Question: 1.Develop a multiple linear regression model to predict housing prices using the living area square feet, number of bathrooms, land value, new construction (0=no, 1=yes),

1.Develop a multiple linear regression model to predict housing prices using the living area square feet, number of bathrooms, land value, new construction (0=no, 1=yes), and central air (0=no, 1=yes) as the predictor variables.

a.Write the reqression equation.

b.Discuss the statistical significance of the model as a whole using the appropriate regression statistic at a 95% level of confidence.

c.Discuss the statistical significance of the coefficient for each independent variable using the appropriate regression statistics at a 95% level of confidence.

d.What percentage of the observed variation in housing price is explained by the model?

e.Plot the residuals and the predicted values from the regression model in a scatterplot.Comment on what you see in the plot.

f.Determine the predicted housing price which has central air, with 2 bathrooms, 2100 square feet of living area, is not new construction and have a land value of $30,000.

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