Please refer to the Project video for complete context about the “Advance Statistics” problem.
The objective of the project is to use the dataset ‘Factor-Hair-Revised.csv‘ to build an optimum regression model to predict satisfaction. You are expected to
Please note the following:
CriteriaPoints1.1 EDA – Basic data summary, Univariate, Bivariate analysis, graphs -4
1.2 EDA – Check for Outliers and missing values and check the summary of the dataset -4
2. Check for Multicollinearity – Plot the graph based on Multicollinearity -6
3. Simple Linear Regression (with every variable) -6
4.1 Perform PCA/FA and Interpret the Eigen Values (apply Kaiser Normalization Rule) -10
4.2 Output Interpretation Tell why only 4 factors are being asked in the questions and tell whether it is correct in choosing 4 factors. Name the factors with correct explanations. -10
5.1 Create a data frame with a minimum of 5 columns, 4 of which are different factors and the 5th column is Customer Satisfaction – 3
5.2 Perform Multiple Linear Regression with Customer Satisfaction as the Dependent Variable and the four factors as Independent Variables -4
5.3 MLR summary interpretation and significance (R, R2, Adjusted R2,Degrees of Freedom, f-statistic, coefficients along with p-values) -8
5.4 Output Interpretation <making it meaningful for everybody> -5
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