
The Expected Value and Standard Deviation of a Discrete Random Variable
The Binomial Probability Distribution
The Poisson Probability Distribution
The Hypergeometric Probability Distribution
The Hypergeometric Probability Distribution: The General Case
Bayes' Theorem and Discrete Random Variables
Chapter 6: Continuous Probability Distributions
Continuous Uniform Probability Distribution
Normal Probability Distribution
Exponential Probability Distribution
Optional Material: Derivation of the Cumulative Exponential Probability Func- tion
Bayes' Theorem and Continuous Random Variables
Chapter 7: Point Estimation and Sampling Distributions
Populations and Samples
The Simple Random Sample
The Sample Statistic: x, s, and p
The Sampling Distribution of x
The Sampling Distribution of p
Some Other Commonly Used Sampling Methods
Bayes' Theorem: Approximate Bayesian Computation
Chapter 8: Confidence Interval Estimation
Interval Estimate of ? When ? Is Known
Interval Estimate of ? When ? Is Unknown
Sample Size Determination in the Case of ?
Interval Estimate of p
Sample Size Determination in the Case of p
Bayes' Theorem: Confidence Intervals or Credible Intervals
Chapter 9: Hypothesis Tests: Introduction, Basic Concepts, and an Example
Chapter 10: Hypothesis Tests about Means and Proportions: Applications
The Lower-Tail Hypothesis Test about ?: ? Is Known
The Two-Tail Hypothesis Test about ?: ? Is Known
The Upper-Tail Hypothesis Test about ?: ? Is Unknown
The Two-Tail Hypothesis Test about ?: ? is Unknown
Hypothesis Tests about p
Calculating the Probability of a Type II Error: ?
Adjusting the Sample Size to Control the Size of ?
Bayes' Theorem and an Inferential Approach to p
Chapter 11: Comparisons of Means and Proportions
The Difference between ?1 and ?2: Independent Samples
The Difference between ?1 and ?2: Paired Samples
The Difference between p1 and p2: Independent Samples
Bayes' Theorem and the Difference between p1 and p2
Chapter 12: Simple Linear Regression
Simple Linear Regression: The Model
The Estimated Regression Equation
Goodness of Fit: The Coefficient of Determination, r2
The Hypothesis Test about ?1
Alternative Approaches to Testing Significance
So Far, We Have Tested Only b1. Will We Also Test b0?
Assumptions: What Are They?
Assumptions: How Are They Validated?
Optional Material: Derivation of the Expressions for the Least-Squares Estimates of ?0 and ?1
Bayes' Theorem: Using Stan to Estimate the Relationship between Two Variables
Chapter 13: Multiple Regression
Simple Linear Regression: A Reprise
Multiple Regression: The Model
Multiple Regression: The Multiple Regression Equation
The Estimated Multiple Regression Equation
Multiple Regression: The 2 Independent Variable Case
Assumptions: What Are They? Can We Validate Them?
Tests of Significance: The Overall Regression Model
Tests of Signicance: The Independent Variables
There Must Be An Easier Way Than This, Right?
