﻿ Hawkes Learning | Statistics Resources | Discovering Business Statistics, Second Edition | Technology

# Discovering Business Statistics, Second Edition

## Technology Instructions

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

### Chapters

Chapter 2: Data, Big Data, and Analytics

• 2.4: Time Series Data and Cross-Sectional Data
• Graphs > Line Graph

Chapter 3: Organizing, Displaying, and Interpreting Data

• 3.2: Displaying Qualitative Data
• Graphs > Bar Charts
• Graphs > Pie Charts
• 3.4: Graphical Displays of Quantitative Data
• Graphs > Histogram
• Graphs > Stem-and-Leaf Plot
• Data Manipulation > Sorting
• Graphs > Dot Plots
• Graphs > Line Graph
• Graphs > Choropleth Map (County)
R | JMP

Chapter 4: Numerical Descriptive Statistics

• 4.1: Measures of Location
• Descriptive Statistics > One Variable
• Descriptive Statistics > Two Variable
• Data Manipulation > Filtering
• 4.2: Measures of Dispersion
• Descriptive Statistics > One Variable
• Graphs > Box Plot
• 4.3: Measures of Relative Position
• Descriptive Statistics > One Variable
• Graphs > Box Plot
• 4.4: Data Subsetting
• Data Manipulation > Subset Calculations
• 4.5: Analyzing Grouped Data
• Descriptive Statistics > Two Variable
• 4.7: Measures of Association Between Two Variables
• Regression > Correlation Coefficient

Chapter 5: Probability, Randomness and Uncertainty

• 5.6: Counting Techniques
• Counting > Combination
• Counting > Factorial
• Counting > Permutation

Chapter 6: Discrete Probability Distributions: About the Future

• 6.2: Discrete Random Variables
• Descriptive Statistics > Two Variable
• 6.4: The Binomial Distribution
• Binomial Distribution > Binomial Probability (pdf)
• Binomial Distribution > Binomial Probability Distribution
• Binomial Distribution > Binomial Probability (cdf)
• 6.5: The Poisson Distribution
• Poisson Distribution > Poisson Probability Distribution
• Poisson Distribution > Poisson Probability (cdf)
• Poisson Distribution > Poisson Probability (pdf)
• 6.6: The Hypergeometric Distribution
• Hypergeometric Distribution > Hypergeometric Distribution

Chapter 7: Continuous Random Variables

• 7.3: Assessing Normality Graphically
• Graphs > Box Plot
• Graphs > Histogram
• Graphs > Normal Probability Plot
• 7.4: The Standard Normal Distribution
• Normal Distribution > Normal Probability (cdf)
• Normal Distribution > Normal Probability Graph
• Normal Distribution > Inverse Normal
• 7.5: Approximations to Other Distributions
• Binomial Distribution > Binomial Probability (cdf)

Chapter 8: Samples and Sampling Distributions

• 8.1: Random Samples and Sampling Distributions
• Sampling > Random Samples
• 8.3: The Distribution of the Sample Proportion
• Normal Distribution > Normal Probability (cdf)

Chapter 9: Estimation with Confidence Intervals: Single Sample

• 9.1: Estimating the Population Mean, σ Known
• Confidence Intervals > z-interval
• 9.2: Estimating the Population Mean, σ Unknown
• Confidence Intervals > t-interval
• 9.3: Estimating the Population Proportion
• Confidence Intervals > Proportion
• 9.4: Estimating the Population Standard Deviation or Variance
• Chi-Square Distribution > Critical Value
• Confidence Intervals > Variance

