1 / 17

Phone Contacts Vs GPA

Phone Contacts Vs GPA. Is there a Correlation between the number of Contacts in someone's phone and their G.P.A?. Intro. We felt that the number of phone contacts vs. GPA was a unique comparison We felt that any correlation would be interesting to see; even if there was no correlation.

walter-gallegos
Télécharger la présentation

Phone Contacts Vs GPA

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Phone Contacts Vs GPA Is there a Correlation between the number of Contacts in someone's phone and their G.P.A?

  2. Intro • We felt that the number of phone contacts vs. GPA was a unique comparison • We felt that any correlation would be interesting to see; even if there was no correlation

  3. Univariate Analysis of GPA • Mean X= 3.45 • SX= .476 Outlier test= Q3-Q1 * 1.5= .7125 Outlier= 2.74 <X<4.16 Outliers are 1.8, 2.5, 2.7

  4. Univariate Analysis of Contacts • Mean X= 93.576 • SX= 60.597 Outlier test= Q3-Q1 * 1.5= 158.25 Outlier= -64.674<X<251.826 No Outliers.

  5. Explanatory & Response Variable • Explanatory = GPA • Response= # of Phone Contacts The GPA of a student affects the amount of contacts they have in their phone because people with higher GPA’s spend more time studying, and therefore less time with friends

  6. Data • Form: Linear • Direction: Negative • Strength: Moderate

  7. Raw Data GPA Contacts GPA Contacts

  8. Variation • Explained variation = sum (ŷ – y-mean)2 • = 25453.37673 • Unexplained variation = sum (y – ŷ)2 • =92048.68388 • Total variation = sum (y – y-mean)2 • =117502.0606

  9. r = -.4654, r2= .2166 or 21.7% • c.v=.335 so r>c.v • Regression line – Y= 297.9936 + -59.309x • There is a Negative correlation between the GPA and number of contacts. The lower the GPA= More contacts; Higher GPA= Less contacts.

  10. GPA X Y # of contacts

  11. Histogram cont’d • Both histograms have an equal distribution For GPA: Outliers are 1.8, 2.5, 2.7 For Contacts: No Outliers • Conforms with Empirical Rule Test

  12. Empirical Rule Test for GPA: 68% of the data falls between the values 3.591 – 0.3445 = 3.2465 3.591 + 0.3445 = 3.9355 95% of the data falls between the values 3.591 – 2(0.3445) = 2.902 3.591 + 2(3.445) = 4.28 99.7% of the data falls between the values 3.591 – 3(0.3445) = 2.5575 3.591 + 3(0.3445) = 4.6245 Empirical Rule Test for Current Events Scores: 68% of the data falls between the values 0.6445 – 0.2896 = 0.3549 0.6445 + 0.2896 = 0.9341 95% of the data falls between the values 0.6445 – 2(0.2896) = 0.0653 0.6445 + 2(0.2896) = 1.2237 99.7% of the data falls between the values 0.6445 – 3(0.2896) = -0.2243 0.6445 + 3(0.2896) = 1.5133 Empirical Rule Test

  13. (y – y)2 ^ 92048.68 n –2 31 Standard Error se = se = se = 54.49

  14. 33 (3.7 – 93.5758)2 n(x0–x)2 1 1 + + 1.03 + E = t2se E = 2.04 n n(x2) – (x)2 33(399.26) – (12936.79) 95% Prediction Interval (X0 = 3.7) E = 68.19

  15. ^ ^ 78.551 – 68.19 < y < 78.511 + 68.19 y - E < y < y + E 95% Prediction Interval (cont’d) 10.361 < y < 146.741 There is a very large prediction interval, due in part to the small r and r2 values.

  16. Residuals This shows linear correlation because the plots are randomly scattered and there is no patter on the residual graph

  17. Conclusion • In conclusion we found out that there was a weak correlation on students GPA and the amount of contacts they have in their phone. Since it was so weak it is only true a very little % of the time. 4.9 GPA- 4 contacts (Mom, Dad, Home, and Steve)

More Related