Course Content

Introduction to Statistics For Data Analysis and Data Science

5m 22s

How to use LearnVern

3m 20s

Fundamentals of Statistics

15m 45s

Basics of Descriptive Statistics

12m 47s

Measures of Frequency

10m 47s

Measures of Central Tendency - Mean

9m 9s

Measures of Central Tendency - Median

4m 48s

Measures of Central Tendency - Mode

5m 21s

Measures of Variability - Mean Absolute Deviation

7m 33s

Measures of Variability - Variance

7m 42s

Measures of Variability - Standard Deviation

10m 36s

Measures of Position

16m

Measures of Shape - Skewness

10m 36s

Measures of Shape - Kurtosis, Box and Whisker Plot

8m 51s

Assignment : Descriptive Statistics

3m 54s

Quiz : Descriptive Statistics

12m

Basics of Inferential Statistics

5m 3s

Introduction to Probability

17m 26s

Basics of Probability

21m 36s

Probability Distribution

7m 40s

Discrete Probability Distribution

19m 25s

Continuous Probability Distribution

12m 9s

Uniform Distribution

4m 16s

Normal Distribution

10m 18s

Standard Normal Distribution

9m 20s

Log Normal Distribution

3m 49s

Exponential Distribution

5m 33s

Methods to Detect Outliers

9m 26s

Methods to Treat Outliers in Python

15m 42s

Feature Scaling - Normalization V/S Standardization

10m 35s

Sampling Methods

12m 43s

Sampling Distribution

18m 30s

Central Limit Theorem

13m 28s

Assignment : Inferential Statistics

7m 18s

Quiz : Inferential Statistics

12m

Hypothesis Testing in Data Science

7m 27s

Null and Alternative Hypothesis

9m 6s

Making a Decision - Reject or Fail to Reject Null Hypothesis

9m 21s

Type I & Type II Errors

12m 47s

Covariance and Correlation Coefficients

11m 47s

Types of Correlation Coefficients

15m 49s

Assignment : Hypothesis Testing

3m 53s

Quiz : Hypothesis Testing

5m

Types of Statistical Tests

3m 9s

Z-Test: Critical Value Method

13m 9s

Examples of Z-Test: Critical Value Method

9m 20s

Z-Test: P-Value Method

9m 33s

Examples of Z-Test: P-Value Method

9m 10s

T-Test: One Sample Mean Test

15m 16s

T-Test: Paired Two Sample Mean Test

14m 13s

T-Test : Unpaired Two-Sample Mean Test

17m 6s

Two Sample Proportion Test

14m 43s

Chi-Square Test: Independence Test

12m 57s

Chi-Square Test: Goodness of Fit

6m 51s

ANOVA Test or F-Test

24m 21s

Assignment : Types of Hypothesis Testing

5m 40s

Quiz : Types of Hypothesis Tests

5m

Course Summary

4m 30s

Interview Questions

16m 12s

Career Guideline

2m 17s

Multiple Linear Regression Analysis

Statistics For Data Science Course Fundamentals of Regression Analysis

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Course Content

Introduction to Statistics For Data Analysis and Data Science

5m 22s

How to use LearnVern

3m 20s

Fundamentals of Statistics

15m 45s

Basics of Descriptive Statistics

12m 47s

Measures of Frequency

10m 47s

Measures of Central Tendency - Mean

9m 9s

Measures of Central Tendency - Median

4m 48s

Measures of Central Tendency - Mode

5m 21s

Measures of Variability - Mean Absolute Deviation

7m 33s

Measures of Variability - Variance

7m 42s

Measures of Variability - Standard Deviation

10m 36s

Measures of Position

16m

Measures of Shape - Skewness

10m 36s

Measures of Shape - Kurtosis, Box and Whisker Plot

8m 51s

Assignment : Descriptive Statistics

3m 54s

Basics of Inferential Statistics

5m 3s

Introduction to Probability

17m 26s

Basics of Probability

21m 36s

Probability Distribution

7m 40s

Discrete Probability Distribution

19m 25s

Continuous Probability Distribution

12m 9s

Uniform Distribution

4m 16s

Normal Distribution

10m 18s

Standard Normal Distribution

9m 20s

Log Normal Distribution

3m 49s

Exponential Distribution

5m 33s

Methods to Detect Outliers

9m 26s

Methods to Treat Outliers in Python

15m 42s

Feature Scaling - Normalization V/S Standardization

10m 35s

Sampling Methods

12m 43s

Sampling Distribution

18m 30s

Central Limit Theorem

13m 28s

Assignment : Inferential Statistics

7m 18s

Hypothesis Testing in Data Science

7m 27s

Null and Alternative Hypothesis

9m 6s

Making a Decision - Reject or Fail to Reject Null Hypothesis

9m 21s

Type I & Type II Errors

12m 47s

Covariance and Correlation Coefficients

11m 47s

Types of Correlation Coefficients

15m 49s

Assignment : Hypothesis Testing

3m 53s

Types of Statistical Tests

3m 9s

Z-Test: Critical Value Method

13m 9s

Examples of Z-Test: Critical Value Method

9m 20s

Z-Test: P-Value Method

9m 33s

Examples of Z-Test: P-Value Method

9m 10s

T-Test: One Sample Mean Test

15m 16s

T-Test: Paired Two Sample Mean Test

14m 13s

T-Test : Unpaired Two-Sample Mean Test

17m 6s

Two Sample Proportion Test

14m 43s

Chi-Square Test: Independence Test

12m 57s

Chi-Square Test: Goodness of Fit

6m 51s

ANOVA Test or F-Test

24m 21s

Assignment : Types of Hypothesis Testing

5m 40s

Course Summary

4m 30s

Interview Questions

16m 12s

Career Guideline

2m 17s

FAQs

Multiple regression is a statistical method for examining the relationship between numerous independent variables and a single dependent variable. The goal of multiple regression analysis is to predict the value of a single dependent variable by using known independent variables.

There are various different forms of multiple regression analyses (for example, standard, hierarchical, setwise, and stepwise), but just two will be discussed here (standard and stepwise). The sort of analysis performed is determined by the researcher's study issue.

When you want to know how strong the association is between two or more independent variables and one dependent variable, you can use multiple linear regression (e.g. how rainfall, temperature, and amount of fertiliser added affect crop growth).

For example, if you're using multiple regression to predict blood pressure (the dependent variable) from independent variables like height, weight, age, and weekly activity hours, you should also include sex as an independent variable.

It's also common to use it to forecast the value of one dependent variable based on the values of two or more independent variables. Multiple regression is used when there are two or more independent variables.

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