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Learn the essential techniques of statistics for data analysis using the world’s most used and famous spreadsheet program Microsoft Excel.
The [course_title] course teaches you histograms, Pareto charts, Boxplots, Bayes’ theorem, and others. You will explore the core principles of statistics and basic probability—from both the conceptual and applied perspectives. The course also focuses on statistics, basic probability, random variables, sampling and confidence intervals, and hypothesis testing. Finally, you will learn to explore these concepts and principles using the environment, functions, and visualizations of Excel.
Assessment
This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.
Certification
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Course Credit: Microsoft.
Course Curriculum
| Introduction | |||
| Online Courses from Microsoft | 00:02:00 | ||
| Welcome from Wayne Winston | 00:01:00 | ||
| Welcome from Liberty Munson | 00:01:00 | ||
| Welcome from Wayne and Liberty | 00:02:00 | ||
| Lesson 1 | |||
| Individuals and Variables | 00:01:00 | ||
| Numerical and Categorical Data | 00:01:00 | ||
| Types of Data | 00:05:00 | ||
| What is a Histogram? | 00:02:00 | ||
| Histograms and Creating a Histogram in Excel | 00:03:00 | ||
| Types of Histograms and Skewness | 00:03:00 | ||
| Computing Mean, Median, Variance, and Standard Deviation | 00:05:00 | ||
| Range, Variance, and Standard Deviation | 00:02:00 | ||
| Computing Mean, Median, Variance, and Standard Deviation | 00:05:00 | ||
| Skewness | 00:03:00 | ||
| Working with Descriptive Statistics using Analysis ToolPak | 00:02:00 | ||
| Working with Descriptive Statistics using Analysis ToolPak | 00:07:00 | ||
| Rule of Thumb and Identifying Outliers | 00:02:00 | ||
| Rule of Thumb and Identifying Outliers Example | 00:05:00 | ||
| Computing Average, Median, Variance, Standard Deviation, and Skewness | 00:08:00 | ||
| RANK, LARGE, and SMALL Functions | 00:05:00 | ||
| Geometric Mean and Compound Annual Growth Rate (CAGR) | 00:02:00 | ||
| Geometric Mean and CAGR | 00:05:00 | ||
| Boxplots | 00:02:00 | ||
| What is a Boxplot? | 00:04:00 | ||
| Creating a Boxplot | 00:02:00 | ||
| Boxplots for Comparing Multiple Populations | 00:03:00 | ||
| Boxplots for Comparing Multiple Populations on Multiple Variables | 00:02:00 | ||
| Nominal Data and Ordinal Data | 00:03:00 | ||
| Summarizing Nominal Data with a Column Chart or Pie Chart | 00:04:00 | ||
| Summarizing Ordinal Data with a Column Chart | 00:01:00 | ||
| Hierarchical Data | 00:03:00 | ||
| What is Hierarchical Data? | 00:02:00 | ||
| 80-20 Rule and Pareto Principle | 00:02:00 | ||
| 80-20 Rule and Pareto Principle 2 | 00:01:00 | ||
| Creating a Pareto Chart for Complaints Example | 00:02:00 | ||
| Lesson 2 | |||
| Introduction to Probability | 00:02:00 | ||
| Axioms of Probability | 00:01:00 | ||
| Introduction to Probability | 00:01:00 | ||
| Law of Complements | 00:01:00 | ||
| Law of Complements in Excel | 00:04:00 | ||
| Mutually Exclusive Events and Finding Prob (A or B) | 00:01:00 | ||
| Mutually Exclusive Events in Excel | 00:05:00 | ||
| Joint Probabilities | 00:02:00 | ||
| Independent Events | 00:01:00 | ||
| Definition of Independent Events | 00:08:00 | ||
| Further Examples of Independent Events | 00:05:00 | ||
| Conditional Probability | 00:05:00 | ||
| Definition of Conditional Probability | 00:06:00 | ||
| Relationship to Independent Event | 00:01:00 | ||
| Finding Joint Probabilities | 00:02:00 | ||
| Further Examples of Conditional Probability | 00:04:00 | ||
| Unions | 00:01:00 | ||
| Law of Total Probability | 00:03:00 | ||
| Law of Total Probability in Excel | 00:06:00 | ||
| Further Discussion on the Law of Total Probability | 00:02:00 | ||
| States of the World, Prior Probabilities, and Likelihoods | 00:03:00 | ||
