Final Exam Pad 715

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CUNY John Jay College of Criminal Justice *

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715

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Economics

Date

Apr 3, 2024

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pdf

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2

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1.The variables study time and grade average have a correlation of 0.87. Explain what this means. The variables education level and poverty level have a correlation of -0.71. Explain what this means. [5] Answer: In this case, the correlation between study time and grade average is 0.87. This positive correlation is close to 1, which indicates a strong positive linear relationship between study time and grade average, as study time increases, the grade average also tends to increase. The closer the correlation coefficient is to 1, the stronger the relationship between the variables. Therefore, there is a very strong positive correlation between study time and grade average ( r = 0.87). This suggests that the longer the study time, the higher the grade average. B.The correlation between education level and poverty level, which is -0.71. This negative correlation coefficient, close to -1, indicates a strong negative linear relationship between education level and poverty level. As education level increases, poverty level tends to decrease. Therefore, there is a strong negative correlation between education level and poverty level ( r = 0.71). This implies that the higher the education level, the lower the poverty level. 2. Tiger Woods played 10 rounds of golf with his son, Charlie last month. His scores were: 67,68,65,71,73,68,70,72,64, and 62. Find the mean, median, and mode of Tiger’s scores. [5] Answer: The scores are: 67, 68, 65, 71, 73, 68, 70, 72, 64, and 62. To find the mean, we sum up all the scores and divide by the total number of scores. So, the mean of Tiger Woods' scores is 69. To find the median, we arrange the scores in ascending order and find the middle value. The median of Tiger Woods' scores is 68. The mode is the score that appears most frequently. In this case, there is no score that appears more than once, so there is no mode.The absence of a mode tells us that there wasn't a score that Tiger Woods hit more frequently than others. 3. The regression model below examines factors that impact the DV home sale price among recently sold homes in a suburban community. The IV is total rooms within the home, and the
EVs include 1) total bedrooms, 2) total bathrooms, 3) whether the home has a basement or not, and 4) the total number of days the home was on the market prior to its sale. Please answer the following regarding the regression output below: A) Identify the variables that are statistically significant predictors (at the five percent level) of a home’s sale price. Explain what metrics you examined to determine each variable’s statistical significance. [10] B) Explain what the coefficient for the EV “Bathrooms” means. [3] C) Explain what the coefficient for the EV “Days on Market” means. [3] D) What does the model’s R-squared indicate? The regression output shows several variables that could potentially impact the home sale price. With a p-value less than 0.05, variables that are statistically significant (at the five percent level) are predictors of a home's sale price. In this case, variables like "bathrooms" and "basement" are statistically significant predictors, since their p-values are 0.000 and 0.078, respectively. Each of the variables "bathrooms" has a coefficient of 43553.03. This means that for every additional bathroom in a home, the sale price is expected to increase by $43,553.03. The R-squared of the model is 0. 3477. Using the independent variables (total rooms, bedrooms, bathrooms, basement, days on market), this value represents the proportion of variance in the dependent variable (home sale price) that can be predicted. Therefore, about 33. These variables explain 47% of the variability in home sale prices. Coefficient for "Days on Market" is -72.53292. For each additional day on market, sale price is expected to decrease by $72.
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