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

Basics of Linear Regression Analysis

25m 45s

Assumptions of Linear Regression

10m 20s

Multiple Linear Regression Analysis

14m 50s

Quiz : Regression Analysis

4m

Course Summary

4m 30s

Interview Questions

16m 12s

Career Guideline

2m 17s

ANOVA Test or F-Test

Statistics For Data Science Course Types of Hypothesis Testing

Enroll Now For Free
Previous Next
Discussion
Previous Discussion Next
Previous
Next
  • Z-Table, T-Table , Chi-Square Table

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

Basics of Linear Regression Analysis

25m 45s

Assumptions of Linear Regression

10m 20s

Multiple Linear Regression Analysis

14m 50s

Course Summary

4m 30s

Interview Questions

16m 12s

Career Guideline

2m 17s

FAQs

The F-test is the ratio of the mean squared error between these two groups, and ANOVA isolates within-group and between-group variance.

When the standard deviation is unknown and the sample size is small, the T-test is used as a univariate hypothesis test. The F-test is a statistical test that examines if the variances of two normal populations are equal.

A researcher use the F-test to do a test for the equivalence of the two population variances. The F-test is used when a researcher wishes to see if two independent samples chosen from a normal population with the same variability are comparable.

Because variances are usually positive, F's numerator and denominator must be positive as well. As a result, F must always be a positive number. (If your ANOVA results in a negative F, double-check your calculations.)

ANOVA is used to compare three or more groups. The t-test is less likely to make a mistake. ANOVA carries a higher risk of error. The mean and standard deviation of a sample of students from classes A and B who have taken a mathematics course may differ.

Recommended Courses

Share With Friend

Have a friend to whom you would want to share this course?

Download LearnVern App

App Preview Image
App QR Code Image
Code Scan or Download the app
Google Play Store
Apple App Store
598K+ Downloads
App Download Section Circle 1
4.57 Avg. Ratings
App Download Section Circle 2
15K+ Reviews
App Download Section Circle 3
  • Learn anywhere on the go
  • Get regular updates about your enrolled or new courses
  • Share content with your friends
  • Evaluate your progress through practice tests
  • No internet connection needed
  • Enroll for the webinar and join at the time of the webinar from anywhere