Using the Estimated Regression Equation for Prediction
Independent Variable Selection: The Best-Subsets Method
Logistic Regression: The Zero-One Dependent Variable
Bayes' Theorem: Stan and Multiple Regression Analysis
Basic Terminology
Data: Qualitative or Quantitative
Data: Cross-Sectional or Longitudinal
Descriptive Statistics
Probability
Statistics: Estimation and Inference
Chapter 2: Descriptive Statistics: Tabular and Graphical Methods
Methods of Summarizing and Displaying Qualitative Data
Methods of Summarizing and Displaying Quantitative Data
Cross Tabulations and Scatter Plots
Chapter 3: Descriptive Statistics: Numerical Methods
Measures of Central Tendency
Measures of Location
Exploratory Data Analysis: The Box Plot Display
Measures of Variability
The z-Score: A Measure of Relative Location
Measures of Association: The Bivariate Case
The Geometric Mean
Chapter 4: Introduction to Probability
Some Important Definitions
Counting Rules
Assigning Probabilities
Events and Probabilities
Probabilities of Unions and Intersections of Events
Conditional Probability
Bayes' Theorem and Events
Chapter 5: Discrete Probability Distributions
The Discrete Uniform Probability Distribution
The Expected Value and Standard Deviation of a Discrete Random Variable
The Binomial Probability Distribution
The Poisson Probability Distribution
The Hypergeometric Probability Distribution
The Hypergeometric Probability Distribution: The General Case
Bayes' Theorem and Discrete Random Variables
Chapter 6: Continuous Probability Distributions
Continuous Uniform Probability Distribution
Normal Probability Distribution
Exponential Probability Distribution
Optional Material: Derivation of the Cumulative Exponential Probability Func- tion
Bayes' Theorem and Continuous Random Variables
Chapter 7: Point Estimation and Sampling Distributions
Populations and Samples
The Simple Random Sample
The Sample Statistic: x, s, and p
The Sampling Distribution of x
The Sampling Distribution of p
Some Other Commonly Used Sampling Methods
Bayes' Theorem: Approximate Bayesian Computation
Chapter 8: Confidence Interval Estimation
Interval Estimate of ? When ? Is Known
Interval Estimate of ? When ? Is Unknown
Sample Size Determination in the Case of ?
Interval Estimate of p
Sample Size Determination in the Case of p
Bayes' Theorem: Confidence Intervals or Credible Intervals
Chapter 9: Hypothesis Tests: Introduction, Basic Concepts, and an Example
Chapter 10: Hypothesis Tests about Means and Proportions: Applications
The Lower-Tail Hypothesis Test about ?: ? Is Known
The Two-Tail Hypothesis Test about ?: ? Is Known
The Upper-Tail Hypothesis Test about ?: ? Is Unknown
The Two-Tail Hypothesis Test about ?: ? is Unknown
Hypothesis Tests about p
Calculating the Probability of a Type II Error: ?
Adjusting the Sample Size to Control the Size of ?
Bayes' Theorem and an Inferential Approach to p
Chapter 11: Comparisons of Means and Proportions
The Difference between ?1 and ?2: Independent Samples
The Difference between ?1 and ?2: Paired Samples
The Difference between p1 and p2: Independent Samples
Bayes' Theorem and the Difference between p1 and p2
Chapter 12: Simple Linear Regression
Simple Linear Regression: The Model
The Estimated Regression Equation
Goodness of Fit: The Coefficient of Determination, r2
The Hypothesis Test about ?1
Alternative Approaches to Testing Significance
So Far, We Have Tested Only b1. Will We Also Test b0?
Assumptions: What Are They?
Assumptions: How Are They Validated?
Optional Material: Derivation of the Expressions for the Least-Squares Estimates of ?0 and ?1
Bayes' Theorem: Using Stan to Estimate the Relationship between Two Variables
Chapter 13: Multiple Regression
Simple Linear Regression: A Reprise
Multiple Regression: The Model
Multiple Regression: The Multiple Regression Equation
The Estimated Multiple Regression Equation
Multiple Regression: The 2 Independent Variable Case
Assumptions: What Are They? Can We Validate Them?
Tests of Significance: The Overall Regression Model
Tests of Signicance: The Independent Variables
There Must Be An Easier Way Than This, Right?
