Đề 13 – Bài tập, đề thi trắc nghiệm online Lý thuyết xác suất và thống kê

A A. The slope of the regression line. B B. The intercept of the regression line. C C. The proportion of variance in the dependent variable that is predictable from the independent variable(s). D D. The standard error of the regression model.

A A. Independent events cannot happen at the same time, while dependent events must happen at the same time. B B. Independent events affect each other's probabilities, while dependent events do not. C C. Independent events do not affect each other's probabilities, while dependent events do. D D. Independent events are always more likely than dependent events.

A A. Normal distribution B B. Binomial distribution C C. Poisson distribution D D. Uniform distribution

A A. The difference between means of two variables. B B. The strength and direction of a linear relationship between two variables. C C. The probability of causation between two variables. D D. The variance of two variables.

A A. Probability deals with past events, while statistics deals with future events. B B. Probability deals with predicting the likelihood of future events based on known models, while statistics uses observed data to infer properties of underlying probability distributions. C C. Probability is a branch of mathematics, while statistics is a branch of computer science. D D. There is no difference; probability and statistics are the same discipline.

A A. The most likely value of the random variable. B B. The median value of the random variable. C C. The average value of the random variable over many repeated trials. D D. The value that occurs with the highest probability.

A A. Sensitivity is the probability of testing negative given the disease is absent, and specificity is the probability of testing positive given the disease is present. B B. Sensitivity and specificity are the same thing in diagnostic testing. C C. Sensitivity is the probability of testing positive given the disease is present, and specificity is the probability of testing negative given the disease is absent. D D. Sensitivity is the proportion of true positives among all positives, and specificity is the proportion of true negatives among all negatives.

A A. Median B B. Mode C C. Mean D D. All are equally affected.

A A. Normal distribution B B. Exponential distribution C C. Binomial distribution D D. Uniform distribution (continuous)

A A. The result is important in a practical, real-world sense. B B. The result is unlikely to have occurred by chance alone, assuming the null hypothesis is true. C C. The result is definitely true and applicable to the entire population. D D. The result is due to a large sample size.

A A. To compare the means of two groups. B B. To analyze the variance within a single group. C C. To compare the means of three or more groups to see if at least one mean is different. D D. To analyze the correlation between two variables.

A A. The probability of rolling a 6 on a fair six-sided die. B B. The probability of drawing a King from a standard deck of cards. C C. The probability of rain tomorrow, given that it is cloudy today. D D. The probability of flipping heads on a fair coin.

A A. The mean of the sample is always equal to the population mean. B B. The sum of independent random variables will always follow a normal distribution. C C. The distribution of the sample mean approaches a normal distribution as the sample size increases, regardless of the population's distribution. D D. The variance of the sample is always equal to the population variance.

A A. The central tendency of a dataset. B B. The probability of an event occurring. C C. The spread or dispersion of data points around the mean. D D. The correlation between two variables.

A A. Type I error is failing to reject a false null hypothesis, and Type II error is rejecting a true null hypothesis. B B. Type I error is rejecting a true null hypothesis, and Type II error is failing to reject a false null hypothesis. C C. Type I error occurs when the sample size is too small, and Type II error occurs when the sample size is too large. D D. Type I error is made in parametric tests, and Type II error is made in non-parametric tests.

A A. Mean B B. Median C C. Range D D. Mode

A A. When the dependent variable is correlated with itself. B B. When independent variables are highly correlated with each other. C C. When the error terms in the regression model are correlated. D D. When there are multiple dependent variables in the model.

A A. T-test B B. ANOVA C C. Chi-square test D D. Regression analysis

A A. A variable whose value is fixed and known with certainty. B B. A variable whose value is a numerical outcome of a random phenomenon. C C. A variable that can only take on integer values. D D. A variable that is used to represent the sample space.

A A. To determine the probability that the null hypothesis is true. B B. To measure the effect size of the findings. C C. To indicate the strength of evidence against the null hypothesis. D D. To set the significance level of the test.

A A. To estimate the probability of a specific sample statistic. B B. To provide a range of values that is likely to contain the true population parameter. C C. To determine the exact value of a population parameter. D D. To test a hypothesis about a population parameter.

A A. Population is a subset of the sample. B B. Sample is the entire group we want to know about, and population is a subset of it. C C. Population is the entire group we want to know about, and sample is a subset of the population from which we collect data. D D. Population and sample are always the same.

A A. The average of the results obtained from a large number of trials should be close to the expected value. B B. Larger samples always lead to larger sample means. C C. The probability of rare events increases with more trials. D D. Sample variance always increases with sample size.

A A. To ensure every individual in the population has an equal chance of being selected. B B. To reduce bias by selecting participants randomly. C C. To divide the population into subgroups (strata) and then randomly sample from each stratum, ensuring representation of each subgroup. D D. To sample individuals based on their convenience and availability.

A A. Temperature in Celsius B B. Height in centimeters C C. Colors of cars (e.g., red, blue, green) D D. Ranking in a race (1st, 2nd, 3rd)

A A. To increase the sample size of a dataset. B B. To estimate the sampling distribution of a statistic by resampling with replacement from the original sample. C C. To reduce bias in data collection. D D. To calculate the exact probability of an event.

A A. To prove the research hypothesis. B B. To state what we expect to find. C C. To state the assumption of no effect or no difference, which we aim to disprove. D D. To accept it as true based on sample data.

A A. Variance B B. Standard Deviation C C. Interquartile Range D D. Median

A A. Normal distribution B B. Binomial distribution C C. Exponential distribution D D. Poisson distribution

A A. To determine the sample size needed for the test. B B. To adjust for the number of parameters estimated from the data, affecting the shape of the test statistic's distribution. C C. To measure the strength of the relationship between variables. D D. To set the significance level of the test.