Data Science Course > Regression Analysis and Correlation

Test assumption of Regression Model in Data Science

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  • Linear Relationship.
  • Independence.
  • Homoscedasticity.
  • Normality.

Plotting the predicted values against the real values in the holdout set is the best way to examine regression data. In ideal circumstances, the points should lie on a 45-degree line going through the origin (y = x is the equation). The better the regression, the closer the points are to this line.

Bring up your data in SPSS and select Analyze –> Regression –> Linear to thoroughly test the regression assumptions using a normal P-P plot, a scatterplot of the residuals, and VIF values.

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Suman Bhattacharya
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It's good to understand

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vaibhav
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best

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Anjali
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Excellent explanation

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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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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
    7m 42s
  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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  1. Skewness in Data Science
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  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
    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
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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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  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
    7m 28s
  2. Least Square and Residual Analysis in Data Science
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  3. Test assumption of Regression Model in Data Science
    7m 3s
  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
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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
    6m 38s

  1. Understanding Clustering in Data Science
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  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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