A consumer's spending is widely believed to be a function of their income. To estimate this relationship, a university professor randomly selected 19 of his students and collected information on their spending (Y, in dollars) and income (X, in dollars) patterns in week 6 of the semester. Assuming a linear relationship between Y and X, the professor used the least-squares method and found that the Y intercept = 20.90 and the slope = 0.66. The professor also found that the standard error of the slope was 0.08. Based on this information, what conclusion should you reached at the 5% level of significance when testing the null hypothesis that there is no linear relationship between the two variables, X and Y? a. There is sufficient evidence at the 5% level of significance to conclude that there is no significant linear relationship between X and Y. b. There is sufficient evidence at the 5% level of significance to conclude that there is a significant linear relationship between X and Y. c. There is sufficient evidence at the 5% level of significance to conclude that there is a significant linear relationship between the Y intercept and the slope. d. There is insufficient evidence at the 5% level of significance to conclude that there is a significant linear relationship between X and Y.
Question
A consumer's spending is widely believed to be a function of their income. To estimate this relationship, a university professor randomly selected 19 of his students and collected information on their spending (Y, in dollars) and income (X, in dollars) patterns in week 6 of the semester. Assuming a linear relationship between Y and X, the professor used the least-squares method and found that the Y intercept = 20.90 and the slope = 0.66. The professor also found that the standard error of the slope was 0.08. Based on this information, what conclusion should you reached at the 5% level of significance when testing the null hypothesis that there is no linear relationship between the two variables, X and Y? a. There is sufficient evidence at the 5% level of significance to conclude that there is no significant linear relationship between X and Y. b. There is sufficient evidence at the 5% level of significance to conclude that there is a significant linear relationship between X and Y. c. There is sufficient evidence at the 5% level of significance to conclude that there is a significant linear relationship between the Y intercept and the slope. d. There is insufficient evidence at the 5% level of significance to conclude that there is a significant linear relationship between X and Y.
Solution
Answer: To test the null hypothesis that there is no linear relationship between the two variables, X and Y, we need to calculate the t-statistic for the slope and compare it to the critical t-value at the 5% level of significance.
The t-statistic is calculated as (b1 - 0) / SE(b1), where b1 is the slope and SE(b1) is the standard error of the slope.
So, t = (0.66 - 0) / 0.08 = 8.25
For a two-tailed test with 19 - 2 = 17 degrees of freedom, the critical t-value at the 5% level of significance is approximately ±2.11 (you can find this value in a t-distribution table or using a statistical software).
Since the calculated t-statistic (8.25) is greater than the critical t-value (2.11), we reject the null hypothesis.
Therefore, the correct answer is:
b. There is sufficient evidence at the 5% level of significance to conclude that there is a significant linear relationship between X and Y.
Similar Questions
A consumer's spending is widely believed to be a function of their income. To estimate this relationship, a university professor randomly selected 19 of his students and collected information on their spending (Y, in dollars) and income (X, in dollars) patterns in week 6 of the semester. Assuming a linear relationship between Y and X, the professor used the least-squares method and found that the Y intercept = 20.90 and the slope = 0.66. Also, the sum of squares total (SST) and the error sum of squares (SSE) were equal to 65600.74 and 12769.51, respectively. Based on this information, the coefficient of determination can be interpreted as follows a. 80.53% of the variation in weekly income is explained by the variation in a student's weekly spending. b. 19.47% of the variation in weekly spending is explained by the variation in a student's weekly income. c. 80.53% of the variation in weekly spending is explained by the variation in a student's weekly income. d. 19.47% of the variation in weekly income is explained by the variation in a student's weekly spending.
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