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Questions and Answers of Business Analytics Data

Name three of the seven reasons to do project management well you think are most important and explain why you think they are more important than the other four.
Choose two unsupervised methods.• Explain in general how they work.• Create a detailed example of how you might use each in an applied setting.
What is the difference between a measure of central tendency, a measure of variability, and a measure of relative position? Why might you use each?
Explain confirmation bias and how descriptive statistics can contribute to it.
Why are descriptive statistics so important?
Why are systematic samples so important in HR Analytics? Name and explain four major considerations when building systematic samples.
Explain the difference between independent and dependent variables. Create two examples of something to study and define which variables are independent and which are dependent.
Create operational definitions of three things you would like to measure. Include all component parts necessary to define the phenomena.
What is the difference between exploratory, constructive, and empirical research?Give examples of when you might use each.
Consider a challenge you have tried to solve at a job. Break the problem down into its component parts: how many problems were there in total and how did they relate to each other? Frame these
What is the difference between deductive and inductive reasoning? What is the value of each?
Why is standard deviation so important? Give two examples of how it might be used in an applied setting.
Why are distributions an important part of descriptive statistics? Provide two examples of common HR data which do not demonstrate normal distributions.
Choose three supervised methods.• Explain in general how they work.• Create a detailed example of how you might use each in an applied setting.
What is the difference between supervised and unsupervised learning? Which is more commonly used in HR?
What is overfitting? Why is it so important to watch out for?
Explain what transparency is and why it is usually so important in HR Analytics.
Name four major considerations for machine learning and explain why they are important.
What is the difference between machine learning and inferential statistics? Why does it matter?
How does the literal nature of computers limit them? How have advances in computing enabled machine learning to remove these limitations?
What are the three main functions of a traditional computer? What are the two metrics by which those functions are measured?
What is the difference between statistically significant and practically significant?Give two examples of when you might need each.
Create two examples of how you have (or might) use the scientific method at a company. Specifically, focus on what makes the approach scientific.
Create two examples of how you have (or might) use the scientific method at a company. Specifically, focus on what makes the approach scientific.
Discuss some pros and cons you see of integrating machine learning into an HR Analytics strategy.
How does machine learning stand to influence HR Analytics Ikigai?
4. What is the employee lifecycle and how does it impact the data ecosystem at most organizations?
3. Why is descriptive analytics the most important form of analytics in HR?
2. What are the main differences between descriptive, predictive, and prescriptive analytics?
1. What is digital transformation and how is it different than HR Analytics? How does machine learning stand to impact both?
2. What are the two main types of professionals moving into the HR analytics space today? What skills do they bring and which do they need to develop?
1. What makes employee data different from other types of data used for data science?
What are the four parts of HR Analytics Ikigai? Why is each important?
What is the difference between deductive and inductive reasoning? What is the value of each?
1.1 Identify each variable as nominal, ordinal, or interval.a. UK political party preference (Labour, Liberal Democrat, Conservative)b. Anxiety rating (none, mild, moderate, severe, very severe)c.
1.2 Each of 100 multiple-choice questions on an exam has four possible answers, one of which is correct. For each question, a student guesses by selecting an answer randomly.a. Specify the
1.4 In his autobiography A Sort of Life, British author Graham Greene described a period of severe mental depression during which he played Russian roulette. This "game"consists of putting a bullet
1.5 When the 2010 General Social Survey asked, "Please tell me whether or not you think it should be possible for a pregnant woman to obtain a legal abortion if she is married and does not want any
1.6 Refer to the vegetarianism example in Section 1.4.3. For testing H0: π = 0.50 against Hα: π ≠ 0.50, show that:a. The likelihood-ratio statistic equals 2[25 log(25/12.5)] = 34.7.b. The
1.7 In a crossover trial comparing a new drug to a standard, π denotes the probability that the new one is judged better. It is desired to estimate π and test H0: π = 0.50 against Hα: π ≠
1.8 Refer to the previous exercise. Suppose you wanted a large enough sample to estimate the probability of preferring the new drug to within 0.05, with confidence 0.95. If the true probability is
1.9 In an experiment on chlorophyll inheritance in maize, for 1103 seedlings of self-fertilized heterozygous green plants, 854 seedlings were green and 249 were yellow.Theory predicts the ratio of
1.10 Table 1.3 contains Ladislaus von Bortkiewicz's data on deaths of soldiers in the Prussian army from kicks by army mules (Fisher 1934, Quine and Seneta 1987).The data refer to 10 army corps, each
1.11 A binomial experiment tests $$H_0: \pi = 0.50$$ against $$H_a: \pi eq 0.50$$ using significance level 0.05. Only n = 5 observations are available. Show that the true null probability of
1.12 A researcher routinely tests using a nominal P (type I error) = 0.05, rejecting Ho if the P-value < 0.05. An exact test using test statistic T has null distribution P(T =0) = 0.30, P(T = 1) =
1.13 The 2006 General Social Survey asked respondents how much government should spend on culture and the arts, with categories (much more, more, the same, less, much less). For 18-21 year-old
1.14 Refer to Example 1.6.4 on estimating the proportion of vegetarians. For the Jeffreys prior, find the posterior mean, the posterior 95% equal-tail interval, and the 95%highest posterior density
1.15 You plan to use Bayesian methods to estimate binomial parameters in two cases, using n observations. In case (1) you want to estimate the probability that a new treatment for skin cancer is
1.16 It is easier to get a precise estimate of the binomial parameter when π is near 0 or 1 than when it is near. Explain why.