| Bayes Theorem | 00:01:00 | ||
| Bayes Theorem Example | 00:03:00 | ||
| Bayes Theorem in Excel | 00:07:00 | ||
| Law of Total Probability | 00:03:00 | ||
| States of the World, Prior Probabilities, and Likelihoods | 00:04:00 | ||
| Bayes Theorem | 00:01:00 | ||
| Bayes Theorem Example | 00:03:00 | ||
| Another Example of Bayes Theorem | 00:02:00 | ||
| Bayes Theorem in Excel | 00:07:00 | ||
| Basic Probability Review | 00:09:00 | ||
| Lesson 3 | |||
| What is a Random Variable? | 00:01:00 | ||
| Random Variables in Excel | 00:02:00 | ||
| Discrete Random Variables | 00:01:00 | ||
| Discrete Random Variables and Probability Mass Function (PMF) | 00:02:00 | ||
| Continuous Random Variables | 00:04:00 | ||
| Independent Random Variables | 00:01:00 | ||
| Independent Random Variables in Excel | 00:03:00 | ||
| Independent Random Variables in Excel | 00:02:00 | ||
| Finding the Mean of a Discrete Random Variable | 00:01:00 | ||
| Finding the Mean of a Discrete Random Variable in Excel | 00:03:00 | ||
| Finding the Mean of a Discrete Random Variable in Excel | 00:03:00 | ||
| Finding the Variance and Standard Deviation of a Discrete Random Variable | 00:01:00 | ||
| Finding the Variance and Standard Deviation of a Discrete Random Variable in Excel | 00:04:00 | ||
| Illustrating Mean, Variance, and Standard Deviation through Simulation | 00:01:00 | ||
| Mean of Sum of Random Variable | 00:01:00 | ||
| Mean of Sum of Random Variables in Excel | 00:02:00 | ||
| Variance and Standard Deviation of Sum of Independent Random Variables | 00:04:00 | ||
| Mean, Variance, and Standard Deviation of Binomial Random Variable in Excel | 00:02:00 | ||
| Binomial Random Variable | 00:01:00 | ||
| Binomial Random Variable and the BINOM.DIST.RANGE Function | 00:03:00 | ||
| Binomial Probabilities | 00:02:00 | ||
| Examples of Computing Binomial Probabilities | 00:04:00 | ||
| Mean, Variance, and Standard Deviation of Binomial Random Variable | 00:01:00 | ||
| Mean, Variance, and Standard Deviation of Binomial Random Variable in Excel | 00:02:00 | ||
| Poisson Random Variable | 00:02:00 | ||
| Poisson Random Variable Usage | 00:02:00 | ||
| Poisson Random Variable and the POISSON.DIST Function | 00:02:00 | ||
| Poisson Probabilities | 00:01:00 | ||
| Mean, Variance, and Standard Deviation of Poisson Random Variable | 00:01:00 | ||
| Definition of Normal Random Variable | 00:01:00 | ||
| Normal Random Variable in Excel | 00:03:00 | ||
| NORM.DIST and NORM.INV Functions | 00:03:00 | ||
| Examples of Normal Probabilities | 00:02:00 | ||
| Examples of Normal Percentiles | 00:02:00 | ||
| Definition of Central Limit Theorem | 00:02:00 | ||
| Central Limit Theorem in Excel | 00:01:00 | ||
| Computing Probabilities with Central Limit Theorem | 00:04:00 | ||
| Further Central Limit Theorem Example | 00:04:00 | ||
| Definition of Z Scores | 00:03:00 | ||
| Z Scores Definition and the STANDARDIZE Function | 00:03:00 | ||
| Computation of Z Scores | 00:06:00 | ||
| Highlighting Outliers via Z Scores | 00:02:00 | ||
| Lesson 4 | |||
| Definition of Population and Population Parameters | 00:01:00 | ||
| Definition of Population and Population Parameters | 00:02:00 | ||
| Populations and Samples | 00:01:00 | ||
| Definition of Population and Population Parameters | 00:03:00 | ||
| Samples and Sample Statistics | 00:01:00 | ||
| Sampling Strategies | 00:02:00 | ||
| Simple Random Sample | 00:02:00 | ||
| Problems in Sampling | 00:03:00 | ||
| Mean, Variance and Standard Deviation of Sample Mean (Xbar) | 00:03:00 | ||
| Xbar and Mean, Variance, and Standard Deviation of Xbar | 00:03:00 | ||
| Examples of Sample | 00:02:00 | ||
| Estimate Population Proportion using P-hat | 00:02:00 | ||
| Estimate Population Proportion using P-hat in Excel | 00:02:00 | ||
| Standard Normal | 00:01:00 | ||
| Standard Normal and the .S Functions | 00:02:00 | ||
| Confidence Interval for Population Mean | 00:02:00 | ||
