Ec248 Final MC PDF

A frequency table A. Organizes only quantitative data; B. Organizes data in time frequency; C. **Organizes data by recording counts and category names;** D. All of the above; E. None of the above. A relative frequency table A. Displays the proportions, not percentages; B. Displays only perce...

A frequency table A. Organizes only quantitative data; B. Organizes data in time frequency; C. **Organizes data by recording counts and category names;** D. All of the above; E. None of the above. A relative frequency table A. Displays the proportions, not percentages; B. Displays only percentage, not proportions; C. Can never include \'O\' per cent; D. All of the above; E. **None of the above.** What is the area principle? A. Data should be displayed as squares; B. **The area of a bar should correspond to the magnitude of its value;** C. An area can never be \'O\': D. All of the above; E. None of the above. Frequencies A. You cannot combine frequencies of two categorical variables; B. The aggregate frequency of a categorical variable is 100%; C. Combining the frequency of two categorical variables gives 100%; D. All of the above; E**. None of the above.** Pie charts A. Are more useful than bar charts; B. **Does not have overlapping categories;** C. May not add 100%; D. All of the above; E. None of the above. The marginal distribution for a variable A. Only exists for bivariate data; B. **In a contingency table is the same as its frequency distribution;** C. Are for variables with negligible probabilities; D. All of the above; E. None of the above. The totals in a contingency table A. Are always expressed in per cent; B. **May be expressed in per cent;** C. Are never expressed in per cent; D. All of the above; E. None of the above A conditional distribution A. **Gives the distribution of one variable for cases that satisfy a specific condition;** B. Gives the distribution of two correlated variables; C. Applies only to categorical variables; D. All of the above; E. None of the above. A contingency table A. Shows the likelihood of an event; B. Shows the standard deviation; C. **Shows how the values of one variable is contingent on the values of another;** D. All of the above; E. None of the above The Simpson\'s Paradox A. Refers the phenomenon of negative probabilities; B. Refers to the fact that nobody on the Simpson\'s show is aging; C. **Results from inappropriately combining percentages of different groups;** D. All of the above; E. None of the above. A stem-and-leaf display A. Uses the first digit of the number as the bin; B. Uses the next digit of the number for the bar; C. Gives a similar shape as the histogram; D. **All of the above;** E. None of the above. For distribution, describe A. Its shape; B. Its center; C. Its spread; D. **All of the above:** E. None of the above. The peaks in a histogram are called A. **Modes;** B. Apex; C. Zeniths; D. All of the above; E. None of the above. A histogram can A. Have no peaks; B. Be unimodal: C. Be multimodal: D. **All of the above:** E. None of the above. A histogram without a peak, means that the data A. Have a unimodal distribution: B. **Are uniformly distributed;** C. Have no distribution; D. All of the above; E. None of the above. A distribution is symmetric A. If it is platykurtic; B. **If both halves besides the center are roughly mirror images;** C. If there is no leptokurtosis; D. All of the above: E. None of the above. Outliers A. Can be errors in the data; B. Affect statistical methods: C. Can be extraordinary events; D. **All of the above:** E. None of the above. A scatterplot A. Plots one categorical variable against another; B. Plots one quantitative variable against time; C. **Plots one quantitative variable against another;** D. All of the above; E. None of the above. In a scatterplot, look A. For direction and symmetry; B. **For direction and form:** C. For direction and mode: D. All of the above; E. None of the above. In a scatterplot, the strength of the relationship is given A. By the direction of the clusters; B. **By the tightness of the clusters along a stream;** C. By the spread of the cluster; D. All of the above: E. None of the above. A scatterplot is A. **A bivariate analysis;** B. A multivariate analysis; C. A univariate analysis; D. All of the above; E. None of the above. An explanatory variable is A. A predictor variable; B. An exogenous variable; C. An independent variable; **D. All of the above;** E. None of the above. Correlation A. **Measures the strength of the linear association between two variables;** B. Measures the strength of any association between two variables: C. Measures the slope of a line through a scatterplot; D. All of the above; E. None of the above. Correlation applies A. Only to categorical variables; B. **Only to quantitative variables;** C. To both categorical and quantitative variables; D. All of the above; E. None of the above. Correlation A. Is not affected by changes in the scale of either variable: B. Its sign gives the direction of the association; C. Is sensitive to outliers; D. **All of the