Data Science Course > Regression Analysis and Correlation

Residual Analaysis of Model example in Data Science

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A residual is a metric that measures how well a line fits a single data point. A residual is the vertical distance between two points. The residual is positive for data points above the line, while it is negative for data points below the line. The better the fit, the closer a data point's residual is to zero.

Residual analysis is the process of analyzing the economic impact of a project or initiative. It is a way for businesses to measure and understand the value delivered by their investments. It helps them make better decisions about what projects to invest in and what projects to terminate. It also helps them determine whether they are receiving value from their investments.

Residual analysis is the process of finding the remaining amount of a product after it has been used. This can be done by analyzing the residuals in the following steps:

  • Identify the amount of product that is left over after each usage.
  • Calculate how much of this product is remaining in each usage
  • Compare this difference to an average residual value and determine whether or not there is any change in residual value.

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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
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  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
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  6. Histogram and Scatter Plot in Data Science
    7m 42s
  7. Cross Tabulation in Data Science
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  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
    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
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  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
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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
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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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  3. Test assumption of Regression Model 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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  6. Residual Analaysis of Model example in Data Science
    9m 7s
  7. Residual Analysis of Model Part 2 in Data Science
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  8. Residual Analysis of Model Part 3 in Data Science
    6m 38s

  1. Understanding Clustering in Data Science
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  2. Hierarchical 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
    5m 41s
  2. Creating Pivots and Preparing Charts
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  3. Slicers in Dashboard
    3m 27s
  4. Preparing Final Dashboard
    8m 20s
  5. Presenting Dashboard
    3m 2s
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