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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Aditya Purohit
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Very good course & amazing cocepts & detailed explaination of each and every thing . Thanku soo much Learn Vern ...

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Abinash prusty
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Shivendra Shahi
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Amazing

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Ashmit Raj
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Very good course for begineers.

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NAGASWETHA
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good explanation

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Bharkha Bhambhani
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excellent

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Umesh Kumar Pandey
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nice

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Luckyrani Sahu
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best

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Ragini Reddy
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can explain more about level of management would help of more understanding

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Ranjit Kumar
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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
    10m 25s

  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
    9m 1s
  4. Summarizing Quantitative Data in Data Science
    7m 25s
  5. Cummulative Distribution in Data science
    8m 4s
  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
    4m 56s
  5. Variance and Standard Deviation in Data Science
    7m 30s
  6. Application of Standard Deviation in Data Science
    5m 40s
  7. Z-score and Co-efficient of Variation in Data Science
    5m 23s
  8. Measures of Central Tendency for Grouped Data in Data Science
    3m 59s
  9. Standard Deviation and Variance for Grouped Data in Data Science
    11m 21s

  1. Skewness in Data Science
    5m 25s
  2. Kurtosis in Data Science
    3m 48s
  3. Box and Whisker Plots in Data Science
    3m 45s

  1. Hypothesis Testing in Data Science
    6m 33s
  2. Statistical Hypothesis in Data Science
    6m 30s
  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
    5m 27s
  6. Statistical Inference with Two Population in Data Science
    8m 33s

  1. R Programming in Data Science
    5m 5s
  2. Data Types in R in Data Science
    3m 51s
  3. Understanding R Console and R Studio in Data Science
    7m 14s

  1. Analysis of Variance and Annova in Data Science
    3m 55s
  2. Assumption and Variation in One-Way ANOVA in Data Science
    7m 50s
  3. Two way ANOVA in Data Science
    7m 48s
  4. Summary of ANOVA in Data Science
    3m 11s
  5. ANOVA example in Data Science
    10m 50s
  6. One way ANOVA Example in Data Science
    4m 7s
  7. Two way ANOVA Example in Data Science
    9m 24s

  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
    12m 1s
  6. Residual Analaysis of Model example in Data Science
    9m 7s
  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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