Course Resources Math 1530

Chapter 2 - Organizing and Summarizing Data

2.2 Organizing Quantitative Data: The Popular Displays

• Reading Bar Graphs
• Reading Line Graphs
• Reading Pie Graph
• Misleading Line Graphs
• Stem and leaf Plots

 

Chapter 3 - Numerically Summarizing Data

3.1 Measures of Central Tendency

• Mean Median and Mode
• Statistics: The Average
  (note…we consider data to have to have multiple modes if the data used most often "ties" with another data item)
• Sample vs. Population Mean

3.2 Measures of Dispersion

• Range and Mid-Range
• Variance of a Population
• Sample Variance --Why the formula uses n-1
• Empirical Rule
• Empirical Rule and Z-score Practice
• Standard Deviation Regular Formula
  Beth Dixon
• Standard Deviation Short Cut Formula
  Beth Dixon

3.4 Measures of Position and Outliers

• Normal Distribution - Z-score
• Z-scores
  Beth Dixon

3.5 The Five-Number Summary and Boxplots

• Box-and-Whisker Plots
• Reading Box-and-Whiskers Plots

 

Chapter 5 - Probability

5.1 Probability Rules

• Events and Outcomes 1
• Basic Probability
• Simple Probability
• Probability with Playing Cards and Venn Diagrams
• Probability 1 Module 1 Examples
• Coin Flipping Example
• Events and Outcomes 2
• Events and Outcomes 3
• LeBron asks about the chances of making 10 free throws

5.2 The Addition Rule and Complements

• Addition Rule for Probabilit

5.3 Independence and the Multiplication Rule

• Compound Probability of Independent Events
• Independent Events 1
• Independent Events 2
• Independent Events 3
• Frequency Probability and Unfair Coins
• Probability of Dependent Events

5.4 Conditional Probability and the General Multiplication Rule

• Getting at Least One Heads:
• Dependent Probability Example 1
• Dependent Probability Example 2

5.5 Counting Techniques

• Permutations
• Combinations
• Permutations and Combinations 1
• Permutations and Combinations 2
• Permutations and Combinations 3
• Permutations and Combinations 4
• Mega Millions Jackpot Probability
• Probability of Dependent Events 2
  *advanced
• Probability using Combinations
• Birthday Probability Problem

 

Chapter 7 – The Normal Probability Distribution

7.2 The Standard Normal Distribution

• Standard Normal Part 1
  Beth Dixon
• Standard Normal Part 2
  Beth Dixon

7.3 Applications of the Normal Distribution

• Normal Distribution
  Beth Dixon

7.5 The Normal Approximation to the Binomial Probability Distribution

• *Only: Determine whether the normal distribution can be used to approximate the binomial distribution

 

Chapter 8 – Sampling Distributions

8.1 Distribution of the Sample Mean

• Central Limit Theorem -- (Idea not practice)
• Sampling Distribution of the Sample Mean 2 (more theory)
• Standard Error of the Mean
• Sampling Distribution Example Problem
• Confidence Interval 1 -- (intro to idea using CLT)

 

Chapter 9 – Estimating the Value of a Parameter Using Confidence Intervals

9.2 Confidence Intervals for a Population Mean When the Population Standard Deviation is Unknown

• T-Statistic Confidence Interval (explanation but not formula we use)

9.3 Estimating a Population Proportion

• Confidence Intervals on Proportions
  Beth Dixon

 

Chapter 10 – Hypothesis Tests Regarding a Parameter

10.1 The Language of Hypothesis Testing

• Type 1 Errors

10.2 Hypothesis Tests for a Population Mean-Population Standard Deviation Known

• * Only: Introduction of logic for hypothesis testing process for both classical and p-value methods (as needed for section 10.3)

10.3 Hypothesis Tests for a Population Mean-Population Standard Deviation

• Small Sample Hypothesis Test
• Hypothesis Testing on Mean – T-Table
  Beth Dixon