﻿ Hawkes Learning | Statistics Resources | Discovering Statistics and Data | Technology

# Discovering Statistics and Data, Third Edition

## Technology Instructions

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

### Chapters

Chapter 2: Data, Reality, and Problem Solving

• 2.3: Time Series Data vs. Cross-Sectional Data
• Graphs > Line Graph

Chapter 3: Visualizing Data

• 3.2: Displaying Qualitative Data Graphically
• Graphs > Bar Charts
• Graphs > Pareto Charts
• Graphs > Pie Charts
• 3.4: Histograms and Other Graphical Displays of Quantitative Data
• Graphs > Histograms
• Data Manipulation > Sorting
• Graphs > Dot plot
• Graphs > Line Graphs
• Graphs > Choropleth Map (County)
R | JMP

Chapter 4: Describing Data from One Variable

• 4.1: Measures of Location
• Descriptive Statistics > One Variable
• Descriptive Statistics > Two Variable
• Data Manipulation > Filtering
• 4.2: Measures of Dispersion
• Graphs > Box Plot
• 4.3: Measures of Relative Position, Box Plots, and Outliers
• Descriptive Statistics > One Variable
• Graphs > Box Plot
• 4.4: Data Subsetting
• Data Manipulation > Subset Calculations
• 4.5: Analyzing Grouped Data
• Descriptive Statistics > Two Variable

Chapter 5: Discovering Relationships

• 5.1: Scatterplots and Correlation
• Descriptive Statistics > One Variable
• 5.2: Fitting a Linear Model
• Regression > Simple Linear Regression
• 5.5: Scatterplots for More Than Two Variables
• Graphs > Multivariate/Multidimensional
• Gapminder Trendalyzer

Chapter 6: Probability, Randomness and Uncertainty

• 6.4: Combinations and Permutations
• Counting > Combination
• Counting > Factorial
• Counting > Permutation

Chapter 7: Discrete Probability Distributions

• 7.2: Discrete Random Variables
• Descriptive Statistics > Two Variable
• 7.4: The Binomial Distribution
• Binomial Distribution > Binomial Probability (pdf)
• Binomial Distribution > Binomial Probability Distribution
• Binomial Distribution > Binomial Probability (cdf)
• 7.5: The Poisson Distribution
• Poisson Distribution > Poisson Probability Distribution
• Poisson Distribution > Poisson Probability (cdf)
• Poisson Distribution > Poisson Probability (pdf)
• 7.6: The Hypergeometric Distribution
• Hypergeometric Distribution > Hypergeometric Distribution

Chapter 8: Continuous Probability Distributions

• 8.3: The Standard Normal Distribution
• Normal Distribution > Normal Probability (cdf)
• Normal Distribution > Inverse Normal
• 8.4: Applications of the Normal Distribution
• Normal Distribution > Normal Probability Graph
• Normal Distribution > Normal Probability (cdf)
• Normal Distribution > Inverse Normal
• 8.5: Assessing Normality
• Graphs > Normal Probability Plot
• Normal Distribution > Inverse Normal
• Graphs > Histograms
• 8.6: Approximation to the Binomial Distribution
• Binomial Distribution > Binomial Probability (cdf)

Chapter 9: Samples and Sampling Distributions

• 9.1: Random Samples
• Sampling > Random Samples
• 9.4: The Distribution of the Sample Proportion
• Normal Distribution > Normal Probability (cdf)

Chapter 10: Estimation: Single Samples

• 10.2: Interval Estimation of the Population Mean
• Confidence Intervals > t-interval
• Confidence Intervals > z-interval
• Graphs > Normal Probability Plot
• 10.3: Estimating the Population Proportion
• Confidence Intervals > Proportion
• 10.4: Estimating the Population Standard Deviation or Variance
• Chi-Square Distribution > Critical Value
• Confidence Intervals > Variance

