CCNA 200-301 Pearson uCertify Network Simulator
Cisco 200-301-SIMULATOR.AB1
CCNA 200-301 Pearson uCertify Network Simulator
ISBN: 978-1-61691-837-8Cisco 200-301-SIMULATOR.AB1
Business Statistics for Beginners
(BUS-STATS.AE1) / ISBN : 978-1-64459-461-2
This course includes
Interactive Lessons
Lab
About This Course
Skills You’ll Get
Interactive Lessons
20+ Interactive Lessons | 55+ Exercises | 276+ Quizzes | 100+ Flashcards | 100+ Glossary of terms
1
Introduction
- About This Course
- Foolish Assumptions
- Icons Used in This Course
- Where to Go from Here
2
The Art and Science of Business Statistics
- Representing the Key Properties of Data
- Probability: The Foundation of All Statistical Analysis
- Using Sampling Techniques and Sampling Distributions
- Statistical Inference: Drawing Conclusions from Data
3
Pictures Tell the Story: Graphical Representations of Data
- Analyzing the Distribution of Data by Class or Category
- Histograms: Getting a Picture of Frequency Distributions
- Checking Out Other Useful Graphs
4
Finding a Happy Medium: Identifying the Center of a Data Set
- Looking at Methods for Finding the Mean
- Getting to the Middle of Things: The Median of a Data Set
- Comparing the Mean and Median
- Discovering the Mode: The Most Frequently Repeated Element
5
Searching High and Low: Measuring Variation in a Data Set
- Determining Variance and Standard Deviation
- Finding the Relative Position of Data
- Measuring Relative Variation
6
Measuring How Data Sets Are Related to Each Other
- Understanding Covariance and Correlation
- Interpreting the Correlation Coefficient
7
Probability Theory: Measuring the Likelihood of Events
- Working with Sets
- Betting on Uncertain Outcomes
- Looking at Types of Probabilities
- Following the Rules: Computing Probabilities
8
Probability Distributions and Random Variables
- Defining the Role of the Random Variable
- Assigning Probabilities to a Random Variable
- Characterizing a Probability Distribution with Moments
9
The Binomial, Geometric, and Poisson Distributions
- Looking at Two Possibilities with the Binomial Distribution
- Determining the Probability of the Outcome That Occurs First: Geometric Distribution
- Keeping the Time: The Poisson Distribution
10
The Uniform and Normal Distributions: So Many Possibilities!
- Comparing Discrete and Continuous Distributions
- Working with the Uniform Distribution
- Understanding the Normal Distribution
11
Sampling Techniques and Distributions
- Sampling Techniques: Choosing Data from a Population
- Sampling Distributions
- The Central Limit Theorem
12
Confidence Intervals and the Student’s t-Distribution
- Almost Normal: The Student’s t-Distribution
13
Testing Hypotheses about the Population Mean
- Applying the Key Steps in Hypothesis Testing for a Single Population Mean
14
Testing Hypotheses about Multiple Population Means
- Getting to Know the F-Distribution
- Using ANOVA to Test Hypotheses
15
Testing Hypotheses about the Population Mean
- Staying Positive with the Chi-Square Distribution
- Testing Hypotheses about the Population Variance
- Practicing the Goodness of Fit Tests
- Testing Hypotheses about the Equality of Two Population Variances
16
Simple Regression Analysis
- The Fundamental Assumption: Variables Have a Linear Relationship
- Defining the Population Regression Equation
- Estimating the Population Regression Equation
- Testing the Estimated Regression Equation
- Using Statistical Software
- Assumptions of Simple Linear Regression
17
Multiple Regression Analysis: Two or More Independent Variables
- The Fundamental Assumption: Variables Have a Linear Relationship
- Estimating a Multiple Regression Equation
- Checking for Multicollinearity
18
Forecasting Techniques: Looking into the Future
- Defining a Time Series
- Modeling a Time Series with Regression Analysis
- Forecasting a Time Series
- Changing with the Seasons: Seasonal Variation
- Implementing Smoothing Techniques
- Comparing the Forecasts of Different Models
19
Ten Common Errors That Arise in Statistical Analysis
