Essay about Stat Project - 1698 Words

Stat 113 Beiyi(Summer) Liu Professor Ihsan Shahwan Final Project Part C In order to figure out how variables relates to each other and the connections among the variables, or one can predict the other. I will choose three quantitative variables or two quantitative variables and one categorical variable on each pairs. I will also use graphs of scatter plots; regression and correlation to understand that how one variable affect other two variables. There are six groups below: Group one: High School Percentile (HSP), Cumulative GPA (GPA), and ACT Composition Score (COMP) a) HSP vs GPA b) HSP vs COMP c) COMP vs GPA From graph a, we can find out that there is moderate†¦show more content†¦From graph b, there is weak positive liner relationship between CREDITS and GPA; the correlation is 0.106; the equation of regression is GPA=0.00141886*CREDITS+2.94831; the slope is 0.00141886 which is positive; when the predictor variable CREDITS increase, the response variable GPA also weakly increase; for example, when CREDITS increase by 1, the GPA will increase 0.00141886. From graph c, there is a strong positive liner association between AGE and CREDITS; the correlation is 0.668; the equation of regression is CREDITS=11.7475*AGE-174.356; the slope is 11.7475 which is positive; when the predictor variable AGE increase, the response variable CREDITS also strongly increase; for instance, when AGE increase by 1, the CREDITS will increase 11.7475. There are some outliers may affect the correlation. Based on the graphs and data above, we can find out a student who is older with a litter lower GPA, but has very higher credits; the student with higher credits also has high GPA. Group Four: ACT English Score (ENGLISH), ACT Composition Score (COMP) and Age (AGE) a) AGE vs ENGLISH b) AGE vs COMP c) ENGLISH vs COMP From graph a, we can see that there is a weak negative liner relationship between AGE and English scores; the correlation is -0.042; the equation of regression is ENGLISH=-0.0814809*AGE+24.469; the slope is -0.0814809 which is negative; when the predictorShow MoreRelatedStat Project1145 Words   |  5 Pages| | | | Regression | 3 | 609.527 | 203.1757 | 130.0599 | 7.56E-06 | | | | Residual | 6 | 9.373017 | 1.562169 | | | | | | Total | 9 | 618.9 |    |    |    | | | | | | | | | | | | |    | Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | Intercept | -10.1702 | 3.473129 | -2.92827 | 0.026346 | -18.6687 | -1.6718 | -18.6687 | -1.6718 | Food Sales (tens of thousands of dollars) | 0.027038 | 0.012041 | 2.245505 | 0.065847Read MoreMy Natural Characteristics Of The Management Consulting Module1935 Words   |  8 PagesHighlighting my natural characteristics of being a logical thinker and operational planner, the assigned summer project would strengthen and amplify my knowledge and understanding of wider issues surrounding the business and industry, moreover it would make me responsive to the ramification of future trends in the market environment. 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