# Assume that your company assigned you to work on the impact of education spending on unemployment and graduation rates. The attached xls file covers a 31-year period of data gathered for a state. Open the attached file including the data and start analyzing the data using statistical analysis tools depending on the nature of the questions. You can use Excel’s insert function to compute the correlations between the variables. . How correlated are gross state product per capita and education spending per student? Compute and analyze the results. 2. How correlated are gross state product per capita and unemployment rates? Compute and analyze the results. 3. How correlated are gross state product per capita and graduation rates? Compute and analyze the results. 4. After reviewing the data, what hypotheses would you come up with? (An example hypothesis: The higher per capita income the state has, the more it spends on education). 5. From regression analysis point of view, how would you predict the impact of independent variable (gross state product per capita) on dependent variables (education spending per student; unemployment rates; and high school graduation rates)? Briefly explain based on your general knowledge. You do not need to compute or run any statistical tools.

The analysis of the impact of education spending on unemployment and graduation rates requires conducting several statistical analyses using the provided dataset. In order to answer the given questions, correlations between gross state product per capita and the variables of interest, namely education spending per student, unemployment rates, and graduation rates, need to be computed and analyzed.

To begin, we can use Excel’s insert function to calculate the correlation coefficient between gross state product per capita and education spending per student. The correlation coefficient measures the strength and direction of the linear relationship between two variables. A positive correlation coefficient indicates a direct relationship, while a negative correlation coefficient suggests an inverse relationship.

Once the correlation coefficient between gross state product per capita and education spending per student is computed, its value can be analyzed. If the correlation coefficient is close to 1, it indicates a strong positive relationship, meaning that as gross state product per capita increases, education spending per student also tends to increase. On the other hand, if the correlation coefficient is close to -1, it suggests a strong negative relationship, indicating that as gross state product per capita increases, education spending per student tends to decrease. A correlation coefficient close to 0 indicates a weak or no relationship between the two variables.

Next, we can calculate the correlation coefficient between gross state product per capita and unemployment rates. This analysis will provide insight into the relationship between a state’s economic performance and its unemployment rates. A positive correlation coefficient would suggest that as gross state product per capita increases, unemployment rates also tend to increase, possibly indicating a higher demand for labor. Conversely, a negative correlation coefficient would indicate that as gross state product per capita increases, unemployment rates tend to decrease, suggesting a stronger economy with more job opportunities.

Similarly, we can compute the correlation coefficient between gross state product per capita and graduation rates. This analysis will help understand the relationship between a state’s economic performance and its educational outcomes. A positive correlation coefficient between these variables would imply that as gross state product per capita increases, graduation rates also tend to increase, possibly indicating better educational resources and opportunities. Conversely, a negative correlation coefficient would suggest that as gross state product per capita increases, graduation rates tend to decrease, pointing towards potential socioeconomic factors influencing educational attainment.

After reviewing the data and conducting these analyses, several hypotheses can be formulated. For instance, one hypothesis could be that states with higher per capita income tend to invest more in education, leading to higher education spending per student. Another hypothesis may be that higher per capita income is associated with lower unemployment rates, indicating a stronger economy. Lastly, it can be hypothesized that states with higher per capita income exhibit higher graduation rates, suggesting a positive relationship between economic prosperity and educational outcomes.

From a regression analysis perspective, predicting the impact of gross state product per capita on the dependent variables, education spending per student, unemployment rates, and graduation rates, would require running regression models. Regression models estimate the relationship between independent and dependent variables, allowing for predictive analysis. However, as per the assignment instructions, there is no need to perform these calculations and analyses.

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