Our textbook this semester is Introductory Statistics by OpenIntro
Lesson 1 Variables
Readings from our Introductory Statistics Textbook
1.1 Variables
- 1.2.1 Observations, variables, and data matrices (pages 11-12)
- 1.2.2 Types of Variables (pages 12-13)
- 1.2.3 Relationships between variables (pages 13-15)
1.2 Quantitative Variables
- 1.2.2 Types of Variables (pages 12-13)
1.3 Levels of Measurement
- 1.2.2 Types of Variables (pages 12-13)
Lesson 2 Sampling Methods
Readings from our Introductory Statistics Textbook
2.1 Populations and Samples
- 1.3.1 Populations and Samples (pages 16-17)
2.2 Random Sampling Methods
- 1.4.2 Sampling methods, non-random sampling methods, and Bias (pages 22-25)
- 1.4.3 Random sampling methods (pages 25-29)
Lesson 3 Study Design
Readings from our Introductory Statistics Textbook
3.1 Observational Studies
- 1.3.4 Observational Studies versus Experiments (pages 19-20)
- 1.4.1 Observational Studies and Confounding Variables (pages 20-22)
3.2 Experimental Studies
- 1.5.1 Experimental Studies, Control/Treatment groups, Placebos (pages 30-31)
3.3 Randomized Experiments
- 1.5.3 Randomized Experiments (pages 32-34)
Lesson 4 Graphing
Readings from our Introductory Statistics Textbook
4.1 Graphing Basics
4.2 Stem-and-leaf plot
- 2.1.2 Stem-and-leaf plots and dot plots (pages 48-49)
4.3 Dotplot
- 2.1.2 Stem-and-leaf plots and dot plots (pages 50-51)
4.4 Scatterplot
- 1.3.3 Explanatory and Response Variables (page 19)
- 2.1.1 Scatterplots for paired data (pages 46-48)
4.5 Timeseries
4.6 Bargraphs and Paretto Charts
- 2.3.1 Contingency tables and bar plots (pages 70-71)
- 2.3.3 Segmented bar plots (pages 73-74)
4.7 Pie Charts
- 2.3.4 The only pie chart you will see in this book (pages 74-75)
Lesson 5 Histograms
Readings from our Introductory Statistics Textbook
5.1 Making a Histogram
- 2.1.3 Histograms (pages 51-54)
5.2 Reading a Histogram
- 2.1.4 Describing Shape (pages 954-55)
Lesson 6 Measures of Center
Readings from our Introductory Statistics Textbook
6.1 Measures of Center - Mean
- 2.2.1 Measures of Center (pages 56-58)
- 2.2.4 Calculator: summarizing 1-variable statistics (pages 64-66)
6.2 Measures of Center - Mode
- 2.2.1 Measures of Center (pages 56-58)
- 2.2.1 Measures of Center (pages 56-58)
- 2.2.4 Calculator: summarizing 1-variable statistics (pages 64-66)
6.4 Quartiles
- 2.2.3 Box plots and quartiles (pages 61-64)
- 2.2.4 Calculator: summarizing 1-variable statistics (pages 64-66)
6.5 Boxplots
- 2.2.3 Box plots and quartiles (pages 61-64)
- 2.2.5 Outliers and robust statistics (pages 66-68)
Lesson 7 Measures of Spread
Readings from our Introductory Statistics Textbook
7.1 Variance
- 2.2.2 Standard deviation as a measure of spread (pages 58-61)
7.2 Standard Deviation
- 2.2.2 Standard deviation as a measure of spread (pages 58-61)
7.3 Z-Score
- 4.1.2 Standardizing with Z-scores (pages 142-143)
7.4 Empirical Rule
- 2.2.2 Standard deviation as a measure of spread (pages 58-61)
Lesson 8 Correlation
Readings from our Introductory Statistics Textbook
8.1 Line of Best Fit
- 8.1.1 Beginning with Straight Lines (pages 340-342)
- 8.1.2 Fitting a line by eye (pages 342)
- 8.1.3 Residuals (pages 342-346)
- 8.2.1 An objective measure for finding the best line (pages 348-349)
- 8.2.2 Conditions for the least squares line (pages 349-350)
8.2 Predictions with Interpolation and Extrapolation
- 8.2.4 Interpreting regression line parameter estimates (pages 352-353)
- 8.2.5 Extrapolation is treacherous (pages 353-354)
8.3 Correlation Coefficient
- 8.1.4 Describing linear relationships with correlation (pages 346-348)
8.4 Coefficient of Determination
- 8.2.6 Using \(r^2\) to describe the strength of a fit (pages 354-355)
Lesson 9 Probabilities
Readings from our Introductory Statistics Textbook
9.1 Definitions and Relative Frequency
- 3.1.1 Probability (pages 88-90)
9.2 Probabilities and the Law of Large Numbers
- 3.1.1 Probability (pages 88-90)
9.3 Complement of an event
