Data Science Course > Regression Analysis and Correlation

Least Square and Residual Analysis in Data Science

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  • Least Square Method: The Least Square method is a form of regression analysis that calculates a linear combination of independent variables (X) to minimize the sum squared residuals (S).
  • Residual Analysis: Residual analysis is a statistical technique for finding patterns in data, which can be used to identify outliers and other important features.

The Least Squares Regression Line is the line that minimises the squared sum of residuals. By subtracting y from y, the residual is the vertical distance between the observed and predicted points.

Least square analysis is used to find the best fit of a function to its data set. It is a linear equation that minimizes the squared distance between the observed and fitted values.

There are many ways in which AI can be used to help data science projects:

  • Generating insights: AI can be used to generate insights that might not have been reached by human scientists.
  • Automating tasks: Data science projects often require a lot of repetitive work and the use of AI tools can automate this process.
  • Generating data sets: Data science projects often require a lot of data and the use of AI tools can generate these data sets at scale.
  • Data augmentation: Data science projects often require additional information about the dataset, such as demographics, geographies, etc.,

The following are some of the advantages of this method: Many statistical software packages that do not offer maximum likelihood estimations may include non-linear least squares software. It has a broader use than maximal likelihood.

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SHAIKH TOHEED
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good but practical knowledge not perfect

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PUSHPAK DILLIP MALI
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The content and teaching style is excellent. But there is slight issue is that video quality is kind of poor the dashboard is blur on 720mp on PC.

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Very useful all data

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Syed Mohammad Qaiser Rizvi
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Way of teaching is very good. I want to get PPT which he is using to teach us ?

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Anuja Bagadi
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It is a very interactive and useful course..

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All courses is very useful for which are looking free courses and gaining knowledge

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Course Content

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  2. How to use LearnVern
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  1. Introduction to Data Measurement in Data Science
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  2. Types of Variables in Data Science
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  3. Levels of Data Measurement in Data Science
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  1. Charts and Graphs in Data Science
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  2. Frequency Distribution in Data Science
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  3. Bar Graphs and Pie Charts in Data Science
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  4. Summarizing Quantitative Data in Data Science
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  5. Cummulative Distribution in Data science
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  6. Histogram and Scatter Plot in Data Science
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  7. Cross Tabulation in Data Science
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  1. Measures of Central Tendency and Variability in Data Science
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  2. Mean and Percentiles in Data Science
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  3. Quartiles in Data Science
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  4. Measures of Variability in Data Science
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  5. Variance and Standard Deviation in Data Science
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  6. Application of Standard Deviation in Data Science
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  7. Z-score and Co-efficient of Variation in Data Science
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  8. Measures of Central Tendency for Grouped Data in Data Science
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  9. Standard Deviation and Variance for Grouped Data in Data Science
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  2. Kurtosis in Data Science
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  3. Errors in Statistical Hypothesis in Data Science
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  4. Statistical Inference for Single Population in Data Science
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  5. Testing Example in Data Science
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  6. Statistical Inference with Two Population in Data Science
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  1. R Programming in Data Science
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  2. Data Types in R in Data Science
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  6. One way ANOVA Example in Data Science
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  7. Two way ANOVA Example in Data Science
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  1. Understanding Datamining in Data Science
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  1. Introduction to Simple Linear Regression and Correlation in Data Science
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  2. Least Square and Residual Analysis in Data Science
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  4. Hypothesis and Co-Efficient of Determination in Data Science
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  5. Regression Example - Creating Model in Data Science
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  7. Residual Analysis of Model Part 2 in Data Science
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  8. Residual Analysis of Model Part 3 in Data Science
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  1. Understanding Clustering in Data Science
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  3. Example of Hierarchial Clustering in Data Science
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  4. K Means Clustering in Data Science
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  5. Example of K Means Clustering in Data Science
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  1. Theoretical Concepts of Dashboard
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  2. Creating Pivots and Preparing Charts
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  3. Slicers in Dashboard
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  4. Preparing Final Dashboard
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  5. Presenting Dashboard
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