Three brands of tea are rated for taste on a scale of 1 to 10. Six persons rate each brand so that there is a total 18 observations. Which of the following test is appropriate to determine if the three brand tastes are equal? OA Mann Whitney O8. Chi-square test of independence OC Kruskal Wallis O D. Wilcoxon
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- The brown-eyed man from the previous question also has kids with a woman with brown eyes, whose whole family for the past 10 generations had brown eyes. What percentage of their children can be expected to have blue eyes? a. 0% b. 25% c. 50% d. 75% e. 100%Suppose that 10,000 people are given a drug test that is 98.2% accurate and that 500 of the people actually are drug users. Using that information, complete the following chart: Accurate Error Total Nonusers (true negative) (false positive) 9500 Users (true positive) (false negative) 500 Total 9820 180 10,000Subject: Machine Learning A dataset showing the decisions about presence of Heart Disease (HD) based on four attributes (Age, Blood pressure, Cholesterol and Heart rate) is listed in the following table: Age BP CH HR HD Old Low Low Medium No Very old Low Medium Low Yes Old Medium Medium Medium No Old Low Low Low Yes Mild Low Low Medium Yes Very old High Medium Low Yes Very old Medium Medium Low Yes Mild Medium Very high Low Yes Old Low Medium Medium No Very old Medium Very high Medium No Very old Medium Low Medium No Mild Low Medium Medium No Mild Low Medium Medium No Compute the entropy for this data set. What is the information gain for the Heart Rate (HR) feature? Given a choice among Age, Blood pressure, Cholesterol and Heart rate, which feature would the ID3 algorithm choose as the root node for a decision…
- A group of 15 students made up of 9 females and 6 males form a committee of 5 What is the probability the committee is made up of 3 females and 2 males?Which of the following measures are Null-Invariant. For the ones that are not null invariant, provide an example illustrating why it isn't Null-Invariant. a. All Confidence b. KulczynskiSuppose that 10,000 people are given a drug test that is 98.2% accurate and that 500 of the people actually are drug users. Using that information, complete the following chart: Accurate Error Total Nonusers (true negative) (false positive) 9500 Users (true positive) (false negative) 500 Total 9820 180 10,000 Hint: Fill out "accurate nonusers" first by looking at what percent of the total nonusers would be accurately tested. Looking only at positive results (accurate users and error nonusers), what is the probability of a false positive (error nonuser)? (Round to three decimal places.) Looking only at negative results, what is the probability of a false negative? (round to three decimal places) Would you consider this test to be accurate or not? Explain your reasoning fully.
- What is the code in Stata for this question? Run the linear regression given by wages = β0 +β1education+β2workexp+β3unionmember +β4south+ β5−9occupation + β10female + u where wages=hourly wage in US dollars; education=years of schooling; workexp=years of work experience; unionmember=a dummy variable equal to ”1” if a person is a union member, and ”0” otherwise; south=a dummy variable equal to ”1” if a person lives in the south, and ”0” otherwise; occupation=a categorical variable equal to ”1” if a person’s occupation is ”management”; ”2” if it is ”sales”; ”3” if it is ”clerical”, ”4” if it is ”service”, ”5” if it is ”professional” and ”6” if it is ”other”. Please make sure to use ”other” as the base category; female =a dummy variable equal to ”1” if a person is female, and ”0” if a person is male.The table below is part of an output of data analyzed whose response variable was whether or not (1=yes,0=no) a student was in a relationship (y), the predictor variables included the students age (x1), gender (x2), fee balance (x3) in Ksh’000’,family size (x4) and the religion coded such that (1=catholic,2=protestant,3=muslim,4=hindu) (x5) Examination Irregularity is punishable by expulsion Page 3 of 4Variables in the equation Step1a B S.E. Wald df Sig. Exp(B) 95% C.I.for EXP(B) Lower Upper Family-size 1.338 .494 4.419 1 .036 .354 .135 .932Age 1.197 .120 2.710 1 .043 .821 .649 1.038Fee bal .201 .173 1.342 1 .247 1.222 .870 1.716Religion 1.085 3 .781Religion(1) -.073 1.477 .002 1 .961 .930 .051 16.801Religion(2) 1.468 1.743 1.710 1 .047 4.342 .143 132.234Religion(3) -19.860 11.714 .000 1 .999 .000 .000 .Gender(1) .645 1.343 .231 1 .631 1.906 .137 26.476Constant 8.329 4.262 3.819 1 .051 412.576i) By citing the reasons highlight the variables that contributed significantly to the…Consider values shown in the table below:i=1 (cold) i=2 (allergy) i=3 (stomach pain) p(Hi)0.60.30.1 p(E1 |Hi)0.30.80.3 p(E2 |Hi)0.60.90.0Those values represent (hypothetically) three mutually exclusive and exhaustive hypotheses for the patient’s condition. For example, H1: the patient has a cold, H2: the patient has an allergy, and H3: the patient has stomach pain with their prior probabilities, p(Hi)’s and two conditionally independent pieces of evidence (E1, patient sneezes and E2, patient coughs) which support these hypotheses to differing degrees. Therefore;a) Compute the posterior probabilities for the hypothesis if the patient sneezes. What is the conclusion that can be derived from this condition?b) Based on the answer from the previous result, as the patient coughs are now observed, compute the posterior probabilities for this condition. Explain the results.
- Which statements are true about LASSO linear regression? Group of answer choices has embedded variable selection by shrinking the coefficient of some variables to exactly zero. has one hyper-parameter lambda (The regularization coefficient) which needs to be tuned if there are multiple correlated predictors lasso will select all of them adds the L2 norm of the coefficients as penalty to the loss function to penalize larger coefficientsIn the goodness-of-fit measures, interpret the coefficient of determination for Earnings with Model 3 and what the sample variation of earnings explains. Model 1 Model 2 Model 3 Standard error of the estimate Se 6,648.6753 5,430.0734 5,834.7554 Coefficient of determination R2 0.9597 0.3501 0.7005 Adjusted R2 0.6243 0.8442 0.4045 .Correct answer will be upvoted else downvoted. Computer science. With likelihood 12, he leaves his present club, then, at that point, makes another club himself and goes along with it. There is just a single understudy (himself) in the new club he makes. With likelihood 12, he doesn't make new clubs. For this situation, he changes his club to another one (perhaps a similar club he is in right now) with likelihood relative to the number of understudies in it. Officially, assume there are k clubs and there are bi understudies in the I-th club for 1≤i≤k (before the understudy blows up). He leaves his present club, and afterward joins the I-th club with likelihood canister. We note that when a club becomes unfilled, understudies won't ever go along with this is on the grounds that any understudy who blows up will get a vacant club together with likelihood 0 as per the above assertion. Homer ponders the normal number of days until each understudy is in a similar club…