Ozone (O3) is a major component of air pollution in many cities. Atmospheric ozone levels are influenced by many factors, including weather. In one study, the mean percent relative humidity (x) and the mean ozone levels (y) were measured for 120 days in a western city. Mean ozone levels were measured in ppb. The following output (from MATLAB) describes the fit of a linear model to these data. Assume that the required assumptions are satisfied sufficiently well that we can do the usual analysis.>> fitlm(Humidity, Ozone)Linear regression model: y ~ 1 + x1Estimated Coefficients:Estimate SE tStat pValue (Intercept)29.7240 2.051 14.490 0.0000x1 -0.1268 0.03825 -3.315 0.0006Number of observations: 120, Error degrees of freedom: 118 Root Mean Squared Error: 6.26 R-squared: 0.960, Adjusted R-Squared 0.890(a) What is the slope of the least-squares line?(b) Find a 95% confidence interval for the slope.(c) Perform a test of the null hypothesis that the slope is equal to −0.1. What is the p-value?