MATH 1040 - Introduction to Statistics

MATH 1040 - Introduction to Statistics

Textbook and Resources

• OpenIntro Textbook
• Reading from Textbook
• Z-Table
• T-Table
• Decision Tree for Inference

Lectures

1: Variables

• Lesson 1 Overview
• 1.1 Variables
• 1.2 Quantitative Variables
• 1.3 Levels of Measurement

2: Sampling Methods

• Lesson 2 Overview
• 2.1 Randomness
• 2.2 Sampling Methods
• 2.3 Not-so-random Sampling Methods

3: Designing a Study

• 3.1 Observational Studies
• 3.2 Experimental Studies
• 3.3 Blind Studies

4: Graphing

• 4.1 Graphing Basics
• 4.2 Dotplots
• 4.3 Stemplots
• 4.4 Scatterplots
• 4.5 Timeseries
• 4.6 Bargraphs
• 4.7 Pie charts

5: Histograms

• 5.1 What is a Histogram
• 5.2 Creating a Histogram
• 5.3 Histogram Shapes

6: Measures of Center

• 6.1 Measures of Center: The Mean
• 6.2 Measures of Center: The Mode
• 6.3 Measures of Center: The Median
• 6.4 Quartiles
• 6.5 Boxplots

7: Measures of Spread

• 7.1 Variance
• 7.2 Standard Deviation
• 7.3 The Z-Score
• 7.4 The Empirical Rule

8: Correlation

• 8.1 Line of Best Fit
• 8.2 Predictions with Linear Regression
• 8.3 Correlation
• 8.4 Coefficient of Determination

9: Probability

• 9.1 Probability
• 9.2 Relative Frequency
• 9.3 Complements

10: Compound Probability

• 10.1 Two Events: “OR” Probabilities
• 10.2 Mutually Exclusive Events
• 10.3 Conditional Probabilities
• 10.4 Two Events: “AND” Probabilities
• 10.5 Independent Events

11: Rules of Counting

• 11.1 Fundamental Counting Rule
• 11.2 Factorials
• 11.3 Permutations
• 11.4 Combinatorics

12: Probability Distributions

• 12.1 Probability Distributions (Not ready)
• 12.2 Distributions (Not ready)
• 12.3 Expected Value

13: Binomial Distributions

14: More on Binomial Distributions

15: Normal Distributions

• 15.1 Discrete and Continuous Variables
• 15.2 The Normal Distribution
• 15.3 Probability from a Normal Distribution
  • Z-Table
• 15.4 Finding the Z-score from a Probability

16: More on Normal Distributions

• 16.1 Practice Calculating Probabilities
• 16.2 Practice Calculating Percentiles/Quartiles
• 16.3

17: Central Limit Theorem

• 17.1 Sampling Distributions
• 17.2 Statistics of Sampling Distributions
• 17.3 The Central Limit Theorem
• 17.4 Example of using the Central Limit Theorem

18: Confidence Intervals with 1 Sample Means

• Lesson 18 Overview
• 18.1 Critical Values
  • Z-Table
• 18.2 Margin of Error
• 18.3 Confidence Interval when you know \(\sigma\)
• 18.4 Finding the best sample size
• 18.5 Confidence Interval when you don’t know \(\sigma\)
  • T-Table

19: Confidence Intervals with 1 Sample Proportions

• Lesson 19 Overview
• 19.1 Critical Values
• 19.2 Confidence Intervals with Proportions

20: Hypothesis Testing with 1 Sample Means

• Lesson 20 Overview
• 20.1 Hypotheses
• 20.2 How Confidence Intervals fit in
• 20.3 Critical Regions
• 20.4 P-Values
• 20.5 Full Hypothesis Test with 1 Sample of Quantitative Data (Means)

21: Hypothesis Testing with 1 Sample Proportions

22: Inference of 2 Independent samples (Means)

• 22.1 Reminder of Dependent vs. Independent samples
• 22.2 Hypotheses of 2 Independent samples
• 22.3 Confidence Intervals for 2 Independent Samples
• 22.4 Hypothesis Test of 2 Independent Samples

23: Inference of 2 Independent samples (Proportions)

• 23.1 Hypotheses of 2 Independent samples
• 23.2 Confidence Intervals for 2 Independent Samples
• 23.3 Hypothesis Test of 2 Independent Samples

24: Inference of 2 Dependent samples (Means)

25: Inference with Correlation