1.17 Suppose that P(Y₁ = 1) = 1 − P(Y₁ = 0) = π, i = 1,..., n, where {Y;} are inde-pendent. Let Y = ΣΥ.a. What is the distribution of Y? What are E(Y) and var(Y)?b. When {Y;} instead have
1.18 For a sequence of independent Bernoulli trials, let Y be the number of successes before the kth failure. Explain why its probability mass function is the negative binomial, $$p(y) = \frac{(y + k
1.19 For the multinomial distribution, show that$$corr(n_j, n_k) = -\pi_j \pi_k/\sqrt{\pi_j (1 - \pi_j)\pi_k(1 - \pi_k)}.$$When c = 2, show that this simplifies to corr(n1, n2) = -1, and explain why
1.20 Show that the moment generating function (mgf) is (a) m(t) = (1 - π + πε')" for the binomial distribution, (b) m(t) = exp{u[exp(t) - 1]} for the Poisson distribution.For each distribution,
1.21 A likelihood-ratio statistic equals to. At the ML estimates, show that the data are exp(to/2) times more likely under Ha than under Ho.
1.22 Suppose that y1, y2,..., yn are independent from a Poisson distribution.a. Obtain the likelihood function. Show that the ML estimator û = ӯ.b. Construct a large-sample test statistic for Ho:
1.23 Inference for Poisson parameters can often be based on connections with binomial and multinomial distributions. Show how to test Ho: μ₁ = μ₂ for two populations based on independent
1.24 Since the Wald confidence interval for a binomial parameter π is degenerate when t = 0 or 1, argue that the probability that the interval covers π cannot exceed[1 - π" – (1 – π)"];
1.25 We noted in Section 1.4.2 that the midpoint of the score confidence interval (1.14)for π is the sample proportion after adding 22/2 observations to the sample, half of each type. This motivates
1.26 A binomial sample of size n has y = 0 successes.a. Show that the confidence interval for based on the likelihood function is[0.0, 1 exp(-22/2/2n)]. For a = 0.05, use the expansion of an
1.27 Suppose that P(T = tj) = πj, j = 1,.... Show that E(mid P-value) = 0.50.[Hint: Show that Σ; π;(π;/2 + πj+1 +۰۰۰) = (Σ; π;)²/2.]
1.28 For a statistic T with cdf F(t) and p(t) = P(T = t), the mid distribution func-tion is Fmid(t) = F(t) - 0.50p(t) (Parzen 1997). Given T = to, show that the mid P-value equals 1 - F(to). (It also
1.29 Genotypes AA, Aa, and aa occur with probabilities [02, 20(1 – 0), (1–0)2].A multinomial sample of size n has frequencies (n1, n2, n3) of these three genotypes.a. Form the log likelihood.
1.30 Refer to Section 1.5.6 and the model for pneumonia infections in calves. Using the likelihood function to obtain the information, show that the approximate standard error of t is √π(1 –
1.31 Refer to Section 1.5.6. Let a denote the number of calves that got a primary, sec-ondary, and tertiary infection, b the number that received a primary and secondary but not a tertiary infection,
1.32 Refer to quadratic form (1.18) that leads to the Pearson chi-squared.a. Verify that the matrix quoted in the text for ¹ is the inverse of 20.b. Show that (1.18) simplifies to Pearson's
1.33 For testing Ho: π; = πjo, j = 1,...,c, using sample multinomial proportions {;}, the likelihood-ratio statistic (1.17) is 2G² = −2η Σπ; log(njo/nj).j jShow that G² ≥ 0, with equality
1.34 For counts {n,}, the power divergence statistic for testing goodness of fit (Cressie and Read 1984, Read and Cressie 1988) is 2λ(λ + 1)λΣη; [(π/μ₁) – 1]a. For λ = 1, show that this
1.35 The chi-squared mgf with df = v is m(t) = (1 - 2t)/2, for |t|
1.36 For the multinomial (n, {π;}) distribution with c > 2, a possible set of score-type simultaneous confidence limits for π; are the solutions of(π; – π;)2/[π;(1 – π;)/n] = (2a/2c)², j
1.37 Consider the Bayesian equal-tail posterior interval for a binomial parameter π, using a beta or logit-normal prior. When y = 0, explain why the lower limit for a can never be 0, unlike the
1.38 Consider estimating the ratio πί/π; of two multinomial parameters. Should the estimate depend at all on the counts in other categories?a. With a frequentist approach, explain why the ML
1.39 Given π, Y has a bin(n, π) distribution, and a has a uniform prior distribution. Show that the marginal distribution of Y is uniform over 0, 1,...,n.
