Introductory Probability and Statistics

Introductory Probability and Statistics, available at $39.99, has an average rating of 3.7, with 88 lectures, based on 11 reviews, and has 116 subscribers.

You will learn about Learn essentials of statistical concepts Learn basic concepts of statistics like mean median mode and standard deviation Learn how to use excel for doing statistical analysis Learn basic concepts of probability Learn various approaches of assinging probability to events Learn revising probability This course is ideal for individuals who are Students who are looking to have foundations in statistics or Students who are doing basic courses in statistics or Students of business studies or Students who are thinking of doing data analysis It is particularly useful for Students who are looking to have foundations in statistics or Students who are doing basic courses in statistics or Students of business studies or Students who are thinking of doing data analysis.

Enroll now: Introductory Probability and Statistics

Summary

Title: Introductory Probability and Statistics

Price: $39.99

Average Rating: 3.7

Number of Lectures: 88

Number of Published Lectures: 88

Number of Curriculum Items: 88

Number of Published Curriculum Objects: 88

Original Price: $129.99

Quality Status: approved

Status: Live

What You Will Learn

Learn essentials of statistical concepts

Learn basic concepts of statistics like mean median mode and standard deviation

Learn how to use excel for doing statistical analysis

Learn basic concepts of probability

Learn various approaches of assinging probability to events

Learn revising probability

Who Should Attend

Students who are looking to have foundations in statistics

Students who are doing basic courses in statistics

Students of business studies

Students who are thinking of doing data analysis

Target Audiences

Students who are looking to have foundations in statistics

Students who are doing basic courses in statistics

Students of business studies

Students who are thinking of doing data analysis

This is an introductory course in probability and statistics. This course helps to serve as a foundation for higher levels of a statistics course, particularly inferential statistics and research methods course.

This course provides 85 video lectures and it also teaches you how to estimate the probability and do statistical analysis using spreadsheets.

The course is structured into 10 sections:

1. What is Statistics- Meaning of Statistics in Singular & Plural Sense, Characteristics of Stat, Nature & Scope, Types -Descriptive & Inferential, Distrust and other limitations of Statistics.

2. Descriptive Statistics- Measures of Central Tendency, Measures of Dispersion and Measures of Shape

3. Probability- Introduction to Probability, Fundamental Rules of Counting, Events & and Sample Space, Set & Venn Diagram, Approaches to Probability, Addition Rule, Multiplication Rule, The Law of Total Probability, Bayes’ Theorem.

4. Random Variable- Meaning, Discrete Random Variable, Continous Random Variable, Expected Value, Variance, Probability distributions- Binomial, Poisson, Normal Distribution

5. Sampling Distribution- Population & Sample, Parameters & Statistics, Sampling Distribution of Mean, Types of Sampling, Non-Probability Sampling, Theorems of Sampling Distribution 

6. Estimation -Estimator & Estimate, Qualities of a good estimator,  Point Estimate, Interval Estimate, the concept of standard error Confidence Interval construction, Sample size determination.

7. Hypothesis Testing- Introduction, Meaning of Null and Alternate Hypothesis, Two-tail & One-tail Tests, Types of Error, Hypothesis Testing Procedure, Hypothesis Test of a Population Mean: Large and Small Sample, Hypothesis Test of  Population Mean: Two Independent Samples, Hypothesis Test of a Population Mean: Paired t-test, Hypothesis Test of Two Population Variance: F-test.   

8. ANOVA: One-Way ANOVA, One- Way ANOVA using Excel, Two-Way ANOVA without replication using excel, Two-Way ANOVA with replication using excel, N-Way ANOVA.

9. Correlation Analysis -Intro to Concept, Scatter Plot, Karl Pearson Coefficient of Correlation, Spearman Rank Order Correlation, Probable Error, Hypothesis Testing of Population Coefficient of Correlation.

10. Regression Analysis- Introduction to Regression, Regression Line, Assumptions of the Classical Linear Regression Model, OLS Method, Coefficient of Determination (R Square), Standard Error of OLS estimates, Confidence Interval for alpha and beta, Hypothesis testing, Two-Tail, One -Tail, Regression Analysis Solved Example, Forecasting With Regression Model, Regression Estimation Using Excel.

This course will teach you statistics in a real sense and help you to remove your all doubts relating to statistics and probability.

If you want really learn probability and statistics in a simple way, you must enrol for this course.

