Udemy # [-0% Off] Introduction to Probability and Statistics -Year 2022 Course Coupon

Duration: 16.0 hours
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16 Hours Course especially designed for the University Students who want to become Expert from very Basics Level.

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### Description

In this course, everything has been broken down into a simple structure to make learning and understanding easy for you.

Probability and statistics help to bring logic to a world replete with randomness and uncertainty. This course will give you the tools needed to understand data, science, philosophy, engineering, economics, and finance. You will learn not only how to solve challenging technical problems, but also how you can apply those solutions in everyday life and can solve many problems from the books for your exams.

With examples from our daily life and and from the famous books on these topics, you will gain a strong foundation for the study of statistical inference, stochastic processes, randomized algorithms, and other subjects where probability is needed.

As this course is specially designed for the University and High School Students who are facing difficulties in their studies and for those who want to boost up their skills in this field.

With this 16 Hours Probability and Statistics course,you can understand from very basic level and can become expert in this course.

Textbooks used for this course

1. Elementary Statistics by ALAN G. BLUMAN.(8th Edition)

2. Probability and Statistics for Engineers and Scientists by WALPOLE & MYERS YE.(9th Edition)

Lecture 1

• What is meant by Statistics?

• Formal Definition of Statistics and types of Statistics.

• Uses of Statistics?

• Population versus Sample.

Why take a sample instead of studying every member of the population?

Usefulness of a Sample in learning about a Population.

• Variables

Types of variables

Discrete versus Continuous Variables

Summary of Types of Variables

• Frequency Table

• Relative Class Frequencies

• Bar Charts

• Frequency Distribution

EXAMPLE – Constructing Frequency Distributions: Quantitative Data

Constructing a Frequency Table - Example

• Class Intervals and Midpoints with Examples

• Relative Frequency Distribution

• Graphic Presentation of a Frequency Distribution

• Histogram

Histogram Using Excel

• Frequency Polygon

• Cumulative Frequency Distribution

Lecture 2

• Numerical Descriptive Measures (Measures of location and dispersion)

• Central Tendency

• Population Mean

EXAMPLE – Population Mean

• Sample Mean

EXAMPLE – Sample Mean

• Properties of the Arithmetic Mean

• The Median

Properties of the Median

EXAMPLES - Median

• The Mode

Example – Mode

• The Relative Positions of the Mean, Median and the Mode

• The Geometric Mean

EXAMPLE – Geometric Mean

• DISPERSION

Samples of Dispersions

Types of Dispersion

• Examples

Range

Mean Deviation

Variance and Standard Deviation

Sample Variance

• The Empirical Rule

• Coefficient of Variance (C.V)

Examples

Lecture 3

• Coefficient of Variance (C.V)

Example

• Mean

Finding the Mean for group data

• Median

Finding the Median for group data.

• Mode

Finding the Mode for group data.

• Finding the Variance & Standard Deviation for Grouped Data

Examples

• Skewness

Examples

• Pearson coefficient of Skewness (PC)

Examples

Lecture 4

• Permutation

Permutation Theorem #1

Solve the above example by theorem.

Permutation Examples

Permutation Theorem #2

• Combination

Examples

• Difference between permutation & combination

• Definitions

Experiment

Outcome

Event

• Classical Probability

Examples

• Mutually Exclusive and Independent Events

• Empirical Probability

Example

Example

• Complement Rule

Example

Lecture 5

• Conditional Probability

Formulae

Examples

• Special Rule for Multiplication

Example

• General Rule for Multiplication

Example

• Contingency Table

Example

• Generalized Conditional Probability

Example

• Bayes’ rule for conditional probability

Example

Lecture 6

• What is a Probability Distribution?

• Probability Distribution of Number of Heads Observed in 3 Tosses of a Coin

• Characteristics of a Probability Distribution

• Random Variables

Types of Random Variables

Discrete Random Variables – Examples

Continuous Random Variables - Examples

• Prob. Mass function (pmf)

• Probability Distribution

The Mean of a Discrete Probability Distribution

The Variance, and Standard Deviation of a Discrete Probability Distribution

Mean, Variance, and Standard Deviation of a Discrete Probability Distribution – Example

Mean of a Discrete Probability Distribution - Example

Variance and Standard Deviation of a Discrete Probability Distribution – Example

• Discrete Probability Distribution

Binomial Probability Distribution.

Example

Poisson Probability Distribution.

Example

-ve binomial and Geometric Probability Distribution

Example

Lecture 7

• Probability density function (PDF)

Properties of PDF

Example

• Cumulative distribution function (CDF)

Properties of CDF

Example

• The Family of Uniform Distributions

• The Uniform Distribution

Mean and Standard Deviation

Examples

Lecture 8

• Normal probability distribution

Examples

Characteristics of a Normal Probability Distribution

The Normal Distribution – Graphically

The Normal Distribution – Families

The Standard Normal Probability Distribution

• Areas Under the Normal Curve

• Z-TABLE

• The Empirical Rule

• Normal Distribution – Finding Probabilities

Examples

• Using Z in Finding X Given Area –

Examples

• Alternate Method

• Simple Linear Regression

• Simple Linear Regression Model

Graph

• Simple Linear Regression Equation

Positive, Negative and Non Relationship

• Estimation Process

• Least Squares Method

Y-Intercept for the Estimated Regression Equation

Lecture 9

• Correlation

Examples

• Hypothesis

What is Hypothesis Testing?

Hypothesis Testing Steps

• The null and alternative hypothesis

• One and Two-tailed test

Lecture 10

• Important Things to Remember about H0 and H1

• Left-tail or Right-tail Test?

• Parts of a Distribution in Hypothesis Testing

• One-tail vs. Two-tail Test

• Test of Single POP Mean (σ Unknown)

Test 1 and Test 2

• Testing for a Population Mean with a Known Population Standard Deviation

Examples

• Estimation and Confidence Intervals

• Interval Estimates

Factors Affecting Confidence Interval Estimates

Confidence Interval Estimates for the Mean

When to Use the z or t Distribution for Confidence Interval Computation

Confidence Interval for the Mean – Example using the t-distribution

• Student’s t-distribution Table

• Two-sample Tests of Hypothesis

Comparing two populations

Comparing two populations (Mean of Independent Samples)

Comparing Population Means with Unknown Population Standard Deviations (the Pooled t-test)

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