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 regression sum of squares (SSR) were equal to 65600.74 and 52831.23, respectively. What is the upper bound of a 95% prediction interval for a student's spending on a weekly income of $60. Assume the h Statistic equals 0.12. a. greater than $80 but less than $120 b. greater than $100 but less than $120 c. greater than $120 d. less than $100

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.

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 is the value of the t test statistic if you are testing the null hypothesis that there is no linear relationship between the two variables, X and Y? Round your final answer to two decimal places.

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 is the upper critical value used to test the null hypothesis that there is no linear relationship between the two variables, X and Y at the 5% level of significance? Use our textbook statistical table to answer the question.

A company's investment is widely believed to be a function of  interest rate. To estimate this relationship, a professor randomly selected 20 of companies in the market and collected information on their investment (Y, in millionss) and interest rate (X) patterns. Assuming a linear relationship between Y and X, the professor used the least-squares method and found that the Y intercept = 15.50 and the slope = -0.46. Also, the sum of squares total (SST) and the regression sum of squares (SSR) were equal to 46980.74 and 32831.23, respectively.  Based on this information, the standard error of the estimate is equal to __________. Round your final answer to two decimal places.

A financial analyst wants to determine the relationship between a company's earnings per share (EPS, Y, in dollars) and its revenue (X, in million dollars). Assuming a linear relationship between Y and X, the analyst used the least-squares method and found that the Y-intercept = 1.75 and the slope = 0.42. Also, the sum of squares total (SST) and the error sum of squares (SSE) were equal to 82000.12 and 15200.43, respectively.Based on this information, what is the coefficient of determination? Round your final answer to four decimal places. Note: don't put your answer in percentage form.