Suppose that three classification models (IrFit, knnTune, and nbFit) were built and evaluated in RStudio, their returned ROC values are provided below: Logistic regression (IrFit): ROC value = 0.95 K-nearest neighbors (knnTune): ROC value= 0.80 Naïve Bayes (nbFit): ROC value = 0.82 Question: Which classification model would you choose? Also, write the R code for using the chosen model to predict the test set (i.e., predictors_test).
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- Suppose you trained your logistic regression classifier which takes an image as input and outputs either dog (class 0) or cat (class 1). Given the input image x, the hypothesis outputs 0.2. What is the probability that the input image corresponds to a dog?Which of the following is NOT CORRECT? A) The past purchase frequency (freqi) affects the purchase probability positively – i.e., customers with high past purchase frequency are more likely to purchase our product in the next year than those with low past purchase frequency. B) In general, customers are likely to buy our product because the baseline parameter is negative. C) High-income customers are more likely to purchase our product in the next year than low- income customers because the coefficient corresponding to the income variable (incomei) is positive. D) Although the number of flyers (nmaili) positively affects the purchase probability, it is not guaranteed that sending flyers to customers as many as possible maximizes profits.In Australia, 16% of the adult population is nearsighted.17 If three Australians are chosen at random, what is the probability that two are nearsighted and one is not? 2.state each of the five assumptions of the classical regression model (OLS) and give an intuitive explanation of the meaning and need for each of them.
- The data are the maximum daily temperatures at Cox-Dayton International Airport for June and July 2018. maximum daily temperatures at Cox-Dayton International Airport for June and July 2018. Date Max (°F) Jun 1 80 Jun 2 82 Jun 3 81 Jun 4 75 Jun 5 76 Jun 6 71 Jun 7 82 Jun 8 85 Jun 9 86 Jun 10 77 Jun 11 75 Jun 12 78 Jun 13 85 Jun 14 82 Jun 15 86 Jun 16 90 Jun 17 92 Jun 18 92 Jun 19 89 Jun 20 85 Jun 21 72 Jun 22 81 Jun 23 79 Jun 24 85 Jun 25 79 Jun 26 79 Jun 27 81 Jun 28 85 Jun 29 89 Jun 30 92 Jul 1 93 Jul 2…A UPS efficiency expert is interested in finding out if the years of driving experience (EXP) can be used to predict the frequency of late deliveries (LATE). Here are the data (late deliveries are per 100 ): EXP ZEXP LATE ZLATE 16 0.49 8 0.43 19 1.34 10 1.45 15 0.21 7 -0.09 15 0.21 8 0.43 14 -0.07 6 -0.60 15 0.21 8 0.43 13 -0.35 5 -1.11 12 -0.64 5 -1.11 4 -2.90 3 -2.14 15 0.21 8 0.43 16 0.49 9 0.94 17 0.78 9 0.94 MEXP = 14.25 SDEXP = 3.54 MLATE= 7.17 SDLATE = 1.95 Use α = .05 for all decisions. Determine how many late deliveries (per 100) we would predict for a driver with 20 years of experience. No explanation is required – just report the predicted value as your final answer.Compute the forecasted values for Yt for July and August in 2020 by using the modelsstated in (c) and (d)
- When the classical linear model assumptions are fulfilled, the OLS estimator is... t-distributed with n-k-1 degrees of freedom. Constant. Normally distributed. F distributed.A jar contains 7 gold, 5 silver, 4 blue, 3 red, and 1 green marbles. Two marbles are to be randomly drawn from the jar. What is the probability a sliver marble is drawn, not returned to the jar, and then a blue marble is drawn? Given the power regression model y = 25x^1.2, which is the linear regression model after transforming to a log-log graph?Suppose that researchers obtain a random sample of adults ages 18 – 40 and collect data on the following variables: shoe size – in inches age – in years height – in inches forearm length – in inches Suppose further that a multiple linear regression model is fit to the resulting data set using R Studio and that the following output is obtained from it. Use this output to answer the question that follows: > summary(lm(shoesize ~ age + height + forearm, data = measures)) Coefficients: (Intercept)ageheightforearm Estimate10.14882 0.06045 -0.02108 -0.06479 Std. Error 4.49245 0.06838 0.06350 0.06847 t value2.259 0.884 -0.332 -0.946 Pr(>|t|) 0.0264 0.3792 0.7408 0.3467 Residual standard error: 1.719 on 85 degrees of freedomMultiple R-squared: 0.01983, Adjusted R-squared: 0.01477 F-statistic: 0.5731 on 3 and 85 DF, p-value: 0.6342 Which of the following is the correct interpretation of the Adjusted R-squared? The probability that our model…
- Suppose that researchers obtain a random sample of adults ages 18 – 40 and collect data on the following variables: shoe size – in inches age – in years height – in inches forearm length – in inches Suppose further that a multiple linear regression model is fit to the resulting data set using R Studio and that the following output is obtained from it. Use this output to answer the question that follows: > summary(lm(shoesize ~ age + height + forearm, data = measures)) Coefficients: (Intercept)ageheightforearm Estimate10.14882 0.06045 -0.02108 -0.06479 Std. Error 4.49245 0.06838 0.06350 0.06847 t value2.259 0.884 -0.332 -0.946 Pr(>|t|) 0.0264 0.3792 0.7408 0.3467 Residual standard error: 1.719 on 85 degrees of freedomMultiple R-squared: 0.01983, Adjusted R-squared: 0.01477 F-statistic: 0.5731 on 3 and 85 DF, p-value: 0.6342 What is the estimate for the standard deviation of the residuals? 1.719 0.01983 -0.946 0.6342Suppose that researchers obtain a random sample of adults ages 18 – 40 and collect data on the following variables: shoe size – in inches age – in years height – in inches forearm length – in inches Suppose further that a multiple linear regression model is fit to the resulting data set using R Studio and that the following output is obtained from it. Use this output to answer the question that follows: > summary(lm(shoesize ~ age + height + forearm, data = measures)) Coefficients: (Intercept)ageheightforearm Estimate10.14882 0.06045 -0.02108 -0.06479 Std. Error 4.49245 0.06838 0.06350 0.06847 t value2.259 0.884 -0.332 -0.946 Pr(>|t|) 0.0264 0.3792 0.7408 0.3467 Residual standard error: 1.719 on 85 degrees of freedomMultiple R-squared: 0.01983, Adjusted R-squared: -0.01477 F-statistic: 0.5731 on 3 and 85 DF, p-value: 0.6342 Which of the following is the correct conclusion for the F-test that was performed? There is strong evidence to…Suppose that researchers obtain a random sample of adults ages 18 – 40 and collect data on the following variables: shoe size – in inches age – in years height – in inches forearm length – in inches Suppose further that a multiple linear regression model is fit to the resulting data set using R Studio and that the following output is obtained from it. Use this output to answer the question that follows: > summary(lm(shoesize ~ age + height + forearm, data = measures)) Coefficients: (Intercept)ageheightforearm Estimate10.14882 0.06045 -0.02108 -0.06479 Std. Error 4.49245 0.06838 0.06350 0.06847 t value2.259 0.884 -0.332 -0.946 Pr(>|t|) 0.0264 0.3792 0.7408 0.3467 Residual standard error: 1.719 on 85 degrees of freedomMultiple R-squared: 0.01983, Adjusted R-squared: -0.01477 F-statistic: 0.5731 on 3 and 85 DF, p-value: 0.6342 What is the test-statistic is used to test whether at least one of the explanatory variables is a significant predictor of…