Course Curriculum

Chapter 1: Statistics

Lecture 1: Introductory Welcome Lecture

Lecture 2: Meaning of Statistics

Lecture 3: Characteristics of statistics

Lecture 4: Nature of Statistics

Lecture 5: Scope of Statistics

Lecture 6: Types of Statistics

Lecture 7: Limitations of Statistics

Chapter 2: Descriptive Statistics

Lecture 1: Introduction

Lecture 2: Measures of Central Tendency: Mean Median Mode

Lecture 3: Measures of Dispersion

Lecture 4: Measures of Shape

Lecture 5: Descriptive Statistics Using MS Excel

Chapter 3: Probability

Lecture 1: Introduction to Probability

Lecture 2: Fundamental Rules of Counting

Lecture 3: Event and Sample Space

Lecture 4: Set and Venn Diagram

Lecture 5: Approaches to Probability

Lecture 6: Addition Rule

Lecture 7: Multiplication Rule Part I

Lecture 8: Multiplication Rule Part II

Lecture 9: The Law of Total Probability

Lecture 10: Bayes Theorem

Chapter 4: Random Variable and its Probability Distribution

Lecture 1: Meaning of Random Variable

Lecture 2: Types of Random Variable

Lecture 3: Binomial Distribution

Lecture 4: Expected value and variance of Binomial Distribution

Lecture 5: Binomial using Excel

Lecture 6: Poisson Distribution

Lecture 7: Normal Distribution

Lecture 8: Normal Distribution:Finding the Area

Lecture 9: Probability of Normal Distribution using Excel

Chapter 5: Sampling Distribution

Lecture 1: Introduction to Sampling

Lecture 2: Population and Sample

Lecture 3: Parameter and Statistics

Lecture 4: Types of Sampling

Lecture 5: Systematic & Stratified Sampling

Lecture 6: Non-Probability Sampling

Lecture 7: Theorems of Sampling Distribution

Chapter 6: Estimation and Confidence Interval

Lecture 1: Introduction to Estimation

Lecture 2: Estimator and Estimate

Lecture 3: Qualities of a good Estimator

Lecture 4: Types of Estimation

Lecture 5: Confidence Interval of Population Mean of Large Sample

Lecture 6: Sample Size Determination

Chapter 7: Hypothesis Testing

Lecture 1: Introduction to Concept of Hypothesis Testing

Lecture 2: Two Tail Test

Lecture 3: One Tail Test

Lecture 4: Hypothesis testing procedure

Lecture 5: Hypothesis Test of a Pop Mean: Large Sample

Lecture 6: Hypothesis Test of a Pop Mean: Small Sample

Lecture 7: Hypothesis Test of a Population Mean:Two Independent Samples (Unequal variance)

Lecture 8: Using Excel for Hypothesis Testing

Lecture 9: Hypothesis Test of a Population Mean:Two Independent Samples (equal variance)

Lecture 10: hypothesis testing of population proportion

Lecture 11: Hypothesis Test of a Population Mean: Paired t test

Lecture 12: Paired t test using Excel

Lecture 13: Hypothesis testing population variance

Lecture 14: Hypothesis testing of two population variance

Lecture 15: Two population variance Using Excel

Chapter 8: ANOVA

Lecture 1: Introduction to ANOVA

Lecture 2: One-Way ANOVA

Lecture 3: One-Way ANOVA using Excel

Lecture 4: Two-Way ANOVA without replication

Lecture 5: using excel

Lecture 6: Two-Way ANOVA with replication using excel

Chapter 9: The Chi Square Test

Lecture 1: Introduction to Chi-square test

Lecture 2: Goodness of Fit Test: The Chi square test

Lecture 3: Chi Square Test of Independence

Chapter 10: Correlation Analysis

Lecture 1: Introduction to Correlation

Lecture 2: Scatter Plot

Lecture 3: Karl Pearson Coefficient of Correlation

Lecture 4: correlation matrix

Lecture 5: Spearman Rank Order Correlation

Lecture 6: concept of probable Error

Lecture 7: Hypothesis testing of Population Coefficient of Correlation

Chapter 11: Regression Analysis

Lecture 1: Introduction to Regression

Lecture 2: Regression Line

Lecture 3: Assumptions of Classical Linear Regression Model

Lecture 4: OLS Method

Lecture 5: Coefficient of Determination (R Square)

Lecture 6: Standard Error of OLS Estimates

Lecture 7: Confidence Interval for Estimated Alpha and Beta

Lecture 8: Hypothesis Testing

Lecture 9: Two Tail Test

Lecture 10: One Tail Test

Lecture 11: Regression Analysis Solved Example

Lecture 12: Forecasting with Regression Model

Lecture 13: Regression Analysis Solved Example using Excel

Instructors

Ash Narayan Sah
Data Scientist

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2 stars: 2 votes

3 stars: 2 votes

4 stars: 2 votes

5 stars: 4 votes

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