Bài sắp đăng
Home | Trắc nghiệm | Kinh tế lượng | 500 câu trắc nghiệm Kinh tế lượng – 9C
Tổng hợp 500 câu trắc nghiệm + tự luận Kinh tế lượng (Elementary Statistics). Tất cả các câu hỏi trắc nghiệm + tự luận đều có đáp án

# 500 câu trắc nghiệm Kinh tế lượng – 9C

Tổng hợp 500 câu trắc nghiệm + tự luận Kinh tế lượng (Elementary Statistics). Tất cả các câu hỏi trắc nghiệm + tự luận đều có đáp án. Nội dung được khái quát trong 13 phần, mỗi phần gồm 3 bài kiểm tra (A, B, C). Các câu hỏi trắc nghiệm + tự luận bám rất sát chương trình kinh tế lượng, đặc biệt là phần thống kê, rất phù hợp cho các bạn củng cố và mở rộng các kiến thức về Kinh tế lượng. Các câu hỏi trắc nghiệm + tự luận của phần 9C bao gồm:

SHORT ANSWER. Write the word or phrase that best completes each statement or answers the question. Provide an appropriate response.
1) When testing to determine if linear correlation is significant, we use the hypothesis H0: $$\rho$$ = 0. H1: $$\rho$$ ≠ 0. What does the the symbol r represent? Explain the meaning for the null and alternative hypotheses.

The symbol r represents the population correlation as opposed to the sample correlation r. The null hypothesis means that the population correlation is 0; that is, there is no significant linear correlation between the two variables. The alternative hypothesis states that there is significant linear correlation.

2) Discuss the guidelines under which the linear regression equation should be used for prediction. Refer to the correlation coefficient, the type of data used to create the linear regression, and the predicting value.

The linear regression equation should be used only if the correlation is significant. Provided the correlation is significant, the linear regression equation should be used only when the predicting value x is within the scope of the data used to create the equation.

3) Define the term independent, or predictor, variable and the term dependent, or response, variable. Give examples for each.

The independent variable is x, representing values we will use for the prediction; the dependent variable is y, representing values we want to predict based on the values of the independent variable. Examples will vary.

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
Given the linear correlation coefficient r and the sample size n, determine the critical values of r and use your finding to state whether or not the given r represents a significant linear correlation. Use a significance level of 0.05.
4) r = 0.282, n = 90
○ Critical values: r = ±0.217, no significant linear correlation
○ Critical values: r = 0.217, significant linear correlation
● Critical values: r = ±0.207, significant linear correlation
○ Critical values: r = ±0.207, no significant linear correlation

Find the value of the linear correlation coefficient r.
5) The paired data below consist of the temperatures on randomly chosen days and the amount a certain kind of plant grew (in millimeters):

 Temp 62 76 50 51 71 46 51 44 79 Growth 36 39 50 13 33 33 17 6 16

○ 0.256
○ 0
● 0.196
○ -0.210
SHORT ANSWER. Write the word or phrase that best completes each statement or answers the question. Describe the error in the stated conclusion.
6) Given: Each school in a state reports the average SAT score of its students. There is a significant linear correlation between the average SAT score of a school and the average annual income in the district in which the school is located.

Conclusion: There is a significant linear correlation between individual SAT scores and family income.

Averages suppress individual variation and tend to inflate the correlation coefficient. The fact that there is significant linear correlation between average SAT scores and average incomes in the district does not necessarily imply that there is significant linear correlation between individual SAT scores and family incomes.