Using the Estimated Regression Equation for Prediction
Independent Variable Selection: The Best-Subsets Method
Logistic Regression: The Zero-One Dependent Variable
Bayes' Theorem: Stan and Multiple Regression Analysis
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statistics using r;beginner statistics;intro to statistics;intro to r;intro stats;introduction to statistics;using r;statistics for dummies;r basics;statistics for psychology;statistics for health;statistics for nursing;statistics for sociology;business statistics;R software guide;bayesian methods
Chapter 1: Introduction and R Instructions
Basic Terminology
Data: Qualitative or Quantitative
Data: Cross-Sectional or Longitudinal
Descriptive Statistics
Probability
Statistics: Estimation and Inference
Chapter 2: Descriptive Statistics: Tabular and Graphical Methods
Methods of Summarizing and Displaying Qualitative Data
Methods of Summarizing and Displaying Quantitative Data
Cross Tabulations and Scatter Plots
Chapter 3: Descriptive Statistics: Numerical Methods
Measures of Central Tendency
Measures of Location
Exploratory Data Analysis: The Box Plot Display
Measures of Variability
The z-Score: A Measure of Relative Location
Measures of Association: The Bivariate Case
The Geometric Mean
Chapter 4: Introduction to Probability
Some Important Definitions
Counting Rules
Assigning Probabilities
Events and Probabilities
Probabilities of Unions and Intersections of Events
Conditional Probability
Bayes' Theorem and Events
Chapter 5: Discrete Probability Distributions
The Discrete Uniform Probability Distribution
The Expected Value and Standard Deviation of a Discrete Random Variable
The Binomial Probability Distribution
The Poisson Probability Distribution
The Hypergeometric Probability Distribution
The Hypergeometric Probability Distribution: The General Case
Bayes' Theorem and Discrete Random Variables
Chapter 6: Continuous Probability Distributions
Continuous Uniform Probability Distribution
Normal Probability Distribution
Exponential Probability Distribution
Optional Material: Derivation of the Cumulative Exponential Probability Func- tion
Bayes' Theorem and Continuous Random Variables
Chapter 7: Point Estimation and Sampling Distributions
Populations and Samples
The Simple Random Sample
The Sample Statistic: x, s, and p
The Sampling Distribution of x
The Sampling Distribution of p
Some Other Commonly Used Sampling Methods
Bayes' Theorem: Approximate Bayesian Computation
Chapter 8: Confidence Interval Estimation
Interval Estimate of ? When ? Is Known
Interval Estimate of ? When ? Is Unknown
Sample Size Determination in the Case of ?
Interval Estimate of p
Sample Size Determination in the Case of p
Bayes' Theorem: Confidence Intervals or Credible Intervals
Chapter 9: Hypothesis Tests: Introduction, Basic Concepts, and an Example
Chapter 10: Hypothesis Tests about Means and Proportions: Applications
The Lower-Tail Hypothesis Test about ?: ? Is Known
The Two-Tail Hypothesis Test about ?: ? Is Known
The Upper-Tail Hypothesis Test about ?: ? Is Unknown
The Two-Tail Hypothesis Test about ?: ? is Unknown
Hypothesis Tests about p
Calculating the Probability of a Type II Error: ?
Adjusting the Sample Size to Control the Size of ?
Bayes' Theorem and an Inferential Approach to p
Chapter 11: Comparisons of Means and Proportions
The Difference between ?1 and ?2: Independent Samples
The Difference between ?1 and ?2: Paired Samples
The Difference between p1 and p2: Independent Samples
Bayes' Theorem and the Difference between p1 and p2
Chapter 12: Simple Linear Regression
Simple Linear Regression: The Model
The Estimated Regression Equation
Goodness of Fit: The Coefficient of Determination, r2
The Hypothesis Test about ?1
Alternative Approaches to Testing Significance
So Far, We Have Tested Only b1. Will We Also Test b0?
Assumptions: What Are They?
Assumptions: How Are They Validated?
Optional Material: Derivation of the Expressions for the Least-Squares Estimates of ?0 and ?1
Bayes' Theorem: Using Stan to Estimate the Relationship between Two Variables
Chapter 13: Multiple Regression
Simple Linear Regression: A Reprise
Multiple Regression: The Model
Multiple Regression: The Multiple Regression Equation
The Estimated Multiple Regression Equation
Multiple Regression: The 2 Independent Variable Case
Assumptions: What Are They? Can We Validate Them?
Tests of Significance: The Overall Regression Model
Tests of Signicance: The Independent Variables
There Must Be An Easier Way Than This, Right?