above;** E. None of the above. A lurking variable A. **Simultaneously affects two variables;** B. Is a lagged variable; C. Is a variable that cannot be observed (like the business cycle): D. All of the above; E. None of the above. What is the sample space? A. It is the number of observations; B. It refers to the number of observations compared to the population; C. **It is the collection of all possible outcomes;** D. All of the above; E. None of the above. The probability of an event A. Is a random phenomenon that generates an outcome: B. **Is its long run frequency;** C. It is the collection of all possible outcomes; D. All of the above; E. None of the above. Independence means A. **That the outcome of one trial does not influence or change the outcome of another;** B. That events do not even out in the short run; C. That all possible outcomes have the same probability; D. All of the above; E. None of the above. Empirical probability A. Is based on matching competing theoretical predictions; B. **Is based on repeatedly observing the event\' outcome:** C. Is based on the Law of Averages; D. All of the above; E. None of the above. The probability of the set of all possible outcomes is A. -infinity B. +infinity; C. 0: D. All of the above; E. **None of the above.** The multiplication rule applies A. Only to disjoint events; B. Only to possible events; C. **Only to independent events;** D. All of the above; E. None of the above. The addition rule applies A. **Only to disjoint events;** B. Only to possible events; C. Only to independent events; D. All of the above; E. None of the above. Disjoint events A. **Cannot be independent;** B. Are always independent; C. Can be but are not necessarily independent; D. All of the above; E. None of the above. The difference between joint and conditional probabilities A. Is none (they are the same); B. **Is that conditional probabilities depend on marginal** **probabilities:** C. Is that conditional probabilities depend on all events; D. All of the above: E. None of the above. The general multiplication rule A. Requires independence; B. Requires dependence; C. **Does not require independence;** D. All of the above; E. None of the above. What is a random variable? A.A variable whose value is based solely on a normal distribution: B.A variable whose value is based solely on a uniform distribution: C. A variable whose value is based solely on a binomial distribution: D. All of the above: E. **None of the above.** Random variables A. Are only discrete variables; B. Are only continuous variables; C. **Can be discreet or continuous variables;** D. All of the above; E. None of the above. Why is Var(X +- c) = Var(X) correct A. It is not correct, but Var(X+-c) = Var(X) +-c; B. **Because, Var(constant)=0:** C. Because, Var(constant) = 1; D. All of the above; E. None of the above. Which is correct? A. **Var(aX) = a\^2 Var(X);** B. Var(aX)=a+- Var(X); C. Var(aX)-Var(X); D. All of the above; E. None of the above. Var(X+-Y)=Var(X) + Var(Y) A. Applies only to quantitative variables; B. Applies to all variables; C. **Applies only to independent variables;** D. All of the above: E. None of the above. The following is NOT a characteristic of a Bernoulli Trial A. The probability of success, denoted p, is the same for each trial. The probability of failure is q=1- 0; B. The trials are independent; C. There are only two possible outcomes (success and failure) for each trial: D. All of the above; E. **None of the above.** The Normal Distribution A. Is symmetric; B. Is also called a Gaussian: C. Is unimodal: D. **All of the above;** E. None of the above. The Standard Normal Distribution A. Is its z-score; B. Has a mean of \"0\'. C. Has a standard deviation of \"1\'. D. **All of the above:** E. None of the above. True proportions A. Are those of the underlying population; B. Are generally not observed; C. We can learn more about them through computer simulation: D. **All of the above;** E. None of the above. The sampling distribution of proportions A.**Is the distribution of proportions over many independent samples from one population;** B. May but does not have to relate to samples from one population: C Is the same distribution as the distribution of the population; D. All of the above: E. None of the above. The sampling error A. Is the errors in the statistics due to non random sampling; B. Is the difference between stratified and clustered sampling: C. **Is the variability in the statistics from different samples of the same population;** D. All of the above: E. None of the above. The distribution for the sample proportions A. Is usually the uniform distribution; B. Is usually the geometric distribution; C. Is usually the exponential distribution; D. All of the above: E. **None of the above.