Preface
1
Introduction
1.1
Basic R commands and syntax
1.2
Plots
1.3
On the style of R in these chapters.
2
Data
2.1
Dates and Time Series
3
Describing Categorical Data
3.1
Analytics in R: Rolling over
3.2
Analytics in R: Selling smartphones
4
Describing Numerical Data
4.1
Analytics in R: Making M&Ms
4.2
Analytics in R: Executive Compensation
5
Association between Categorical Variables
5.1
Analytics in R: Car Theft
5.2
Analytics in R: Airline Arrivals
5.3
Analytics in R: Real Estate
5.4
Mosaic plots
6
Association between Numerical Variables
6.1
Analytics in R: Locating a new store
7
Probability
8
Conditional Probability
9
Random Variables
10
Association of Random Variables
11
Probability Models for Counts
11.1
Analytics in R: Focus on Sales
11.2
Analytics in R: Defects in Semiconductors
12
The Normal Probability Model
12.1
Analytics in R: SATs and Normality
12.2
Analytics in R: Value at Risk
12.3
Normal Quantile Plots in R
13
Samples and Surveys
14
Sampling Variation and Quality Control
14.1
Analytics in R: Monitoring a Call Center
14.1.1
Using the
qcc
package
15
Confidence Intervals
15.1
Analytics in R: Property Taxes
15.2
Analytics in R: A Political Poll
16
Statistical Tests
16.1
Analytics in R: Do Enough Households Watch
16.2
Analytics in R: Comparing Returns on Investments
17
Comparison
17.1
Analytics in R: A/B Testing
17.2
Analytics in R: Comparing Two Diets
17.3
Analytics in R: Evaluating a Promotion
17.4
Analytics in R: Sales Force Comparison
18
Inference for Counts
18.1
Analytics in R: Retail Credit
18.2
Analytics in R: Detecting Accounting Fraud
18.3
Analytics in R: Web Hits
19
Linear Patterns
19.1
Analytics in R: Estimating Consumption
19.2
Analytics in R: Lease Costs
20
Curved Patterns
20.1
Analytics in R: Optimal Pricing
21
The Simple Regression Model
21.1
Analytics in R: Locating a Franchise Outlet
21.2
Analytics in R: Climate Change
22
Regression Diagnostics
22.1
Analytics in R: Estimating Home Prices
22.2
Analytics in R: Cell Phone Subscribers
23
Multiple Regression
23.1
Analytics in R: Subprime Mortgages
23.2
Plotting a regression model
24
Building Regression Models
24.1
Analytics in R: Market Segmentation
24.2
Analytics in R: Retail Profits
25
Categorical Explanatory Variables
25.1
Analytics in R: Priming in Advertising
26
Analysis of Variance
26.1
Analytics in R: Judging the Credibility of Advertisements
27
Time Series
27.1
Analytics in R: Predicting Sales of New Cars
27.2
Analytics in R: Forecasting Unemployment
27.3
Analytics in R: Forecasting Profits
References
An R-companion for Statistics for Business: Decision Making and Analysis
10
Association of Random Variables
The calculations shown in this chapter do not require R.