Statistics For Data Science Course > Fundamentals of Regression Analysis

Basics of Linear Regression Analysis

15.3k

More than just fitting a linear line through a cloud of data points is involved in linear regression analysis. It has three stages: (1) examining the data for correlation and directionality, (2) estimating the model, i.e. fitting the line, and (3) evaluating the model's validity and utility.

A linear regression model is predicated on four assumptions: Linearity refers to the relationship between X and the mean of Y. Homoscedasticity: For every value of X, the variance of the residual is the same. Independent observations: Observations are not reliant on one another.

y = b0 + b1*x + e is the linear regression mathematical formula, where b0 and b1 are known as the regression beta coefficients or parameters: The anticipated value when x = 0 is b0, which is the intercept of the regression line. The slope of the regression line is b1.

Linear Regression is a supervised machine learning technique with a continuous and constant slope projected output. Rather than aiming to classify data into categories, it's used to predict values within a continuous range (e.g. sales, price) (e.g. cat, dog).

These are some considerations to make when selecting a linear model:

  • Comparing linear models for the same dataset is the only way to go.
  • Look for a model with a high R2 adjusted.
  • Ascertain that the residuals in this model are evenly distributed around zero.
  • Ascertain that the model's faults are contained within a narrow bandwidth.

Learner's Ratings

4.7

Overall Rating

  • 75%
  • 19%
  • 6%
  • 0%
  • 0%

Reviews

M
MD Mishkat Ahsan
5

plzz provide pdf notes also

N
Neha Tiwari
5

Mujhe ye link Sanjeev Sir k ek vedio se mili.. & i am very happy to watch this vedio.. & my all doughts are clear..

A
Ankur Sharma
5

Very good course content and explanation

V
Vivek srivastava
4

Really very good course

S
Shivendra Shahi
5

Fantastic

P
Pradeep Verma
5

The best statistics course ever found . The tuitor is very friendly and her explanation is genuinely very good. Thank you very much learnvern for this beautiful course.

R
Rashmi Mahajan
5

topics covered and explained in well manner. daily life examples given which makes us easy to understand the concept. very simple and understandable way of representation.

Akshay Balkrishna Chattar
5

Great Course..

R
RITESH KUMAR
4

Great learning.

M
Mritunjoy Das
4

nice

Show More
Recommended Courses
Course Content

  1. Introduction to Statistics For Data Analysis and Data Science
    5m 22s

  1. Fundamentals of Statistics
    15m 45s

  1. Basics of Descriptive Statistics
    12m 47s
  2. Measures of Frequency
    10m 47s
  3. Measures of Central Tendency - Mean
    9m 9s
  4. Measures of Central Tendency - Median
    4m 48s
  5. Measures of Central Tendency - Mode
    5m 21s
  6. Measures of Variability - Mean Absolute Deviation
    7m 33s
  7. Measures of Variability - Variance
    7m 42s
  8. Measures of Variability - Standard Deviation
    10m 36s
  9. Measures of Position
    16m
  10. Measures of Shape - Skewness
    10m 36s
  11. Measures of Shape - Kurtosis, Box and Whisker Plot
    8m 51s
  12. Assignment : Descriptive Statistics
    3m 54s

  1. Basics of Inferential Statistics
    5m 3s
  2. Introduction to Probability
    17m 26s
  3. Basics of Probability
    21m 36s
  4. Probability Distribution
    7m 40s
  5. Discrete Probability Distribution
    19m 25s
  6. Continuous Probability Distribution
    12m 9s
  7. Uniform Distribution
    4m 16s
  8. Normal Distribution
    10m 18s
  9. Standard Normal Distribution
    9m 20s
  10. Log Normal Distribution
    3m 49s
  11. Exponential Distribution
    5m 33s
  12. Methods to Detect Outliers
    9m 26s
  13. Methods to Treat Outliers in Python
    15m 42s
  14. Feature Scaling - Normalization V/S Standardization
    10m 35s
  15. Sampling Methods
    12m 43s
  16. Sampling Distribution
    18m 30s
  17. Central Limit Theorem
    13m 28s
  18. Assignment : Inferential Statistics
    7m 18s

  1. Hypothesis Testing in Data Science
    7m 27s
  2. Null and Alternative Hypothesis
    9m 6s
  3. Making a Decision - Reject or Fail to Reject Null Hypothesis
    9m 21s
  4. Type I & Type II Errors
    12m 47s
  5. Covariance and Correlation Coefficients
    11m 47s
  6. Types of Correlation Coefficients
    15m 49s
  7. Assignment : Hypothesis Testing
    3m 53s

  1. Types of Statistical Tests
    3m 9s
  2. Z-Test: Critical Value Method
    13m 9s
  3. Examples of Z-Test: Critical Value Method
    9m 20s
  4. Z-Test: P-Value Method
    9m 33s
  5. Examples of Z-Test: P-Value Method
    9m 10s
  6. T-Test: One Sample Mean Test
    15m 16s
  7. T-Test: Paired Two Sample Mean Test
    14m 13s
  8. T-Test : Unpaired Two-Sample Mean Test
    17m 6s
  9. Two Sample Proportion Test
    14m 43s
  10. Chi-Square Test: Independence Test
    12m 57s
  11. Chi-Square Test: Goodness of Fit
    6m 51s
  12. ANOVA Test or F-Test
    24m 21s
  13. Assignment : Types of Hypothesis Testing
    5m 40s

  1. Basics of Linear Regression Analysis
    25m 45s
  2. Assumptions of Linear Regression
    10m 20s
  3. Multiple Linear Regression Analysis
    14m 50s

  1. Course Summary
    4m 30s

  1. Interview Questions
    16m 12s

  1. Career Guideline
    2m 17s
Enroll For Free