** p̂ is modeled as p̂ \~ N(p,(pq/n)) A. If the sample size is large enough; B. The sampled values are independent; C. If ¡ is from a sample; D. **All of the above:** E. None of the above. The 10% condition states A. **The sample size must be no larger than 10% of the population;** B. At least 10% of the sample must be drawn at random: C At least 10% of the sample must be successes or failures; D. All of the above; E. None of the above. The success/failure condition states A. That p must be success and q must be failure; B. **That np and nq (n sample size, p success and q failure) are expected to be at least 10;** C. That q = 1 - p; D. All of the above; E. None of the above. The Central Limit Theorem A. States that the distribution of any mean becomes a normal distribution if the sample size is large enough; B. Is true if the underlying distribution is not normally distributed: C. Requires a larger sample size if the underlying population is skewed: D. **All of the above:** E. None of the above. What is the standard error of p̂? A. The sample error; B. The standard deviation of a standard normal: C. **The estimate of the standard deviation of p̂;** D. All of the above: E. None of the above. What is the difference between SD(p̂) and SE(p̂)? A. There is no difference; B. SD(p̂) is divided by n and SE(p̂) is divided by n - 1; C. **SE(p̂) is in terms of \^p rather than p;** D. All of the above; E. None of the above. Can we be certain that p̂ is within p̂ +- 2 x SE(p̂)? A. Yes, because of the CLT; B**. No, we can only be 95% certain;** C. Yes, because the true value of p is close to p; D. All of the above: E. None of the above. The margin of error is: A. ME = 2 [\$\\sqrt{}pq/n\$]{.math.inline}: B. ME = 2SD (p̂); **C. ME = 2SE (p̂);** D. All of the above: E. None of the above. How can we be certain that p is within the confidence interval? A. **If the confidence interval is between 0% and 100%;** B. If the confidence interval is between -infinity and infinity: C. If the confidence interval is zero: D. All of the above; E. None of the above. The a critical value z\* for the 95% confidence interval is A. 1.810; B. **1.960:** C. 2.132: D. All of the above; E. None of the above. Data for 1000 trading day on the TSEs give the proportion of up days of 0.515. A. p= 0.515; **B. p̂ = 0.515;** C. q = 0.485; D. All of the above; E. None of the above. Where does the hypothesis testing belong in A. **Always in the null hypothesis (Ho);** B. Always in the alternative hypothesis (HA); C. In either the null or in the alternative; D. All of the above; E. None of the above. HA may also be called: A. Ĥo: **B. H1:** C. A0; D. All of the above: None of the above. HA: p ≠ 0.5 is a: A. One sided alternative: B. **Two sided alternative;** C. A random alternative; D. All of the above; E. None of the above. HA: p \< 0.5 is a: A. **One sided alternative;** B. Two sided alternative; C. A random alternative; D. All of the above; E. None of the above. What the researcher is interested in belongs in: A. The null hypothesis; B. **The alternative;** C. Either the null hypothesis or the alternative; D. All of the above; E. None of the above. The P-value is the probability of seeing the observed data: A. **Given the null hypothesis;** B. Given the alternative: C. Given random sampling; D. All of the above: E. None of the above. A low p-value means that: A. The sampling distribution is unlikely to be approximately normal; B. The given data are very likely given the null hypothesis; C. **The given data are very unlikely given the null hypothesis;** D. All of the above: E. None of the above. We can: A. **Fail to reject a null hypothesis;** B. Accept a null hypotheses; C. Accept an alternative hypothesis; D. All of the above; E. None of the above. If the P-value \> a: A. **Fail to reject the null hypothesis;** B. Fail to reject the alternative hypothesis; C. Accept a null hypotheses; D. All of the above; E. None of the above. The standard error **A. Is an estimate of the standard deviation;** B. Is the same as the standard deviation: C. Is the standard deviation divided by n - 1; D. All of the above: E. None of the above. The Student\'s t distribution A. Changes with the sample size; B. Changes with the degrees of freedom; C. Becomes more similar to the Normal distribution when the sample size increases; **D. All of the above;** E. None of the above. William Gosset was working for: A. Molson Coors Brewery; B. Tsingtao Brewery Group; C. Heineken Brewery; D. All of the above; E. **None of the above.** The degrees of freedom for Student\'s t are: A. **The sample size n minus 1;** B. The sample size n minus p; C. The sample size n; D. All of the above: E. None of the above. The critical value t(n-1) depends on: A. The confidence level: B. The degrees of freedom; C. The sample size; **D. All of the above;** E. None of the above. For very small samples (n \< 15): A. The Student\'s t only holds approximately; B. The Student\'s t holds even in the presence of outliers: **C The Student\'s t should not be used if data do not follow normal model closelv:** D. All of the above: E. None of the above. For sample sizes larger than 40: **A. t models are appropriate to use unless the data are very skewed:** B. t models are appropriate to use even if the data are very skewed; C. t models are not appropriate in the presence of outliers; D. All of the above: E. None of the above.