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Course Project Part C

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1. Scatterplot for Credit Balance vs Size Analyzing the scatterplot credit balance vs size, it seems like size (x) will be a good predictor of credit balance (y). The line has a positive slope, and shows that when x increase per 1 unit, y will change by the slope. 2. Equation that describes the relationship between Credit Balance vs Size Y = β0 + β1 β0 : y – intercept β1 : slope Credit Balance($) = 2591.44 + 403.221 Size 3. Coefficient of correlation R = √R2 R = √ 0.5662 = 0.75246262 When we have a high coefficient of correlation like 0.75, means that there is a strong linear relationship between the credit balance and the size. 4. Coefficient of determination R2 = 0.5662 = 56.62 % The …show more content…

Reject H0 X1 (Income): T- Value = 7.42196 is greater than F0.025 = 2. Reject H0 X2 (size): T- Value = 9.62668 is greater than F0.025 = 2. Reject H0 X3 (years): T- Value = 0.63885 is less than F0.025 = 2. Do not reject H0 We have strong evidence that X1 and X2 are significantly related to Y. However, there is insufficient evidence that X3 is related to Y. Therefore, we should keep X1 and X2, but discard X3. 14. Is the Multiple Regression model better than the Linear model? All the results show the relationship between income and size with credit balance, which have sense because with high income and larger household you have more expenses, which translate in more credit balance. Nevertheless, how many years you live in an area don’t necessarily mean more credit balance. This support that Multiple Linear Regression model is better than the Simple Linear Regression, because it show the relationship with the variables more accurately and you can know which one to discard, and which one to

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