Data Science Course > Regression Analysis and Correlation

Regression Example - Creating Model in Data Science

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The following are the steps in the process of regression:

  • Establishing a set of independent variables that should be included in the regression analysis.
  • Establish a set of dependent variables for which you would like to predict values using your predicted values for each independent variable.
  • Calculate a predicted value for each dependent variable based on your estimated values for each independent variable and then compare them to actual values to see how well they predict your dependent variables.
  • Repeat steps 2 and 3 until you have reached convergence or have reached an endpoint

The equation for a linear regression line is Y = a + bX, with X as the explanatory variable and Y as the dependent variable. The intercept (the value of y when x = 0) is a, while the slope of the line is b.

Regression is a statistical approach for modelling the connection between one or more independent variables and a dependent variable. Regression is one of the most significant methods in Machine Learning and is frequently employed in a variety of statistical analysis tasks.

Regression is a method for modelling and analysing the relationships between variables, as well as how they contribute to and are connected to obtaining a specific outcome. A regression model with only linear variables is referred to as a linear regression.

Regression is a statistical method that allows us to estimate the relationship between two variables. It can also be used to predict an outcome when we have enough information about the model.

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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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  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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  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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  1. Introduction to Simple Linear Regression and Correlation 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
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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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  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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