- Designing Misleading Graphs
- Drawing the Wrong Conclusion from a Confidence Interval
- Misinterpreting the Results of a Hypothesis Test
- Placing Too Much Confidence in the Coefficient of Determination (R2)
- Assuming Normality
- Thinking Correlation Implies Causality
- Drawing Conclusions from a Regression Equation when the Data do not Follow the Assumptions
- Including Correlated Variables in a Multiple Regression Equation
- Placing Too Much Confidence in Forecasts
- Using the Wrong Distribution
20
Ten Key Categories of Formulas for Business Statistics
- Summary Measures of a Population or a Sample
- Probability
- Discrete Probability Distributions
- Continuous Probability Distributions
- Sampling Distributions
- Confidence Intervals for the Population Mean
- Testing Hypotheses about Population Means
- Testing Hypotheses about Population Variances
- Using Regression Analysis
- Forecasting Techniques
The Art and Science of Business Statistics
- Understanding the Daily Step Counts of Your Club Members
- Keeping Track of Visitors on a Personal Blog
- Assessing the Level of Student Participation in Various Extracurricular Activities
- Conducting a Survey
- Visualizing the Temperature Fluctuations
- Visualizing Exam Grades Distribution
Pictures Tell the Story: Graphical Representations of Data
- Calculating the Relative Frequency
- Figuring the Class Width
- Calculating the Cumulative Frequency
- Illustrating a Cumulative Frequency
- Illustrating a Relative Frequency
- Illustrating a Frequency Distribution
- Representing Fluctuations of Gold Price
Finding a Happy Medium: Identifying the Center of a Data Set
- Calculating the Arithmetic Mean
- Calculating the Weighted Geometric Mean
- Calculating the Weighted Arithmetic Mean
- Representing Positively Skewed Data Set
- Representing Negatively Skewed Data Set
- Representing Symmetrical Data Set
- Discovering the Mode
Searching High and Low: Measuring Variation in a Data Set
- Calculating Percentiles
- Finding Quartiles
- Finding Coefficient of Variation
Measuring How Data Sets Are Related to Each Other
- Calculating the Sample Covariance
Probability Theory: Measuring the Likelihood of Events
- Performing Set Operations
- Looking at Types of Probabilities
- Finding Unconditional Probabilities
- Finding the Conditional Probability
- Calculating the Multiplication Rule
- Calculating the Complement Rule
Probability Distributions and Random Variables
- Calculating the Probability Distribution
- Calculating the Expected Value
The Binomial, Geometric, and Poisson Distributions
- Calculating the Binomial Probability
- Representing the Binomial Distribution
- Calculating Geometric Probabilities
- Computing Poisson Probabilities
The Uniform and Normal Distributions: So Many Possibilities!
- Representing the Discrete Distribution
- Uniform Distribution: Computing Variance and Standard Deviation
- Calculating the Expected Value
- Computing Uniform Probabilities with Formulas
Sampling Techniques and Distributions
- Portraying Sampling Distributions Graphically
- Calculating the Moments a Sampling Distribution
- Converting Random Variable into a Standard Normal Random Variable
Confidence Intervals and the Student’s t-Distribution
- Graphing the t-distribution
- Calculating the Variance of a t-distribution
Testing Hypotheses about the Population Mean
- Graphing the Standard Normal Distribution
- Determining the Two-Tailed Hypothesis Test
- Determining the Test Statistic
Testing Hypotheses about Multiple Population Means
- Calculating the Error Sum of Squares (SSE)
Testing Hypotheses about the Population Mean
- Testing Hypotheses about the Population Variance
Simple Regression Analysis
- Calculating the Slope of a Line from Two Given Points
- Calculating Coefficients and Predicting Sales Revenue in Simple Linear Regression
- Calculating Total Sum of Squares (TSS)
Multiple Regression Analysis: Two or More Independent Variables
- Visualizing the Test Statistics
Forecasting Techniques: Looking into the Future
- Analyzing User Growth Trends
