(a) What is the GINI index of the dataset? (b) What is the GINI index of the split on
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(a) What is the GINI index of the dataset?
(b) What is the GINI index of the split on A1 and that on A2 respectively?
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- one -Let there be three levels of the row variable and four levels of the column variable in a crosstab. If there are 150 observations at the 1st level of the row variable, 150 at the 2nd level and 70 at the 3rd level, 130 observations at the 1st level of the column variable, 20 at the 2nd level and 100 observations at the 3rd level, which of the following is the sample size?a)430 B)870 NS)620 D)560 TO)370Problem 4 Car manufacturers are interested in whether there is a relationship between the size of car an individual drives and the number of people in the driver’s family (that is, whether car size and family size are independent). To test this, suppose that 800 car owners were randomly surveyed with the following results. Conduct a test for independence. Family Size Sub & Compact Mid-size Full-size Van & Truck 1 20 35 40 35 2 20 50 70 80 3 - 4 20 50 100 90 5+ 20 30 70 70 TABLE 2This problem is based on problems 18.1, 18.2, 18.9, & 18.10 from Lomax & Hahs-Vaughn, 3rd ed.You are given the following data, where X1 (final percentage in math class) and X2 (number of absences) are used to predict YY (standardized math test score in fifth grade): YY X1X1 X2X2 350 78 3 390 80 1 375 88 3 345 70 3 450 89 1 485 99 0 415 95 2 410 72 1 400 82 0 420 80 2 375 92 6 480 98 0 Determine the following multiple regression values.Report intercept and slopes for regression equation accurate to 3 decimal places: Intercept: a= Partial slope X1: b1= Partial slope X2: b2=Report sum of squares and coefficient of multiple determination accurate to 3 decimal places: R2= SSTotal= Test the significance of the overall regression model (report F-ratio accurate to 3 decimal places and P-value accurate to 4 decimal places): F-ratio = P-value = Report the variance of the residuals accurate to 3 decimal places: MSres= Report the…
- Problem 2: Naïve Bayes Classification In order to reduce my email load, I decide to use a classifier to decide whether or not I should read an email, or simply file it away instead. To train my model, I obtain the following data set of binary-valued features about each email, including whether I know the author or not, whether the email is long or short, and whether it has any of several key words, along with my final decision about whether to read it ( y = +1 for “read”, y = −1 for “discard”). Help me build the classifier. Know author? Is long? Has “research”? Has “grade”? Has “lottery”? Read? X1 X2 X3 X4 X5 Y 0 0 1 1 0 -1 1 1 0 1 0 -1 0 1 1 1 1 -1 1 1 1 1 0 -1 0 1 0 0 0 -1 1 0 1 1 1 1 0 0 1 0 0 1 1 0 0 0 0 1 1 0 1 1 0 1 1 1 1 1 1 -1 In the case of any ties, predict class +1. Use naïve Bayes classifier to make…A problem with a phone line that prevents a customer from receiving or making calls is upsetting to both the customer and the telecommunications company. The file Phone contains samples of 20 problems reported to two different offices of a telecommunications company and the time to clear these problems (in minutes) from the customers’ lines Central Office I Time to Clear Problems (minutes) 1.48 1.75 0.78 2.85 0.52 1.60 4.15 3.97 1.48 3.10 1.02 0.53 0.93 1.60 0.80 1.05 6.32 3.93 5.45 0.97 Central Office II Time to Clear Problems (minutes) 7.55 3.75 0.10 1.10 0.60 0.52 3.30 2.10 0.58 4.02 3.75 0.65 1.92 0.60 1.53 4.23 0.08 1.48 1.65 0.72 Perform a hypothesis test to determine if there’s evidence in this data of a difference in the mean waiting time between the two offices by answering the following questions: (a) What are the null and alternate hypotheses for this test? (b) Assuming that the population variances from both offices are equal, what is…A problem with a phone line that prevents a customer from receiving or making calls is upsetting to both the customer and the telecommunications company. The file Phone contains samples of 20 problems reported to two different offices of a telecommunications company and the time to clear these problems (in minutes) from the customers’ lines: Central Office I Time to Clear Problems (minutes) 1.48 1.75 0.78 2.85 0.52 1.60 4.15 3.97 1.48 3.10 1.02 0.53 0.93 1.60 0.80 1.05 6.32 3.93 5.45 0.97 Central Office II Time to Clear Problems (minutes) 7.55 3.75 0.10 1.10 0.60 0.52 3.30 2.10 0.58 4.02 3.75 0.65 1.92 0.60 1.53 4.23 0.08 1.48 1.65 0.72 Perform a hypothesis test to determine if there’s evidence in this data of a difference in the mean waiting time between the two offices by answering the following questions: (b) Assuming that the population…
- A problem with a phone line that prevents a customer from receiving or making calls is upsetting to both the customer and the telecommunications company. The file “Phone” contains samples of 20 problems reported to two different offices of a telecommunications company and the time toclear these problems (in minutes) from the customers’ lines: Central Office I Time to Clear Problems (minutes) 1.48 1.75 0.78 2.85 0.52 1.60 4.15 3.97 1.48 3.10 1.02 0.53 0.93 1.60 0.80 1.05 6.32 3.93 5.45 0.97 Central Office II Time to Clear Problems (minutes) 7.55 3.75 0.10 1.10 0.60 0.52 3.30 2.10 0.58 4.02 3.75 0.65 1.92 0.60 1.53 4.23 0.08 1.48 1.65 0.72 Assuming that the population variances from both offices are not equal, is there evidence of a difference in the mean waiting time between two offices? (Use a = 0.01) ▪ You may need to download file “Phone”. Referring to Table 10-2, which of the following is an appropriate null hypothesis? Question 2 options: 1)…Problem 8. A common statistical test used in sensory discrimination experiments in food science (as well as consumer products like household cleaning products, cosmetics, etc) is the so-called triangle test.It is often described as follows. To determine whether there is a perceivable difference 3 between products say, an established soft drink and a newly developed soft drink with a cheaper recipeyou assemble a tasting panel and present each member with three samples (cups of soft drink), two of which are the same (say, the established recipe) and the other different (the cheaper recipe). The samples should look identical and be presented in random order to each taster. The taster is asked to select the one sample that's different from the other two. The total number k of correct selections (for the n tasters) would be the result of random guessing if there were no perceivable difference, so a 1/3 chance of being correct, so look up k in the table of the cumulative binomial…