Human capital theory suggests that those who have invested in higher levels of education will be able to command higher wages. A labour economist collected data on annual wages (Y, in $'000) and years of study (X) from a random sample of 12 employees to test this proposition. Assuming a linear relationship between Y and X, the labour economist used a least-squares method and found that the Y intercept = -23.50 and the slope = 9.73. The labour economist also found that the standard error of the slope was 1.61. 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 1% level of significance? Use our textbook statistical table to answer the question

Human capital theory suggests that those who have invested in higher levels of education will be able to command higher wages. A labour economist collected data on annual wages (Y, in $'000) and years of study (X) from a random sample of 12 employees to test this proposition. Assuming a linear relationship between Y and X, the labour economist used a least-squares method and found that the Y intercept = -23.50 and the slope = 9.73. The labour economist also found that the standard error of the slope was 1.61. Based on this information, what statistical decision would you made if you are testing the null hypothesis that there is no linear relationship between the two variables, X and Y? a. Accept the null hypothesis. b. Reject the null hypothesis. c. Do not reject the null hypothesis. d. Reject the alternative hypothesis.

Human capital theory suggests that those who have invested in higher levels of education will be able to command higher wages. A labour economist collected data on annual wages (Y, in $'000) and years of study (X) from a random sample of 12 employees to test this proposition. Assuming a linear relationship between Y and X, the labour economist used a least-squares method and found that the Y intercept = -23.50 and the slope = 9.73. Also, the sum of squares total (SST) and the error sum of squares (SSE) were equal to 11132.92 and 2406,01, respectively. Based on this information, we can say that a. around 21.61% of the variation in Y is explained by the variation in X. b. around 21.61% of the sample variability in Y is due to factors other than X c. around 78.39% of the sample variability in Y is due to factors other than X d. around 21.61% of the variation in X is explained by the variation in 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 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 UQ master student collected data on annual wages (Y, in $'000) and years of study (X) from a random sample of 10 part-time workers to test this proposition. Assuming a linear relationship between Y and X, the student used a least-squares method and found that the Y intercept = -21.50 and the slope = 9.73. The student also found that the standard error of the slope was 1.61. 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 three 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. Based on this information, the slope should be interpreted as: a. For each increase of $1 in a student's weekly income, his/her mean value of weekly spending is estimated to increase by $0.66 (or 66 cents). b. For each increase of $1in a student's weekly spending, his/her mean value of weekly income is estimated to increase by $0.66 (or 66 cents). c. For each increase of $1 in a student's daily income, his/her mean value of daily spending is estimated to increase by 0.66 cents. d. For each increase of $1in a student's weekly income, his/her mean value of weekly spending is estimated to increase by $20.90.