Chapter 11: Hypothesis Testing: Single Samples

• 11.2: Testing a Hypothesis about a Population Mean
• Graphs > Normal Probability Plot
• Hypothesis Testing > t-Test
• Hypothesis Testing > z-Test
• Normal Distribution > Normal Probability (cdf)
• t-Distribution > t-Probability (cdf)
• 11.3: The Relationship Between Confidence Interval Estimation and Hypothesis Testing
• Confidence Intervals > z-Interval
• 11.4: Testing a Hypothesis about a Population Proportion
• Hypothesis Testing > One Proportion z-Test
• Normal Distribution > Normal Probability (cdf)
• 11.5: Testing a Hypothesis about a Population Standard Deviation or Variance
• Chi-Square Distribution > Right Tailed Probability (cdf)

Chapter 12: Inferences about Two Samples

• 12.1: Inference about Two Means: Independent Samples
• Confidence Intervals > Two Sample z-Interval
• Confidence Intervals > Two Sample t-Interval (Independent Samples)
Minitab | R | Rguroo | SPSS
• Hypothesis Testing > Two Sample z-Test
• Hypothesis Testing > Two Sample t-Test (Independent Samples)
• Graphs > Normal Probability Plot
• t-Distribution > t-Probability (cdf)
• 12.2: Inference about Two Means: Dependent Samples (Paired Difference)
• Confidence Intervals > t-Interval
• Graphs > Normal Probability Plot
• Hypothesis Testing > t-Test
• Hypothesis Testing > Two Sample t-Test (Dependent Samples, Paired Difference)
• t-Distribution > t-Probability (cdf)
• Confidence Intervals > t-Interval
• Confidence Intervals > Two Sample t-Interval (Dependent Samples, Paired Difference)
• 12.3: Inference about Two Population Proportions
• Confidence Intervals > Two Sample Proportions z-Interval
• Hypothesis Testing > Two Proportion z-Test

Chapter 13: Regression, Inference, and Model Building

• 13.1: Assumptions of the Simple Linear Model
• Regression > Simple Linear Regression
• 13.2: Inference Concerning β1
• Regression > Simple Linear Regression
• t-Distribution > Inverse t
• 13.3: Inference Concerning the Model's Prediction
• Regression > Regression Prediction Intervals
• Regression > Linear Regression Fitted Line Plot with Confidence Interval
• Regression > Linear Regression Fitted Line Plot with Prediction Interval

Chapter 14: Multiple Regression

• 14.1: The Multiple Regression Model
• Regression > Multiple Regression
• 14.2: The Coefficient of Determination and Adjusted R2
• Regression > Multiple Regression
• 14.4: Inference Concerning the Multiple Regression Model and Its Coefficients
• Regression > Multiple Regression
• 14.5: Inference Concerning the Model's Prediction
• Regression > Regression Prediction Intervals

Chapter 15: Analysis of Variance (ANOVA)

• 15.1: One-Way ANOVA
• ANOVA > One-Way
• Graphs > Normal Probability Plot
• F-Distribution > F-Probability (cdf)
• 15.2: Two-Way ANOVA: The Randomized Block Design
• ANOVA > Two-Way
• 15.3: Two-Way ANOVA: The Factorial Design
• ANOVA > Two-Way

Chapter 16: Looking for Relationships in Qualitative Data

• 16.1: The Chi-Square Distribution
• Chi-Square Distribution > Critical Value
• 16.2: The Chi-Square Test for Goodness of Fit
• Chi-Square Distribution > Test for Goodness of Fit
• Chi-Square Distribution > Right Tailed Probability (cdf)
• 16.3: The Chi-Square Test for Association
• Chi-Square Distribution > Test for Association

Chapter 17: Nonparametric Tests

• 17.1: The Sign Test
• Nonparametric > Sign Test
• 17.2: The Wilcoxon Signed-Rank Test
• Nonparametric > Wilcoxon Signed Rank
• 17.3: The Wilcoxon Rank-Sum Test
• Nonparametric > Wilcoxon Rank Sum
• 17.4: The Rank Correlation Test
• Nonparametric > Spearman Rank Correlation
• 17.5: The Runs Test for Randomness
• Nonparametric > Runs Test for Randomness
• 17.6: The Kruskal-Wallis Test
• Nonparametric > Kruskal-Wallis Test