Statistics Masterclass- Stats Using SPSS, Excel, R Python

Statistics Masterclass: Stats Using SPSS, Excel, R & Python, available at $59.99, has an average rating of 4.15, with 107 lectures, based on 27 reviews, and has 1198 subscribers.

You will learn about Learn Univariate and Multivariate Statistics from Scratch with beginning from scratch Manual calculation of basic and advanced statistics in a state by step manner along with a conceptual explanation Demonstration of calculation using IBM SPSS Statistics to boost your confidence like how Researchers do statistics Demonstration of calculation using R-Package to boost your confidence like how Researchers and Data Scientists do statistics Demonstration of calculation using Python to boost your confidence like how Programmers and Data Scientists do statistics This course is ideal for individuals who are Anyone looking to master statistics for research and data science It is particularly useful for Anyone looking to master statistics for research and data science.

Enroll now: Statistics Masterclass: Stats Using SPSS, Excel, R & Python

Summary

Title: Statistics Masterclass: Stats Using SPSS, Excel, R & Python

Price: $59.99

Average Rating: 4.15

Number of Lectures: 107

Number of Published Lectures: 107

Number of Curriculum Items: 107

Number of Published Curriculum Objects: 107

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Learn Univariate and Multivariate Statistics from Scratch with beginning from scratch

Manual calculation of basic and advanced statistics in a state by step manner along with a conceptual explanation

Demonstration of calculation using IBM SPSS Statistics to boost your confidence like how Researchers do statistics

Demonstration of calculation using R-Package to boost your confidence like how Researchers and Data Scientists do statistics

Demonstration of calculation using Python to boost your confidence like how Programmers and Data Scientists do statistics

Who Should Attend

Anyone looking to master statistics for research and data science

Target Audiences

Anyone looking to master statistics for research and data science

Welcome to this course on Discovering Statistics!

Statistics is the foundation of all other statistical disciplines. It helps us to understand the world around us. It is the first step towards understanding the world. A statistician must be able to calculate basic statistics. He can use these statistics to analyze and interpret data. He can also use them to make decisions.

This is a comprehensive five in one course in statistics covering the following:

Manual calculation of basic and advanced statistics in a state by step manner along with a conceptual explanation

Demonstration of calculation using Excel to boost your confidence like how managers do statistics

Demonstration of calculation using IBM SPSS Statistics to boost your confidence like how Researchers do statistics

Demonstration of calculation using R-Package to boost your confidence like how Researchers and Data Scientists do statistics

Demonstration of calculation using Python to boost your confidence like how Programmers and Data Scientists do statistics

Pedagogy:

The course will be delivered in an easy-to-understand and self-explanatory manner. The course will provide you with the skills to learn the concepts of statistics. The course will help you to master the use of statistical software and to understand the concepts of statistics.

The course will start with the basics of statistics. It will explain how to calculate the most important statistics manually. Then, it will show how to calculate them using four software i.e., Excel, SPSS, R, and Python. Finally, it will give a detailed conceptual explanation of statistics.

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That means you can enroll now and start learning today, and if you’re not satisfied, within 30 days of your purchase, you can take your full refund! No questions asked!

But don’t worry, you don’t have to wait 30 days to start enjoying the results!

Course Curriculum

Chapter 1: Introduction to Statistics

Lecture 1: Defining Statistics

Lecture 2: Statistics Defined as a Summary Feature of Sample

Lecture 3: History of Statistics

Lecture 4: Evolutions and Revolutions in Statistics

Lecture 5: Types of Statistics: Descriptive vs. Inferential

Lecture 6: Types of Statistics: Parametric vs. Non-Parametric Statistics

Lecture 7: What We Mean by Sample and Population?

Lecture 8: What We Mean by Statistics and Parameter?

Lecture 9: Understanding Data, Dataset, Values, Observations, Variables

Lecture 10: Understanding Data, Observations, Values, Cases through Covid Data

Lecture 11: Understanding Data, Dataset, Observations, Variables and Values: ESS Example

Chapter 2: Scales of Measurement

Lecture 1: Introduction to Scales of Measurement

Lecture 2: What are Nominal Scales?

Lecture 3: What are Ordinal Scales?

Lecture 4: What are Interval Scales?

Lecture 5: Are Likert Scales Ordinal or Interval Scales?

Lecture 6: What are Ratio Scales?

Lecture 7: Summing Up Scales of Measurement

Chapter 3: Understanding Frequency Distribution

Lecture 1: What is a Frequency Distribution?

Lecture 2: Understanding Frequency Distribution Through a Problem: Favourite Eating Option

Lecture 3: Creating a Frequency Distribution in Excel

Lecture 4: Creating Frequency Distribution in SPSS

Chapter 4: Introduction to R Language

Lecture 1: How to Clear R Console _Useful R Commands

Lecture 2: Getting Familiar with R Environment

Lecture 3: Assignment Operators in R

Lecture 4: Creating Numeric and String Vectors in R

Lecture 5: Importing Excel File in R using File import wizard

Lecture 6: Importing Excel File using Setwd and read.csv commands

Lecture 7: How to Seek help and search in R?