- 3.1.4 Complement of an event (page 95)
Lesson 10 Compound Probabilities
Readings from our Introductory Statistics Textbook
10.1 OR probabilities
- 3.1.2 Disjoint or mutually exclusive outcomes (pages 90-92)
10.2 Mutually Exclusive Events
- 3.1.2 Disjoint or mutually exclusive outcomes (pages 90-92)
10.3 Conditional Probability
- 3.2.1 Conditional Probability (pages 98-100)
- 3.2.2 Defining conditional probability (pages 100-102)
- 3.2.4 General multiplication rule (pages 103-104)
- 3.2.7 Tree diagrams (pages 109-110)
10.4 AND probabilities and Independence
- 3.1.3 Probabilities when events are not disjoint(pages 92-94)
10.5 Independent Events
- 3.1.5 Independence (pages 96-98)
- 3.2.6 Checking for independent and mutually exclusive events (pages 106-108)
Lesson 11 Rules of Counting
Readings from our Introductory Statistics Textbook
11.1 Fundamental Counting Rule
11.2 Factorials
11.3 Permutations
11.4 Combinatorics
Lesson 13 Binomial Probability
Readings from our Introductory Statistics Textbook
3.3 The binomial
Lesson 15 The Normal Distribution
Readings from our Introductory Statistics Textbook
15.1 Discrete and Continuous Variables
- 3.6 Continuous distributions (pages 125-127)
15.2 The Normal Distribution
- 4.1.1 Normal distribution model (pages 141-142)
15.3 Probability from a Normal Distribution
- 4.1.3 Normal probability table (pages 143-144)
- 4.1.4 Normal probability examples (pages 144-147)
- 4.1.5 Calculator: finding normal probabilities (pages 148-150)
- 4.1.6 68-95-99.7 rule (page 150)
15.4 Finding Z-scores from a Probability
- 4.1.5 Calculator: finding normal probabilities (pages 148-150)
Lesson 16 The Normal Distribution with Real Data
16.1 Standardizing with Z-scores
- 4.1.2 Standardizing with Z-scores (pages 142-143)
16.2 Finding probabilities with values
- 4.1.3 Normal probability table (pages 143-144)
- 4.1.4 Normal probability examples (pages 144-147)
- 4.1.5 Calculator: finding normal probabilities (pages 148-150)
- 4.1.6 68-95-99.7 rule (page 150)
16.3 Finding values with probabilities
- 4.1.5 Calculator: finding normal probabilities (pages 148-150)
Lesson 17 Central Limit Theorem
Readings from our Introductory Statistics Textbook
17.1 Sampling Distributions
- 4.2.1 The mean and standard deviation of $\bar{x}$ (pages 155-157)
17.2 Statistics of Sampling Distributions
- 4.2.1 The mean and standard deviation of $\bar{x}$ (pages 155-159)
17.3 The Central Limit Theorem
- 4.2.2 Examining the Central Limit Theorem (pages 159-162)
Lesson 18 Confidence Intervals with 1 Quantitative Sample
Readings from our Introductory Statistics Textbook
18.1 Critical Values
- 4.2.3 Normal approximation for the sampling distribution of $\bar{x}$ (pages 162-163)
18.2 Margin of Error
- 7.1.1 Using the z-distribution for inference when $\mu$ is unknown and $\sigma$ is known (page 277)
18.3 Confidence Interval with 1 Quantitative Sample
- 7.1.4 The Normality Condition (page 282)
- 7.1.1 Using the z-distribution for inference when $\mu$ is unknown and $\sigma$ is known (page 277)
18.4 Determining \(n\)
- 7.1.6 Choosing a sample size when estimating a mean (pages 285-286)
18.5 Interpreting the Confidence Interval
- 7.1.2 Introducing the t-distribution (pages 278-281)
- 7.1.3 The t-distribution and the standard error of a mean (page 281)
- 7.1.5 One sample t-intervals (pages 282-285)
Lesson 19 Confidence Intervals with 1 Categorical Sample
Readings from our Introductory Statistics Textbook
19.1 Critical Value and Margin of Error for a Proportion
- 6.1.1 Confidence intervals for a proportion (pages 218–222)
Lesson 19.2 Confidence Interval for a Proportion
- 6.1.1 Confidence intervals for a proportion (pages 218–222)
Lesson 20 Hypothesis Testing with 1 Sample Means
Readings from our Introductory Statistics Textbook
20.1 Hypotheses
- 5.3.1 Introducing Hypotheis Testing (page 195)
- 5.3.2 Setting up the null and alternate hypotheses (pages 196-198)
Lesson 25 Correlation Inference
Readings from our Introductory Statistics Textbook