ECON 2300 - Business Statistics - Online

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Lecture Notes

Video Nr. Topics Covered
001 Course Overview
Topic 01: What is Business Statistics?
002 Objectives
What is Business Statistics?
Kinds of Variables
Population and Samples
Descriptive and Inferential Statistics
Problem Set 1
Topic 02: Tabular and Graphical Descriptive Statistics of Categorical Data
003 Objectives
Problem Set 2
004 Examples of Graphical Illustrations
005 Cross Tabs
Topic 03: Tabular and Graphical Descriptive Statistics of Quantitative Data
006 Objectives
Problem Set 3
007 Histogram Example
008 Stem and Leaf Plot Example
009 Stem and Leaf Plot “Criminology”
010 Shapes of Distributions
011 Scatter Plots
Topic 04: Numeric Descriptive Statistics of Central Tendency
012 Objectives
Population Parameters and Sample Statistics
The Arithmetic Mean
The Geometric Mean
The Arithmetic vs. Geometric Mean – Example
Problem Set 4
013 Mode
Example: Arithmetic Mean, Median Mode, and Percentile
Measures of Central Tendency to Describe Shapes of Distributions
When to best use the mean, mode, or median to describe data?
Topic 05: Numerical Descriptive Statistics of Dispersion
014 Objectives
Standard Deviation
Example: Variance and Standard Deviation
Problem Set 5
015 Why dividing by (n-1) when we calculate the sample standard deviation?
016 The Coefficient of Variation
The Interquartile Range (IQR)
When to Best use the Range, Variance, Standard Deviation, and IQR?
017 Box and Whisker Plot
018 Covariance and Correlation Coefficient
Calculating Covariance and Correlation Matrix – Numerical Example
019 Chebychev’s Theorem
The Empirical Rule of Normally Distributed Population
Topic 06: Probability
020 Objectives
Approaches to Probability
Mutually and Non-Mutually Exclusive Events
Independent and Dependent Events
Probability Rules
Problem Set 6
021 Examples
022 Independence Tests – Independent Events Example
023 Independence Tests – Dependent Events Example
024 Bayes Theorem
025 Bayes Theorem Example I
026 Bayes Theorem Example II
027 Combinations
Combinations vs. Permutations Compared
028 Explaining the Odds of Winning the Lottery
Topic 07: Discrete Probability Distributions
029 Objectives
Discrete vs. Continuous Random Variables
Describing a Discrete Random Variable
Problem Set 7
030 The Binomial Distribution - The Bernoulli Process
The Binomial Distribution – Formula
031 The Binomial Distribution – An Example
The Cumulative Binomial Distribution
The Cumulative Binomial Distribution Table
Probability and Cumulative Probability Density Functions
032 Mean, Variance, Standard Deviation of Binomial Distribution
The Poisson Distribution
The Poisson Distribution – Example
Topic 08: Continuous Probability Distribution
033 Objectives
Continuous vs. Discrete Probability Distributions
The Uniform Distribution
The Normal Distribution
Problem Set 8
034 The z-Table
Working with the Standard Normal Distribution – Example
Topic 09: Sampling and Sampling Distributions
035 Objectives
What is sampling?
The Phenomenon to be Uncovered – The Central Limit Theorem
Problem Set 9
036 The Sampling Distribution of the Sample Means
037 The Student t-Distribution
Degrees of Freedom
038 The Student t-Distribution and his Father
So When to Use What? Example
039 Using the Standard Normal to Approximate the Binomial
Topic 10: Confidence Intervals
040 Objectives
The Basic Idea
The Formula for the Confidence Interval and Graphical Illustration
Problem Set 10
041 Typical Example
042 Using z- instead of t-scores
Example: Confidence Interval using z scores
Topic 11: Hypothesis Tests
043 Objectives
The Basic Idea
Stating a Scientific Hypothesis
Again: z or t-Table?
The Meaning of a=x% and Rejecting and Failing to Reject a Null Hypothesis
Problem Set 11
044 Type I and Type II Error
045 Steps in Conducting a Hypothesis Test