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

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

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