Using the Estimated Regression Equation for Prediction
Independent Variable Selection: The Best-Subsets Method
Logistic Regression: The Zero-One Dependent Variable
Bayes' Theorem: Stan and Multiple Regression Analysis
Basic Terminology
Data: Qualitative or Quantitative
Data: Cross-Sectional or Longitudinal
Descriptive Statistics
Probability
Statistics: Estimation and Inference
Chapter 2: Descriptive Statistics: Tabular and Graphical Methods
Methods of Summarizing and Displaying Qualitative Data
Methods of Summarizing and Displaying Quantitative Data
Cross Tabulations and Scatter Plots
Chapter 3: Descriptive Statistics: Numerical Methods
Measures of Central Tendency
Measures of Location
Exploratory Data Analysis: The Box Plot Display
Measures of Variability
The z-Score: A Measure of Relative Location
Measures of Association: The Bivariate Case
The Geometric Mean
Chapter 4: Introduction to Probability
Some Important Definitions
Counting Rules
Assigning Probabilities
Events and Probabilities
Probabilities of Unions and Intersections of Events
Conditional Probability
Bayes' Theorem and Events
Chapter 5: Discrete Probability Distributions
The Discrete Uniform Probability Distribution
The Expected Value and Standard Deviation of a Discrete Random Variable
The Binomial Probability Distribution
The Poisson Probability Distribution
The Hypergeometric Probability Distribution
The Hypergeometric Probability Distribution: The General Case
Bayes' Theorem and Discrete Random Variables
Chapter 6: Continuous Probability Distributions
Continuous Uniform Probability Distribution
Normal Probability Distribution
Exponential Probability Distribution
Optional Material: Derivation of the Cumulative Exponential Probability Func- tion
Bayes' Theorem and Continuous Random Variables
Chapter 7: Point Estimation and Sampling Distributions
Populations and Samples
The Simple Random Sample
The Sample Statistic: x, s, and p
The Sampling Distribution of x
The Sampling Distribution of p
Some Other Commonly Used Sampling Methods
Bayes' Theorem: Approximate Bayesian Computation
Chapter 8: Confidence Interval Estimation
Interval Estimate of ? When ? Is Known
Interval Estimate of ? When ? Is Unknown
Sample Size Determination in the Case of ?
Interval Estimate of p
Sample Size Determination in the Case of p
Bayes' Theorem: Confidence Intervals or Credible Intervals
Chapter 9: Hypothesis Tests: Introduction, Basic Concepts, and an Example
Chapter 10: Hypothesis Tests about Means and Proportions: Applications
The Lower-Tail Hypothesis Test about ?: ? Is Known
The Two-Tail Hypothesis Test about ?: ? Is Known
The Upper-Tail Hypothesis Test about ?: ? Is Unknown
The Two-Tail Hypothesis Test about ?: ? is Unknown
Hypothesis Tests about p
Calculating the Probability of a Type II Error: ?
Adjusting the Sample Size to Control the Size of ?
Bayes' Theorem and an Inferential Approach to p
Chapter 11: Comparisons of Means and Proportions
The Difference between ?1 and ?2: Independent Samples
The Difference between ?1 and ?2: Paired Samples
The Difference between p1 and p2: Independent Samples
Bayes' Theorem and the Difference between p1 and p2
Chapter 12: Simple Linear Regression
Simple Linear Regression: The Model
The Estimated Regression Equation
Goodness of Fit: The Coefficient of Determination, r2
The Hypothesis Test about ?1
Alternative Approaches to Testing Significance
So Far, We Have Tested Only b1. Will We Also Test b0?
Assumptions: What Are They?
Assumptions: How Are They Validated?
Optional Material: Derivation of the Expressions for the Least-Squares Estimates of ?0 and ?1
Bayes' Theorem: Using Stan to Estimate the Relationship between Two Variables
Chapter 13: Multiple Regression
Simple Linear Regression: A Reprise
Multiple Regression: The Model
Multiple Regression: The Multiple Regression Equation
The Estimated Multiple Regression Equation
Multiple Regression: The 2 Independent Variable Case
Assumptions: What Are They? Can We Validate Them?
Tests of Significance: The Overall Regression Model
Tests of Signicance: The Independent Variables
There Must Be An Easier Way Than This, Right?
Using the Estimated Regression Equation for Prediction
Independent Variable Selection: The Best-Subsets Method
Logistic Regression: The Zero-One Dependent Variable
Bayes' Theorem: Stan and Multiple Regression Analysis
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
