Although the data are slightly right skewed, because of the large sample size(n=152) our inference on the mean is valid, by the Central Limit Therom. The Box Cox plot suggests a power transformation using a power, p= -0.5 would make the data more shaped like a normal distribution. The Normal Q-Q plot shows the transformed data points lying close to the straight line and the box plot of the transformed data looks symmetric with two outliers. The numerical summary of the transformed data confirms that the transformed data are symmetric (mean=1 and median=1). A Shapiro-Wilk test on the transformed data provides no evidence against the transformed data having come fro a normal distribution (P-value=0.4286).
The confidence interval for the first group mean is thus (4.1, 13.9). Similarly for the second group, the confidence interval for the mean is (12.1, 21.9). Notice that the two intervals overlap. However, the t-statistic for comparing two means is: t= 17 − 9 2.5 2 + 2.5 2 = 2.26 which reflects that the null hypothesis, that the means of the two groups are the same, should be rejected at the α = 0.05 level. To verify the above conclusion, consider the 95 percent confidence interval for the difference between the two group means: (17 − 9 ) ± 1.96 × 2.5 2 + 2.5 2 which yields (1.09, 14.91).
The following is my evaluation of Table 3.1 Data: The education predictor has very low correlation with citizenship and absence measures. Citizenship is shown to be positive and absence is shown to be negative; however, they are both close to 0. On the contrary, the correlations between education and the performance and promotion potential are slightly higher than citizenship and absence, but they are still undesirably low. Out of these 4 measures, the correlation between education and promotional potential is considered to be the highest because education can definitely predict a future employee’s promotion potential. Promotion potential has a p value of <.01 which makes it statistically significant.
It was found however that the method of utilizing a calibration curve proved fruitless in determining fluorine ion concentration, as the calculated value of fluoride ion’s in drinking water was 13.29ppm, where the required value by law must fall within the range of 0.9-1.5ppm. Since this calculated value is so ridiculous, the method of standard addition, in which the calculated value of the unknown solution being measured was much higher than the calibration curve (4.81E-3M verses 3.78E-4M), should be a preferred method for calculated low concentration ion solutions compared to that of a linear calibration curve. Introduction: Fluoride is a necessary component in minimizing tooth decay by helping rebuild enamel. Because of this chemicals very effective ability to complete this task, the fluoride ion can be found in toothpaste. The associated salt (NaF) is often put into substances that are utilized every day, such as tap water and certain food products, in order to help prevent dental problems of the general population.
However, in a Probit model the marginal effect is the defined by the coefficient multiplied by the G function. This means we are looking for the statistical significance of this combined partial effect not the statistical significance of the coefficient. This statistical significance is illustrated appendix 2.2 and we observe that resplast, weekslast, propresp and mailsyear are statistically significant at the 5% confidence level. However only the variable avggift was not statistically significant at the 5% confidence level
Which t ratio in Table 2 represents the greatest relative or standardized difference between the pretest and 3 months outcomes? Is this t ratio statistically significant? Provide a rationale for your answer. The t ratio of 4.14 represent the greatest relative or standardized difference between the pretest and 3 months outcomes. Yes, it is significant because as indicated by the asterisk, p <0.05 is the least acceptable value for statistical significance.
Results: | | Tension (N) | a (experimental) | a (theoretical) | 0.05 | 0.05 | 0.49 | 0 | 0 | 0.05 | 0.1 | 0.65 | 3.24 | 3.27 | 0.1 | 0.15 | 1.18 | 2.07 | 1.96 | 0.1 | 0.2 | 1.31 | 3.43 | 3.27 | Analysis/Discussion: The results show that the greater the ratio of mass, the acceleration will be also greater. The hypothesis was found to be true. When both masses are the same, the acceleration (both theoretical and experimental) are indeed zero. As the ratio of the masses increased, so did the acceleration. The tension got bigger as the
The reason why knowledge is not a basic good is because hedonism gives it no value toward the evaluation of lives. Here I will provide an example of how this works. Suppose there are two people. Person # 1 has 100 hedons, 0 dolars, and 10 percent knowledge. Person # 2 has 100 hedons, 0 dolars, and 90 percent knowledge.
Equally likely outcomes. C. Only two possible outcomes. D. A fixed number of trails. Ans: B 5) Which of the following is not a requirement of a probability distribution? A.
- Beta alone is not a good predictor of returns. High beta stocks did not outperform low beta stocks. Beta is dead. - High BE/ME is a good predictor of returns. Book to market ratio was the most powerful scaled price variable for predicting stock returns (more than PE and A/ME).