﻿ Hawkes Learning | Statistics Resources | Beginning Statistics

# Beginning Statistics, Third Edition

## Technology Instructions

What follows are step-by-step instructions for using various types of technology to evaluate statistical concepts.

### Chapters

Chapter 1: Introduction to Statistics

• 1.3: The Process of a Statistical Study
• Sampling > Random Samples

Chapter 2: Graphical Descriptions of Data

• 2.1: Frequency Distributions
• Frequency Distribution > Qualitative Frequency Distribution
• Frequency Distribution > Quantitative Frequency Distribution
• 2.2: Graphical Displays of Data
• Graphs > Bar Charts
• Graphs > Choropleth Map (County)
R | JMP
• Graphs > Dot plot
• Graphs > Histograms
• Graphs > Line Graphs
• Graphs > Pareto Chart
• Graphs > Pie Charts
• Graphs > Stem-and-Leaf Plot

Chapter 3: Numerical Descriptions of Data

• 3.1: Measures of Center
• Descriptive Statistics > One Variable
• 3.2: Measures of Dispersion
• Descriptive Statistics > One Variable
• 3.3: Measures of Relative Position
• Descriptive Statistics > One Variable
• Graphs > Box Plot

Chapter 4: Probability, Randomness, and Uncertainty

• 4.4: Combinations and Permutations
• Counting > Combination
• Counting > Factorial
• Counting > Permutation
• 4.5: Combining Probability and Counting Techniques
• Counting > Combination
• Counting > Factorial
• Counting > Permutation

Chapter 5: Discrete Probability Distributions

• 5.1: Discrete Random Variables
• Descriptive Statistics > One Variable
• 5.2: Binomial Distribution
• Binomial Distribution > Binomial Probability (pdf)
• Binomial Distribution > Binomial Probability Distribution
• Binomial Distribution > Binomial Probability (cdf)
• 5.3: Poisson Distribution
• Poisson Distribution > Poisson Probability (cdf)
• Poisson Distribution > Poisson Probability (pdf)
• Poisson Distribution > Poisson Probability Distribution
• 5.4: Hypergeometric Distribution
• Hypergeometric Distribution > Hypergeometric Distribution

Chapter 6: Normal Probability Distributions

• 6.2: The Standard Normal Distribution
• Normal Distribution > Normal Probability (cdf)
• 6.3: Finding Probability Using a Normal Distribution
• Normal Distribution > Normal Probability (cdf)
• 6.4: Finding Values of a Normally Distributed Random Variable
• Normal Distribution > Inverse Normal
• 6.5: Approximating a Binomial Distribution Using a Normal Distribution
• Normal Distribution > Normal Probability (cdf)

Chapter 7: The Central Limit Theorem

• 7.2: Central Limit Theorem with Means
• Normal Distribution > Normal Probability (cdf)
• 7.3: Central Limit Theorem with Proportions
• Normal Distribution > Normal Probability (cdf)

Chapter 8: Confidence Intervals

• 8.1: Estimating Population Means (Sigma Known)
• Confidence Intervals > z-interval
• 8.2: Student's t-Distribution
• t-Distribution > Inverse t
• 8.3: Estimating Population Means (Sigma Unknown)
• Confidence Intervals > t-interval
• 8.4: Estimating Population Proportions
• Confidence Intervals > Proportion
• 8.5: Estimating Population Variances
• Confidence Intervals > Standard Deviation
• Confidence Intervals > Variance

Chapter 9: Confidence Intervals for Two Samples

• 9.1: Comparing Two Population Means (Sigma Known, Independent Samples)
• Confidence Intervals > Two Sample z-Interval
• 9.2: Comparing Two Population Means (Sigma Unknown, Independent Samples)
• 9.3: Comparing Two Population Means (Sigma Unknown, Dependent Samples)
• 9.4: Comparing Two Population Proportions
• Confidence Intervals > Two Sample Proportions z-Interval

Chapter 10: Hypothesis Testing

• 10.2: Hypothesis Testing for Population Means (Sigma Known)
• Hypothesis Testing > z-Test
• 10.3: Hypothesis Testing for Population Means (Sigma Unknown)
• Hypothesis Testing > t-Test
• 10.4: Hypothesis Testing for Population Proportions
• Hypothesis Testing > One Proportion z-Test
• 10.5: Hypothesis Testing for Population Variances
• Chi-Square Distribution > Critical Value
• Chi-Square Distribution > Left Tailed Probability (cdf)
• Chi-Square Distribution > Right Tailed Probability (cdf)
• 10.6: Chi-Square Test for Goodness of Fit
• Chi-Square Distribution > Test for Goodness of Fit
• 10.7: Chi-Square Test for Association
• Chi-Square Distribution > Critical Value
• Chi-Square Distribution > Test for Association

Chapter 11: Hypothesis Testing (Two or More Populations)

• 11.1: Hypothesis Testing: Two Population Means (Sigma Known, Independent Samples)
• Hypothesis Testing > Two Sample z-Test
• Normal Distribution > Normal Probability (cdf)
• 11.2: Hypothesis Testing: Two Population Means (Sigma Unknown, Independent Samples)
• Hypothesis Testing > Two Sample t-Test (Independent Samples)
• 11.3: Hypothesis Testing: Two Population Means (Sigma Unknown, Dependent Samples)
• Hypothesis Testing > Two Sample t-Test (Dependent Samples, Paired Difference)
• 11.4: Hypothesis Testing: Two Population Proportions
• Hypothesis Testing > Two Proportion z-Test
• 11.5: Hypothesis Testing: Two Population Variances
• Hypothesis Testing > Two Sample F-Test
• 11.6: ANOVA (Analysis of Variance)
• ANOVA > One-Way

Chapter 12: Regression, Inference, and Model Building

• 12.1: Scatter Plots and Correlation
• Graphs > Scatterplot
• Regression > Correlation Coefficient
• Regression > Coefficient of Determination
• Regression > Simple Linear Regression
• 12.2: Linear Regression
• Regression > Simple Linear Regression
• 12.3: Regression Analysis
• Regression > Simple Linear Regression
• Regression > Confidence Intervals for Slope and y-Intercept
• 12.4: Multiple Regression
• Regression > Simple Linear Regression
• Regression > Multiple Regression