1.40 Consider the Bayes estimator of the binomial parameter π using a beta prior distri-bution.a. Show that the ML estimator is a limit of Bayes estimators, for a certain sequence of beta prior
1.41 For the Dirichlet prior for multinomial probabilities, show the posterior expected value of π¡ is formula (1.19). Derive the expression for this Bayes estimator as a weighted average of pi and
2.1 According to the FBI website (www.fbi.gov), in 2008, of female murder victims, 1710 were slain by males and 200 by females, whereas of male murder victims, 4351 were slain by males and 455 by
2.2 According to the FBI website, of all blacks slain in 2008, 92% were slain by blacks, and of all whites slain in 2005, 85% were slain by whites. Let Y denote race of victim and X denote race of
2.3 An article in The New York Times (Feb. 17, 1999) about the PSA blood test for detecting prostate cancer stated: "The test fails to detect prostate cancer in 1 in 4 men who have the disease
2.4 Table 2.10 shows fatality results for drivers and passengers in auto accidents in Florida in 2008, according to whether the person was wearing a seat belt.a. Estimate the probability of fatality,
2.5 Consider the following two studies reported in The New York Times.a. A British study reported (Dec. 3, 1998) that of smokers who get lung cancer,"women were 1.7 times more vulnerable than men to
2.6 According to a report by the United Nations Office on Drugs and Crime, the number of homicides involving firearms per million people is about 62.4 in the United States, 6.0 in Canada, 5.6 in
2.7 An article in The Economist (July 3, 2010) stated that the number of people in prison is 154 per 100,000 in England and Wales, 96 per 100,000 in France, 87 per 100,000 in Germany, and 753 per
2.8 At the start of the 2010 World Cup, the betting exchange Betfair stated that the odds against being the winning team were 9/2 for Spain, 11/2 for Brazil, 6/1 for England, and 90/1 for the United
2.9 In a recent survey of people aged 50-71 in the United States summarized by N.Freedman et al. (Lancet Oncol. 9: 649-656, 2008), during a follow-up period the annual probability of lung cancer
2.10 For adults who sailed on the Titanic on its fateful voyage, the odds ratio between gender (female, male) and survival (yes, no) was 11.4. (For data, see R. J. M.Dawson, J. Statist. Ed. 3,
2.11 A research study estimated that under a certain condition, the probability that a subject would be referred for heart catheterization was 0.906 for whites and 0.847 for blacks.a. A press release
2.13 For the Women's Health Study, heart attacks were reported for 198 of 19,934 taking aspirin and for 193 of 19,942 taking placebo (J. Am. Med. Assoc. 295: 306-313, 2006). Construct the 2 x 2 table
2.14 According to poll results released by the Pew Research Center (www.people-press.org) in 2010, when adults in the United States were asked whether there is solid evidence that the average
2.15 Table 2.11 refers to applicants to graduate school at the University of California at Berkeley, for fall 1973. It presents admissions decisions by gender of applicant for Table 2.11 Data for
2.16 State three "real-world" variables X, Y, and Z for which you expect a marginal association between X and Y but conditional independence controlling for Z.
2.17 Based on murder rates in the United States, an Associated Press story reported that the probability that a newborn child has of eventually being a murder victim is 0.0263 for nonwhite males,
2.18 At each age level, the death rate is higher in South Carolina than in Maine, but overall, the death rate is higher in Maine. Explain how this could be possible. [For data, see H. Wainer, Chance
2.20 Table 2.12 is from an early study on the death penalty in Florida. Analyze these data and show that Simpson's paradox occurs.Table 2.12 Data for Exercise 2.20 on the Death Penalty Death Penalty
2.21 Smith and Jones are baseball players. Smith has a higher batting average than Jones in each of K years. Is is possible that for the combined data from the K years, Jones has the higher batting
2.22 Table 2.13 summarizes responses from a General Social Survey about homosexual sex and premarital sex. Find and interpret a measure of association.
2.23 For the data in Table 2.13, the two marginal distributions are dependent rather than independent samples, but the measure Δ can still compare those distributions. Find it, and interpret.
2.24 Table 2.14 cross-classifies job satisfaction by race. Determine whether the groups are stochastically ordered, and estimate the difference between the probability that job satisfaction is higher
2.25 For a diagnostic test of a certain disease, let π₁ denote the probability that the diagnosis is positive given that a subject has the disease, and let π₂ denote the probability that the
2.26 Show that the odds ratio and relative risk need not be similar when π; is close to 1.0 for both groups.
2.27 Let D denote having a certain disease and E denote having exposure to a certain risk factor. The attributable risk (AR) is the proportion of disease cases attributable to that exposure (see
2.28 In comparing new and standard treatments with success probabilities π₁ and π₂, the number needed to treat (NNT) is the number of patients that would need to be treated with the new

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