Statistical Analysis and Research using Excel
Statistical Analysis and Research using Excel, available at $49.99, has an average rating of 4.54, with 79 lectures, 11 quizzes, based on 185 reviews, and has 12822 subscribers.
You will learn about Statistical Analysis Research Methodology Excel Non Parametric Tests Parametric Tests This course is ideal for individuals who are Students curious about Statistics and Excel or Statictical Analysts or Researchers or Graduate Students or Data Analyst or Beginner Data Analysts or Business Management Students It is particularly useful for Students curious about Statistics and Excel or Statictical Analysts or Researchers or Graduate Students or Data Analyst or Beginner Data Analysts or Business Management Students.
Enroll now: Statistical Analysis and Research using Excel
Summary
Title: Statistical Analysis and Research using Excel
Price: $49.99
Average Rating: 4.54
Number of Lectures: 79
Number of Quizzes: 11
Number of Published Lectures: 79
Number of Published Quizzes: 11
Number of Curriculum Items: 94
Number of Published Curriculum Objects: 94
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- Statistical Analysis
- Research Methodology
- Excel
- Non Parametric Tests
- Parametric Tests
Who Should Attend
- Students curious about Statistics and Excel
- Statictical Analysts
- Researchers
- Graduate Students
- Data Analyst
- Beginner Data Analysts
- Business Management Students
Target Audiences
- Students curious about Statistics and Excel
- Statictical Analysts
- Researchers
- Graduate Students
- Data Analyst
- Beginner Data Analysts
- Business Management Students
Statistical Analysis and Research using Excel is a blended learning program of theoretical knowledge with its application in Microsoft Excel software. This course is a base to all the analytical studies and research studies. It is focused on more industry relevant examples and situations, where in you learn how you actually need to apply your research and analytical skills at workplace. This can also be considered as a foundation course if you are looking out for higher education in research and analytics.
From the work perspective, this course is more suitable to jobs for data engineering, data analyst and marketing research jobs.
Course Curriculum
Chapter 1: Overview about the Course Progress
Lecture 1: Course Preview
Chapter 2: Introduction to Statistics
Lecture 1: Base of Statistics
Lecture 2: Excel Environment Setup
Lecture 3: Concept – Basic Aggregations
Lecture 4: Lab – Basic Aggregations
Lecture 5: Statistical Analysis Using Charts
Chapter 3: Measures of Central Tendency
Lecture 1: Components of Central Tendency
Lecture 2: Concept – Arithmetic Mean
Lecture 3: Lab – Arithmetic Mean
Lecture 4: Concept – Geometric Mean
Lecture 5: Lab – Geometric Mean
Lecture 6: Concept – Harmonic Mean
Lecture 7: Lab – Harmonic Mean
Lecture 8: Concept – Median
Lecture 9: Lab – Median
Lecture 10: Concept – Quartile
Lecture 11: Lab – Quartile
Lecture 12: Concept – Percentile
Lecture 13: Lab – Percentile
Lecture 14: Concept – Decile
Lecture 15: Lab – Decile
Lecture 16: Concept – Mode
Lecture 17: Lab – Mode
Chapter 4: Measures of Dispersion
Lecture 1: Components of Dispersion
Lecture 2: Concept – Range
Lecture 3: Lab – Range
Lecture 4: Concept – Mean Deviation
Lecture 5: Lab – Mean Deviation
Lecture 6: Concept – Quartile Deviation
Lecture 7: Lab – Quartile Deviations
Lecture 8: Concept – Standard Deviations
Lecture 9: Lab – Standard Deviations
Lecture 10: Concept – Variance
Lecture 11: Lab – Variance
Chapter 5: Correlation
Lecture 1: Types of Correlation
Lecture 2: Concept – Correlation Scatter Diagrams
Lecture 3: Lab – Correlation Scatter Diagrams
Lecture 4: Concept – Pearson Correlation
Lecture 5: Lab – Pearson Correlation
Lecture 6: Concept – Rank Correlation
Lecture 7: Lab – Rank Correlation
Chapter 6: Regression
Lecture 1: Introduction to Regression Analysis
Lecture 2: Concept – Regression Analysis
Lecture 3: Lab – Regression Analysis
Lecture 4: Concept – Residuals Analysis
Lecture 5: Lab – Residuals Analysis
Lecture 6: Concept – Multiple Regression
Lecture 7: Lab – Multiple Regression
Chapter 7: Assignment – Statistical Analysis
Chapter 8: Introduction to Research
Lecture 1: Introduction to Research Analysis
Lecture 2: Methods and Process of Research
Lecture 3: Data Sources
Lecture 4: Hypothesis
Chapter 9: Managing Research Data
Lecture 1: Data Collection
Lecture 2: Questionnaire Design
Lecture 3: Measurement Scales
Lecture 4: Sampling
Lecture 5: Data Preparation
Chapter 10: Descriptive Statistics
Lecture 1: Introduction to Descriptive Statistics
Lecture 2: Concept – Components of Descriptive Statistics
Lecture 3: Lab – Components of Descriptive Statistics
Chapter 11: T Test and Z Test
Lecture 1: Concept – T Test
Lecture 2: Lab – T Test
Lecture 3: Interpretation – T Test
Lecture 4: Concept – Z Test
Lecture 5: Lab – Z Test
Lecture 6: Interpretation – Z Test
Lecture 7: Concept – Paired T Test
Lecture 8: Lab – Paired T Test
Lecture 9: Interpretation – Paired T Test
Chapter 12: Chi Square Analysis
Lecture 1: Concept – Chi Square
Lecture 2: Lab – Chi Square
Lecture 3: Interpretation – Chi Square
Chapter 13: ANOVA
Lecture 1: Concept – One-way ANOVA
Lecture 2: Lab – One-way ANOVA
Lecture 3: Interpretation – One-way ANOVA
Instructors
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EdSolver Learning
Lead with Learning
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 1 votes
- 3 stars: 30 votes
- 4 stars: 69 votes
- 5 stars: 84 votes
Frequently Asked Questions
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