Question

The adult income data set is explained as the adult census income information for 1994. Income is the dependent variable for the data set and all other variables are independent variables for the analysis. Follow the given instruction and do proper analysis for the given data set using R a) Do data pre-processing and data cleaning (eg: remove NAs or ?s) and labeling(Categorising) for the given data set. b) Identify the variable type (discrete/continuous) and do appropriate visualization and summary statistics for each variable. c) Run PCA and interpret your results 2. Use previous PCA results, d) Spilt data set into training and testing data 80% and 20% respectively. e) Consider the appropriate component from PCA analysis and use those variables to run binary logistic regression for the variable “Income”. Interpret your results. f) Do model validation and efficiency calculation for the final model. 3. Consider the same data set and do the following analysis considering age as the response variable and work class and race as predictor variables, g) Construct boxplots and main effects plots for each predictor variable with the response variable h) Conduct a Two-Way ANOVA analysis for the age and interpret your results. PLEASE SHOW THE R STUDIO CODE, OUTPUT AND ANSWER EVERY QUESTION DATA

Name: the adult income data set is explained as the adult census income information for 1994 income is the dependent variable for the data set and all other variables are independent variables for 24581
Uploaded: 2023-02-12T14:17:03-08:00
Duration: 9 min 40 s
Channel: Sri K
Description: the adult income data set is explained as the adult census income information for 1994 income is the dependent variable for the data set and all other variables are independent variables for 24581

          The adult income data set is explained as the adult census income information for 1994. Income is the dependent variable for the data set and all other variables are independent variables for the analysis. Follow the given instruction and do proper analysis for the given data set using R 
a) Do data pre-processing and data cleaning (eg: remove NAs or ?s) and labeling(Categorising) for the given data set. 
b) Identify the variable type (discrete/continuous) and do appropriate visualization and summary statistics for each variable. 
c) Run PCA and interpret your results 2. Use previous PCA results, 
d) Spilt data set into training and testing data 80% and 20% respectively.      e) Consider the appropriate component from PCA analysis and use those variables to run binary logistic regression for the variable “Income”. Interpret your results. 
f) Do model validation and efficiency calculation for the final model. 3. Consider the same data set and do the following analysis considering age as the response variable and work class and race as predictor variables, 
g) Construct boxplots and main effects plots for each predictor variable with the response variable 
h) Conduct a Two-Way ANOVA analysis for the age and interpret your results.
PLEASE SHOW THE R STUDIO CODE, OUTPUT AND ANSWER EVERY QUESTION
DATA

Added by Benjamin R.

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