Sensitivity Analysis

Refer to the complete listing of characteristics in the C-D Model before completing the chart. Use specific examples, not generalities

Iterations of analysis eliminated data points that were listed as “unusual observations,” or any data point with a large standardized residual. After 5 iterations, the analysis showed improved residual plots. Randomness in the versus fits and versus order plots means that the linear regression model is appropriate for the data; a straight line in the normal probability plot illustrates the linearity of the data, and a bell shaped curve in the histogram illustrates the normality of the data.

Table 6.1.1 displays the matlab output of beta, standard error, t-statistic and p-value for the two independent variables during 10-year period. It is found that beta of X1 is 0.2750 which indicates there is a positive relationship between the utilities excess return and the healthcare excess return. This positive relationship is statistically significant as the p-value is close to 0 which is much less than the significance level of 5%. In addition, the standard error of X1 is 0.0300 which represents the average distance that the observed values fall from the regression line. This indicates that the model fits the data. In contrast, it is found that the material excess return is negatively

My original model was able to pass most of the assumptions but not all. The error terms were normal (Figure 1) and there were no serious outliers, multicollinearity, or autocorrelation. However, the scatter plot of the predicted Y values and the residuals showed signs of heteroscedasticity, depicted in Figure 2. Given that, I transformed my dependent variable by squaring the values of the dependent variables. This corrected the heteroscedasticity, as shown in Figure 3 and the error terms still followed a normal distribution, as given by Figure 4. My final model now checked off all of the assumptions and I could move forward.

Model Fit Summary CMIN Model | NPAR | CMIN | DF | P | CMIN/DF | Saturated model | 36 | .000 | 0 | | | Independence model | 8 | 3797.971 | 28 | .000 | 135.642 | RMR, GFI Model | RMR | GFI | AGFI | PGFI | Saturated model | .000 | 1.000 | | |

6. Why is the black line so much more variable than the red line? What 's the difference between the data they show?

29. A distribution center for a chain of electronics supply stores fills and ships orders to retail outlets. A random sample of orders is selected as they are received and the dollar amount of the order (in thousands of dollars) is recorded, and then the time (in hours) required to fill the order and have it ready for shipping is determined. A scatterplot showing the times as the response variable and the dollar amounts (in thousands of dollars) as the predictor shows a linear trend. The least squares regression line is determined to be: yˆ= 0.76 +1.8x. A plot of the residuals versus the dollar amounts showed no pattern, and the following values were reported: Correlation r +0.90; R 2 = 0.81; standard deviation of the residuals is 0.48. What percentage of the variation in the times required to prepare an order for shipping is accounted for by the fitted line?

14. Is this multiple regression model better than the linear model that we generated in parts 1-10? Explain. No the linear regression has a p-value less than alpha so we reject Ho and concluded that

The process of the collection of an underground fluid would not be possible without the use of hydraulic fracturing. In the Shale reserves, located about 5,000 feet underground, suffer an extremely low permeability rate. Permeability is the measure of how well a fluid flows through an absorbent material at the depth, and within such nonporous rock, the ability of fluids to travel to the well is greatly limited. Fracturing increases the area of the fluid that is exposed to porous materials and thus greatly increases production. The method of fracturing utilizes a few key components which allow for an economical extraction of resources.

Looking at the crosstab, there are five dependent variable attributes/rows. The first distinction between two dependent attributes interests me the most is college diploma (e.g., bachelors degree). It has a standard residual of .0 for Asian, -.7 for Black, -.7 for Hispanic and 1.3 for Whites. The results show that Black and Hispanic are less likely to have completed college. As for Whites, it results showed that they are

(TCO 3) Before performing linear regression, it is important to ensure that a linear relationship exists between the dependent and independent variables by plotting observed

The residual plot seems to be random with no clear pattern so it does not indicate any curvature. There are a few points that are remarkably deviated such as the one at 1992 or the one at 1987. Again, these seem more like outliers more than anything else. Overall, most of the residual points are relatively small Therefore, the LSRL appears to be a reasonably fair model for this relationship between years and average numbers of home runs.

For ages, our society has dealt with constant controversies about what is right and what is wrong. In these debates, conflicting perspectives are often exposed to many. People are often taught to permit any different viewpoints, but in reality, continuous acceptance leads to more harm than good. The tolerance of others is essential in our society; however, the line of acceptance is drawn when one’s differences promotes physical violence.

Fit SE Fit Residual St Resid 9 5.0 7820 4542 182 3278 2.67R 18 10.9 5043 4752 533 292 0.26 X 43 7.3 7365 4624 243 2741 2.25R 48 9.9 2107 4716 446 -2609 -2.25RX R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large

As stated previously, the objective of this porosity lab is to calculating the effective porosity of a core plug sample by measuring the bulk and grain densities. The effective porosites calculated for the core plug sample using various techniques did not show huge differences. After using the caliper to obtain five different diameters and three different lengths readings for the core plug sample at various locations, we were able to obtain an average diameter of 3.86cm3 and an avarage length of 5.24cm3.