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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ashif mahiboob yadwad
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Very good

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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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Sayali Jadhav
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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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Kashinath Myakale
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All courses is very useful for which are looking free courses and gaining knowledge

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ANKiT KUMAR BAMNiYA
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SC

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DOGALA UDAYKUMAR
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OK

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Anuj Jehta
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nice

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Course Content

  1. Understanding of Data Science
    7m 33s
  2. How to use LearnVern
    3m 20s

  1. Introduction to Data Measurement in Data Science
    10m 6s
  2. Types of Variables in Data Science
    2m 26s
  3. Levels of Data Measurement in Data Science
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  1. Charts and Graphs in Data Science
    4m 2s
  2. Frequency Distribution in Data Science
    5m 46s
  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
    7m 42s
  7. Cross Tabulation in Data Science
    6m 13s

  1. Measures of Central Tendency and Variability in Data Science
    6m 51s
  2. Mean and Percentiles in Data Science
    5m 32s
  3. Quartiles in Data Science
    2m 38s
  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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  1. Skewness in Data Science
    5m 25s
  2. Kurtosis in Data Science
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  3. Box and Whisker Plots in Data Science
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  1. Hypothesis Testing in Data Science
    6m 33s
  2. Statistical Hypothesis in Data Science
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  3. Errors in Statistical Hypothesis in Data Science
    5m 45s
  4. Statistical Inference for Single Population in Data Science
    7m 11s
  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
    5m 5s
  2. Data Types in R in Data Science
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  3. Understanding R Console and R Studio in Data Science
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  1. Analysis of Variance and Annova in Data Science
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  2. Assumption and Variation in One-Way ANOVA in Data Science
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  3. Two way ANOVA in Data Science
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  4. Summary of ANOVA in Data Science
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  5. ANOVA example 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
    7m 33s

  1. Introduction to Simple Linear Regression and Correlation in Data Science
    7m 28s
  2. Least Square and Residual Analysis in Data Science
    7m 15s
  3. Test assumption of Regression Model in Data Science
    7m 3s
  4. Hypothesis and Co-Efficient of Determination in Data Science
    4m 9s
  5. Regression Example - Creating Model in Data Science
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  6. Residual Analaysis of Model example in Data Science
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  7. Residual Analysis of Model Part 2 in Data Science
    8m 10s
  8. Residual Analysis of Model Part 3 in Data Science
    6m 38s

  1. Understanding Clustering in Data Science
    5m 58s
  2. Hierarchical Clustering in Data Science
    8m 33s
  3. Example of Hierarchial Clustering in Data Science
    13m 39s
  4. K Means Clustering in Data Science
    8m 6s
  5. Example of K Means Clustering in Data Science
    9m 41s

  1. Theoretical Concepts of Dashboard
    5m 41s
  2. Creating Pivots and Preparing Charts
    5m 36s
  3. Slicers in Dashboard
    3m 27s
  4. Preparing Final Dashboard
    8m 20s
  5. Presenting Dashboard
    3m 2s
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