Chapter 10: Hypothesis Testing: Single Sample

• 10.2: Testing a Hypothesis about a Population Mean, σ Known
• Hypothesis Testing > z-Test
• Normal Distribution > Normal Probability (cdf)
• 10.3: Testing a Hypothesis about a Population Mean, σ Unknown
• Hypothesis Testing > t-Test
• t-Distribution > t-Probability (cdf)
• t-Distribution > Inverse t
• 10.4: The Relationship Between Confidence Interval Estimation and Hypothesis Testing
• Confidence Intervals > z-Interval
• 10.5: Testing a Hypothesis about a Population Proportion
• Hypothesis Testing > One Proportion z-Test
• 10.6: Testing a Hypothesis about a Population Variance
• Chi-Square Distribution > Right Tailed Probability (cdf)

Chapter 11: Inferences about Two Samples

• 11.1: Comparing Two Population Means, σ1 and σ2 Known
• Confidence Intervals > Two Sample z-Interval
• Hypothesis Testing > Two Sample z-Test
• 11.2: Comparing Two Population Means, σ1 and σ2 Unknown
• Confidence Intervals > Two Sample t-Interval (Independent Samples)
• Hypothesis Testing > Two Sample t-Test (Independent Samples)
• 11.3: Paired Difference Test
• Hypothesis Testing > Two Sample t-Test (Dependent Samples, Paired Difference)
• t-Distribution > t-Probability (cdf)
• Confidence Intervals > Two Sample t-Interval (Dependent Samples, Paired Difference)
• 11.4: Comparing Two Population Proportions
• Confidence Intervals > Two Sample Proportions z-Interval
• Hypothesis Testing > Two Proportion z-Test
• 11.5: Comparing Two Population Variances
• F-Distribution > Critical Value
• F-Distribution > F-Probability (cdf)

Chapter 12: Analysis of Variance (ANOVA)

• 12.1: Introduction to Analysis of Variance (ANOVA)
• ANOVA > One-Way
• 12.2: Assumptions in an ANOVA Test
• Graphs > Box Plot
• Graphs > Histogram
• Graphs > Normal Probability Plot
• F-Distribution > Critical Value
• Normal Distribution – Test for Normality
• 12.3: The F-Distribution and the F-Test
• ANOVA > One-Way
• 12.4: Multiple Comparison Procedures
• ANOVA > One-Way
• ANOVA > Fisher's LSD
• ANOVA > Tukey's HSD
• q-Distribution > Critical Value
• 12.5: Two-Way ANOVA: The Randomized Block Design
• ANOVA > Two-Way
• 12.6: Two-Way ANOVA: The Factorial Design
• ANOVA > Two-Way
• F-Distribution > Critical Value

Chapter 13: Regression, Inference, and Model Building

• 13.1: The Simple Linear Regression Model
• Regression > Simple Linear Regression
• 13.2: Residual Analysis
• Regression > Simple Linear Regression
• Graphs > Normal Probability Plot
• 13.3: Evaluating the Fit of the Linear Regression Model
• Regression > Coefficient of Determination
• Regression > Correlation Coefficient
• 13.5: Inference Concerning the Slope
• Regression > Simple Linear Regression
• t-Distribution > Inverse t
• 13.6: 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
• 14.3: Inference Concerning the Multiple Regression Model and Its Coefficients
• Regression > Multiple Regression
• 14.4: Inference Concerning the Model's Prediction
• Regression > Regression Prediction Intervals
• 14.5: Models with Qualitative Independent Variables
• Regression > Multiple Regression

Chapter 15: Time Series Analysis and Forecasting

• 15.1: Time Series Components
• Graphs > Line Graph
• 15.2: Moving Averages
• Time Series > Simple Moving Average
• Time Series > Weighted Moving Average
• 15.3: Exponential Smoothing Techniques
• Time Series > Simple Exponential Smoothing
• Time Series > Adjusted Exponential Smoothing
• 15.4: Forecast Accuracy
• Time Series > Mean Absolute Deviation
• Time Series > Mean Absolute Percentage Error
• Time Series > Mean Squared Error

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 Statistics

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

Chapter 18: Statistical Process Control

• 18.1: Basic Charts and Diagrams Used in Quality Control
• Graphs > Pareto Charts