Table 1.1

Preliminary Observation (X1)

The graph displayed a positive linear relationship between the dependent variable, Alumni Giving Rate, and the independent variable, Graduation Rate.

This indicates that as the Student–Faculty ratio increases, the percentage of Alumni contribution increases as well. This corresponded to the assumption that if more students graduate, they will donate back to the school, thus increasing the alumni contribution.

The linear relationship is strong as the dots are gathered closely together in the scatter graph. It implies that the independent variable could be used as a predictor of the Alumni Giving Rate. [pic]

Table 1.2

Preliminary Observation (X2)

The graph displayed a positive linear relationship between the dependent variable, Alumni Giving Rate, and the independent variable, Percent of Classes under 20.

This indicates that as the number of smaller classes with fewer than 20 students’ increases, the percentage of Alumni contribution increases as well. This corresponded to the assumption that having smaller classes will increase the alumni contribution rate.

The linear relationship is moderately strong as the dots are gathered only fairly closely together in the scatter graph. It implies that the independent variable can be used as a predictor of the Alumni Giving Rate but might not be the only factor that we should consider.

[pic]

Table 1.3

Preliminary Observation (X3)

The graph displayed a negative linear relationship between the dependent variable, Alumni Giving Rate, and the independent variable, Student–Faculty Ratio.

This indicates that as the Student–Faculty ratio increases, the percentage of Alumni contribution decreases. This corresponded to the assumption that a smaller student to faculty ratio will increase the alumni contribution.

The linear relationship is strong as the dots are gathered quite close together. It implies that the independent variable could be used as a predictor of the Alumni Giving Rate.

Alumni Giving Rate vs. Location (X4)

As location is a qualitative data, it cannot be plotted against the variable. However, it is my belief that the variable could still influence the alumni contribution unless proven otherwise. Prof. Jimmy commented that due to a strong correlation between the Alumni Giving Rate and the Student–Faculty Ratio, a 50% contribution rate can be achieved if the ratio is 15. To test his claims, I have devised the following statistical tests.

|Model Summary | |Model |

Table 2.1

|Coefficientsa | |Model |Unstandardized Coefficients |Standardized |T |Sig. | | | |Coefficients | | | | |

Table 2.2

|ANOVAb | |Model | |b. Dependent Variable: Alumni Giving Rate (%) |

Table 2.3

Linear Regression and Relationship

From the r value, the correlation coefficient, generated in Table 2.1, we can determine that there is a strong relationship between the dependent variable, Alumni Giving Rate, and the independent...

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