Đề 5 – Bài tập, đề thi trắc nghiệm online Thống kê trong kinh tế và kinh doanh

A A. Comparing groups at a single point in time. B B. Analyzing data collected over time to identify patterns and trends. C C. Examining relationships between variables at a single point in time. D D. Categorical data analysis.

A A. To increase production speed regardless of quality. B B. To monitor and improve production processes, identify defects, and ensure product consistency. C C. To replace manual inspection entirely. D D. To make products more expensive.

A A. A perfect linear relationship between the dependent and independent variable. B B. A high correlation between two or more independent variables. C C. A lack of correlation between the independent variables. D D. Non-linearity in the data.

A A. The hypothesis that the researcher wants to prove. B B. A statement of no effect or no difference, which is tested against the alternative hypothesis. C C. The hypothesis that is always true. D D. The hypothesis based on sample data.

A A. Mean B B. Median C C. Mode D D. Standard Deviation

A A. The probability of making a Type II error. B B. The probability of rejecting a true null hypothesis (Type I error). C C. The power of the test. D D. The confidence level.

A A. Bar chart B B. Pie chart C C. Scatter plot D D. Histogram of residuals

A A. Stratified sampling is always cheaper. B B. Stratified sampling can reduce sampling error and improve representativeness, especially when subgroups within the population are of interest. C C. Simple random sampling is more complex to implement. D D. Stratified sampling is less prone to bias.

A A. Type I error is always more serious. B B. Type II error is always more serious. C C. The seriousness depends on the specific business context. D D. Neither type of error is serious in business.

A A. The central tendency of data. B B. The spread or dispersion of data points around the mean. C C. The median value of data. D D. The most frequent value in data.

A A. A large group of people B B. The entire set of individuals, items, or data points of interest in a study C C. A subset of data used for analysis D D. The general public

A A. To prove a theory is absolutely true B B. To provide evidence to support or reject a claim about a population C C. To calculate descriptive statistics D D. To create data visualizations

A A. It is too expensive. B B. It may not be representative of the population. C C. It always requires a large sample size. D D. It is difficult to implement.

A A. Nominal data B B. Ordinal data C C. Interval data D D. Ratio data

A A. To confirm pre-existing hypotheses. B B. To summarize and describe the main characteristics of a dataset and uncover patterns. C C. To build predictive models. D D. To perform hypothesis testing.

A A. Chi-square test B B. Paired t-test C C. Independent samples t-test D D. ANOVA

A A. A causal relationship between the variables. B B. A strong linear relationship between the variables. C C. No relationship between the variables. D D. A non-linear relationship between the variables.

A A. To determine the exact value of a population parameter. B B. To provide a range of plausible values for a population parameter. C C. To test hypotheses about sample statistics. D D. To describe the sample data.

A A. Descriptive statistics and confidence intervals B B. Hypothesis testing for proportions C C. Regression analysis D D. Time series analysis

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

A A. Temperature in Celsius B B. Customer's income in dollars C C. Ranking of employees performance D D. Colors of cars

A A. The range of all data values. B B. The range of the middle 50% of the data. C C. The average of the upper and lower quartiles. D D. The standard deviation multiplied by the sample size.

A A. To describe the sample data. B B. To make generalizations about a population based on sample data. C C. To calculate descriptive statistics. D D. To organize and present data.

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

A A. To identify seasonal patterns in data. B B. To smooth out short-term fluctuations and highlight longer-term trends. C C. To measure the volatility of data. D D. To predict exact future values with high accuracy.

A A. A parameter is calculated from a sample, while a statistic is calculated from a population. B B. A parameter describes a population, while a statistic describes a sample. C C. There is no difference; they are interchangeable terms. D D. A parameter is always known, while a statistic is always unknown.

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 explained by the independent variable(s). D D. The standard error of the regression model.

A A. Statistical models are always perfectly accurate. B B. Statistical models can only be used for large datasets. C C. Statistical models rely on assumptions that may not always hold true in real-world scenarios. D D. Statistical models eliminate all uncertainty in decision-making.

A A. The null hypothesis is likely true. B B. There is strong evidence to reject the null hypothesis. C C. A Type II error has occurred. D D. The sample size is too small.

A A. To make the data look visually appealing. B B. To transform data into different formats. C C. To identify and correct errors, inconsistencies, and missing values in data. D D. To perform statistical calculations.