please answer 6.1 only

The following data resulted from a study commissioned by a large management consulting company to investigate the relationship between amount of job experience (months) for a junior consultant and the likelihood of the consultant being able to perform a certain complex task. The company is interested in determining the relationship between experience and project success. The company needs to predict what the probability of success is when a junior consultant has had 1 year of experience. The company automatically promotes all junior consultants to 'normal' consultants after they are able to complete projects with a 75% success rate. Approximately how many months of experience would this require? experience number of months employed as a junior consultant status whether consultant performed task successfully, Success or Failure Reference: Devore, J .L. (2009). Probability and Statistics for Engineering and the Sciences, 7e. Belmont, CA: Brooks/Cole. consul": ing . df = read . table E "Hgthonsulting . txt " , header = TRUE) consul": ing . df $sta'3us = factor (consulting . d_":'$stat.us) plotCas.nmneric{status) ~ experience, data = consultingdifjI 2.0 O O OOO O O 8 as.numeric( status) N 2. OOO O O O O O O 5 10 15 20 25 30 35 40 experienceconsulting . glm = glm(status - experience, family = binomial, data = consulting. df) summary (consulting. glm) # # Call : # # glm (formula = status - experience, family = binomial, data = consulting. df) #: # # Deviance Residuals : Min 1Q Median 3Q Max ## -1.9090 -0.7506 -0. 1058 0 . 8845 1. 9072 ## Coefficients: Estimate Std. Error z value Pr(>|z|) # # (Intercept) -3.02597 1 . 25190 -2.417 0 . 0156 * experience 0. 17303 0. 06773 2.555 0 . 0106 * ## Signif. codes: ( ' ***' 0.001 '**' 0.01 '*' 0.05 ' . ' 0.1 ' ' 1 # # ## (Dispersion parameter for binomial family taken to be 1) ### # Null deviance: 38.816 on 27 degrees of freedom ## Residual deviance: 28.950 on 26 degrees of freedom ## AIC: 32.95 +# ## Number of Fisher Scoring iterations: 4 confint (consulting.glm) ## Waiting for profiling to be done. .. # # 2.5 %% 97 .5 % ## (Intercept) -5.92311205 -0.8657181 ## experience 0. 05778004 0.3301324 exp (confint (consulting. glm)) ## Waiting for profiling to be done... ## 2.5 %% 97 .5 % ## (Intercept) 0.002676857 0. 4207493 ## experience 1.059481923 1.3911522 100 * (exp(confint (consulting . glm) ) - 1) ## Waiting for profiling to be done. . . # # 2.5 % 97 .5 % ## (Intercept) -99.732314 -57. 92507 ## experience 5. 948192 39. 11522 table (consulting. df$status, predict (consulting.glm, type = 'response') >= 0.5, dnn = c('Observed' , 'Predicted')) ## Predicted ## Observed FALSE TRUE # # Failure 10 4 ## Success 3 11Question .1. Calculate the Sensitivity and Specil'iicitg,r of the model tted? if we predict project success if the predicted probability is 3} 0.5. Question 6.2. \"That would we expect to happen if we were to increase the bar for predicting project success to a. probability of 0.75? You do not need to give a numeric anawer.