Lecture 8: Counting in R Table Function

Chapter 5: Measures of Central Tendency

Lecture 1: Understanding Geometric Progression and Learning to Create it

Lecture 2: Understanding Formula and Manual Calculation of Geometric Mean

Lecture 3: GM exercise Calculate GM using GEOMEAN nth Root and Antilog formula

Lecture 4: Calculating Geometric Mean Using SPSS

Lecture 5: How to calculate Log of a number using log Table

Lecture 6: Harmonic Mean Definition and Formula

Lecture 7: Properties of Harmonic Mean

Lecture 8: Calculation of Harmonic Mean: Manual Calculation with Excel and SPSS Demo

Lecture 9: Calculation of Arithmetic Mean for Grouped Data

Lecture 10: Calculation of Arithmetic Mean for Grouped Data Using SPSS

Lecture 11: Median Calculation for Discrete and Continuous Data

Lecture 12: Mode Calculation for Grouped Data

Lecture 13: Weighted Mean

Chapter 6: Positional Averages: Quartiles, Percentiles and Deciles

Lecture 1: What are positional averages Quartiles Percentiles Deciles

Lecture 2: Understanding Quartiles

Lecture 3: Understanding Formula of Quartiles for Grouped and Ungrouped Data

Lecture 4: Calculation of Quartiles_Understading formula

Lecture 5: Calculation of Quartiles for Even Ungrouped Series

Lecture 6: Calculation of Quartiles for Odd Ungrouped Series

Lecture 7: Quartile Calculation for Class Interval Data

Lecture 8: Calculation of Quartiles in Excel

Lecture 9: Calculation of Quartiles in SPSS

Lecture 10: Merits and Demerits of Quartiles

Lecture 11: Understanding Deciles

Lecture 12: Understanding Formula for Deciles

Lecture 13: Calculation of Deciles for Ungrouped Data

Lecture 14: Decile Calculation for Continuous Data

Lecture 15: Decile Calculation in SPSS

Lecture 16: Percentiles Definition and Formula

Lecture 17: Percentile Calculation of Discrete Data

Lecture 18: Percentile Calculation by Anderson et al. Method

Lecture 19: Percentile Calculation by Inclusion vs. Exclusion Method

Lecture 20: Formula for Percentiles for Continuous Data

Chapter 7: Measures of Dispersion

Lecture 1: Understanding Measures of Dispersion

Lecture 2: Types of Measures of Dispersion: Absolute Vs. Relative

Lecture 3: Analytical Strategy for Measures of Dispersion

Lecture 4: Understanding Range, its Usage and Calculation

Lecture 5: Calculation of Range and Coefficient of Range for Individual Series

Lecture 6: Calculation of Range for Discrete Series

Lecture 7: Calculation of Range for Continuous Series

Lecture 8: Calculation of Range in Excel and SPSS

Lecture 9: Merits and Demerits of range

Lecture 10: Quartile Deviation Definition and Manual Calculation with Excel and SPSS Demo

Lecture 11: Merits and Demerits of Quartile Deviation

Lecture 12: Understanding the Concept and Formula of Mean Deviation

Lecture 13: Calculation of Mean Deviation for Individual Series

Lecture 14: Calculation of Mean Deviation for Discrete and Continuous Series

Lecture 15: Standard Deviation: Understanding Sample and Population Standard Deviation

Lecture 16: Variance and Coffiecient of Variation: Definition and Formula

Lecture 17: Calculation of Sample Standard Deviation for Ungrouped Data

Lecture 18: Calculation of Sample Standard Deviation for Ungrouped Data in Excel

Lecture 19: Calculation of Sample Standard Deviation for Ungrouped Data Using SPSS

Lecture 20: Manual Calculation of Population Std Dev for Ungrouped Data

Lecture 21: Calculation of Population Standard Deviation Using Excel

Lecture 22: Calculation of Population Standard Deviation Using SPSS

Chapter 8: Measures of Distribution: Skewness and Kurtosis

Lecture 1: What is a Statistical Distribution?

Lecture 2: Understanding Symmetric and Asymmetric Distributions

Lecture 3: Normal Distribution: Explanation and Properties

Lecture 4: What is Skewness – Understanding Positive and Negative Skewness

Lecture 5: Properties of Skewness

Lecture 6: Measures of Skewness

Lecture 7: Pearsons Coefficient of Skewness

Instructors

Scholarsight Learning
Courses in High Impact Research & Technology

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1 stars: 1 votes

2 stars: 1 votes

3 stars: 0 votes

4 stars: 7 votes

5 stars: 18 votes

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