| Confidence Interval Examples | 00:02:00 | ||
| 95% Confidence Interval for Population Mean | 00:04:00 | ||
| Demonstration of Meaning for Confidence Interval | 00:01:00 | ||
| 95% Confidence Interval for Population Proportion | 00:02:00 | ||
| Blyth’s Formula for Proportion Confidence Interval | 00:03:00 | ||
| Sample Size for Estimating Population Mean | 00:01:00 | ||
| Sample Size for Estimating Population Mean 1589 | 00:02:00 | ||
| Sample Size for Estimating a Population Proportion | 00:03:00 | ||
| Finite Correction Formula | 00:02:00 | ||
| Finite Correction Factor | 00:04:00 | ||
| Finite Correction Formula for Estimating Population | 00:02:00 | ||
| Sample Size and the Finite Correction Factor | 00:02:00 | ||
| Lesson 5 | |||
| Null and Alternative Hypotheses | 00:02:00 | ||
| Hypothesis Testing and Null and Alternative Hypotheses | 00:02:00 | ||
| Upper One-Sided Alternative | 00:02:00 | ||
| Upper One-Sided Alternative 2 | 00:02:00 | ||
| Lower One-Sided Alternative | 00:04:00 | ||
| Two-Tailed Alternative | 00:02:00 | ||
| Choosing between One-Tailed or Two-Tailed Test | 00:01:00 | ||
| One-Tailed or Two-Tailed Test | 00:02:00 | ||
| Type I and Type II Error | 00:03:00 | ||
| Type I and Type II | 00:02:00 | ||
| More on the Null and Alternative Hypothesis | 00:02:00 | ||
| Critical Region | 00:02:00 | ||
| Critical Region in Excel | 00:03:00 | ||
| One Sample Z-Test | 00:02:00 | ||
| Critical Region | 00:02:00 | ||
| One Sample Z Test Example | 00:02:00 | ||
| P-Values | 00:02:00 | ||
| P-Values and Application to One Sample Z-Test | 00:05:00 | ||
| Critical Region, P-Values, and T.INVERSE Function | 00:02:00 | ||
| One Sample T-Test : One Tailed | 00:02:00 | ||
| One Sample T-Test : Two Tailed | 00:02:00 | ||
| One Sample T-Test in Excel | 00:02:00 | ||
| Single Sample Test for Population Proportion | 00:02:00 | ||
| Single Sample Test for Population Proport | 00:02:00 | ||
| Single Sample Test for Population Proportion Example | 00:06:00 | ||
| Single Sample Test for Population Proportion | 00:02:00 | ||
| Single Sample Test for Population Proportion in Excel | 00:02:00 | ||
| Single Sample Test for Population Proportion Example | 00:02:00 | ||
| Testing Equality of Variances | 00:02:00 | ||
| Definition of Testing Equality of Variances and the F.TEST Function | 00:02:00 | ||
| Testing Equality of Variances Example | 00:02:00 | ||
| Testing Equality of Variances | 00:02:00 | ||
| Definition of Testing Equality of Variances and the F.TEST Function | 00:01:00 | ||
| Testing Equality of Variances Example | 00:03:00 | ||
| Four Types of Tests | 00:02:00 | ||
| Which of the Four Types of Tests Should You Use and When | 00:02:00 | ||
| Two Sample Z-Test | 00:02:00 | ||
| Two Sample Z-Test in Excel | 00:02:00 | ||
| Equal Variance T-Test | 00:02:00 | ||
| Equal Variance T-Test in Excel | 00:02:00 | ||
| Unequal Variance T-Test | 00:02:00 | ||
| Unequal Variance T-Test in Excel | 00:02:00 | ||
| Idea of Pairing Samples | 00:00:00 | ||
| Idea of Pairing Samples | 00:01:00 | ||
| Two Sample Z-Test in Excel | 00:03:00 | ||
| Two Sample Z-Test | 00:02:00 | ||
| Equal Variance T-Test | 00:02:00 | ||
| Unequal Variance T-Test | 00:03:00 | ||
| Unequal Variance T-Test in Excel | 00:03:00 | ||
| Idea of Pairing Samples | 00:03:00 | ||
| T-Test Paired Two Sample | 00:02:00 | ||
| Example of T-Test Paired Two Sample | 00:03:00 | ||
| Contingency Table and Hypothesis of Independence | 00:02:00 | ||
| Example of Contingency Table and Hypothesis of Independence | 00:02:00 | ||
| Computation of Chi Squared Statistic | 00:03:00 | ||
| Computation of Chi Squared Statist | 00:02:00 | ||
| Conducting the Hypothesis Test and Computing the P-Value | 00:04:00 | ||
| Assment | |||
| Submit Your Assignment | 00:00:00 | ||
| Certification | 00:00:00 | ||
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