Prestige Prediction App.

The Prestige dataset used in my application has been taken from the library ‘car’ in R. This dataset has 102 observations and 6 attributes. The observations are the Canadian occupations.

Out of the 6 attributes, 4 attributes have been selected namely :

• ‘type’ representing the type of occupation.
• ‘education’ representing average education of occupational incumbents
• ‘income’ which represents average income of incumbents
• ‘prestige’ represents the prestige score for occupation.

• Predict prestige that represents the score for occupation given the predictors: ‘type’ , ‘education’ and 'income'

• Fit a linear regression model for prediction due to the linear nature of predictors with respect to the desired outcome.

• Calculate the 95% Confidence Interval for each fitted value.

summary(fit)

Call:
lm(formula = prestige ~ as.numeric(education) + as.numeric(income) + 
    as.factor(type), data = trainingdata)

Residuals:
     Min       1Q   Median       3Q      Max 
-14.0956  -4.6313   0.1798   4.6625  17.9948 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)            2.4040974  6.7085817   0.358 0.721199    
as.numeric(education)  3.2518354  0.8173297   3.979 0.000173 ***
as.numeric(income)     0.0010817  0.0002518   4.295 5.77e-05 ***
as.factor(type)prof    7.8350319  4.8904655   1.602 0.113837    
as.factor(type)wc     -0.0928479  3.3078956  -0.028 0.977691    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 7.55 on 67 degrees of freedom
Multiple R-squared:  0.8164,    Adjusted R-squared:  0.8055 
F-statistic:  74.5 on 4 and 67 DF,  p-value: < 2.2e-16