Adam's Adv Stats & Analytics Blog

  Module 10. Assignment: Question A:   A1:  In the report, please state the result of coefficients and significance to any variables you like both under ANOVA and multivariate analysis. Please provide a specific interpretation of R results.    In this analysis, I used pemax as the dependent variable for the multiple regression experiment. Results: Weight = (p = 0.03287) Bmp = (p = 0.02036) Fev1 = (p = 0.04695) Age = (p = 0.31389)   Age is the only variable that isn't significant. The p-values of weight, bmp, and fev1 are all significant. Weight: A one-unit increase in weight increases the predicted pemax by 2.6882 Bmp: A one-unit increase in bmp decreases pemax by 2.0657 units. Fev1: A one-unit increase in fev1 increases pemax by 1.0882 units.   ANOVA test results: Age = (p = 0.00035) Weight = (p = 0.2038) Bmp = (p = 0.050) Fev1 = (p = 0.0469) The weight variable in the regression model is significant unlike in the ANOVA. The age variable in the ANO...

  Module 9. Assignment: R_Code: Question A: df <- data.frame( country = c("France", "Spain", "Germany", "Spain", "Germany", "France", "Spain", "France", "Germany", "France"),   age = c(44, 27, 30, 38, 40, 35, 52, 48, 45, 37),   salary = c(6000, 5000, 7000, 4000, 8000, 6000, 5000, 7000, 4000, 8000), purchased = c("No", "Yes", "No", "No", "Yes", "Yes", "No", "Yes", "No", "Yes") ) A1:  Generate a one-way table for "purchased" A2:  Generate a two-way table for "c ountry" and "purchased." Question B: data ( mtcars ) mtcars_df <- table ( mtcars $ gear , mtcars $ cyl , dnn = c ( "Gears" , "Cylinders")) B1:  Add the addmargins() function to report on the sum totals of the rows and columns of " mtcars_df" table B2:  A dd p...

Module 8. Assignment: Question A: Results: After the ANOVA test was performed in R, we got these results:   Source: Group; Df: 2 Sum Sq: 82.11 Mean Sq: 41.06 F-value: 21.36 Pr(>F): 4.08e-05 Source: Residuals; Df: 15 Sum Sq: 28.83 Mean Sq: 1.92 Interpretation: We used a one-way ANOVA to test if stress levels affect the reaction scores after taking the drug. Therefore, our null hypothesis (H_O) is if the mean reaction score is the same for all three stress groups. Next, our alternative hypothesis (H_1) is if at least one group is different. After running the test, we gathered a p-value of 4.08e-05, which is significantly smaller than the significant level of 0.05. Therefore, we have no choice but to reject the null hypothesis. Meaning, that there is a notable difference between the stress groups and that stress levels play a large role on the reaction scores after taking the drug. Those under high stress tend to have higher scores, indicating that the drug's effect may vary depend...

  Module 7. Assignment: Question A: A1:  Define the relationship model between the independent and the dependent variable. The simple linear regression model is:  Y = a +bX + e, where: y = dependent variable x = independent variable a = intercept (the value of y when x = 0) b = slope e = error  A2:  Calculate the coefficients. The calculated regression equation is: y = 19.2056 + 3.2691x, where: Intercept = 19.2056 Slope = 3.2691  Question B: B1:  Define the relationship model between the predictor and the response variable. Discharge = a + b(waiting) Discharge = response variable Waiting = predictor variable  B2:  Extract the parameters of the estimated regression equation with the coefficients function. My model is: discharge = -1.874 + 0.0756(waiting), where: Intercept = -1.874 waiting = 0.0756  B3:  Determine the fit of the eruption duration using the estimated regression equation. y = -1.874 + 0.0756(80) y = -1.874 + 6.0504 y...