Statistics

The direct relationship has become more insignificant with the addition of the mediator variables. Although the researchers saw an almost significant relationship, the mediators indirect path looks significant and the direct path looks insignificant. If we can show that a mediator variable significantly explains a direct path, and a direct path becomes more significant, it fully explains why more condition (X) leads to (Y). The matrix originally determined the direct relationship being equal to P=0.0766, but with the mediators, the direct relationship has a P-value of 0.3211.

10. Refer to the model and estimates in the previous question. You want to test that

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 problem of the experiment is: which metal out of galvanized iron, copper, stainless steel, and steel will rust the least in acetic acid? The hypothesis is: If Copper is placed in the acetic acid then it will be the material that loses the least mass when mass lost is related to material submerged. The independent variable is what type of metal is being used. The dependent variable is the percent of mass lost while submerged in the vinegar. The controls include the amount of vinegar per sample, the same person pouring the vinegar, the same scale taking measurements, and the same amount of time submerged in the vinegar. The standard of comparison is how much the mild steel corrodes because that is the metal currently used to make the gas tanks at gas stations. The experiment designed to test this starts with cleaning of five different samples of copper, iron, mild steel, and stainless steel. After that, the mass of each sample is measured using an electronic balance. Then, each sample is put in a cup, and submerged in 200 mL of vinegar. They are then left in the vinegar for about 24 hours. Next, after the 24 hour period, each sample is then taken out, cleaned, and measured again. This process is repeated one more time. After the experiment is done, you calculate the percent of each sample lost by day, and find the average of those. Then, the metals are compared by percent of mass lost to find the most corrosion resistant.

COMMENTS argument is that because the average effect size for published research was equivalent to that of a medium effect, the reviewer 's decision to reject the bogus manuscript under the nonsignificant condition was "reasonable." Further examination of the Haase et al. (1982) article and our own analysis of published research, however, demonstrates that the power of the bogus study was great enough to detect effect sizes that are typical of research published in JCP, which was our intention when we designed the bogus study. First, although the median effect size (if) for all univariate statistical tests, significant and nonsignificant, reported by Haase et al. (1982) was .083, this index was steadily increasing at a rate of approximately .5% per year, so that the projected median if- in 1981 (the year our study was completed) would be .13. Importantly, an r)2 of .13 corresponds to an effect size (/) of .39, which Cohen (1977) designates as a large effect. A further examination of the Haase et al. (1982) data also lends support to our argument. Their analysis examined the strength of association for 11,044 univariate statistical tests derived from only 701 manuscripts; thus, each manuscript reported an average of more than 15 statistical tests. Since statistically significant and

Using the regression function in excel, the model is given by C=4.5t+35.7857 with an R2 value of 0.9652.

The Scatter diagram illustrate a strong positive linear correlation and a direct relationship between the two variables.

There is a significant relationship between the two variables. Two observations are identified as having a large standardized residual and should be treated as possible outliers; the following standardized residual plot does not indicate any other problems with the residuals.

In regards to these results, the researchers theorized a couple potential reasons why these relationships exist. First, they reasoned that

Consequently we find the better model after dropping variables with the p-values more than 0.05. Model 5 is the best model which has all the significant variables and has lowest AIC value of 1478.82 and BIC value of 1513.17.

After I analyzed the full model, I reduced the data set to create a more appropriate regression that removed all unnecessary variables and satisfied all of the assumptions of no serious outliers, autocorrelation, heteroscedasticity, non-normality of errors, or multicollinearity. I had no need to include a trend variable or to transform the dependent variable in any way as shown by the histograms and scatter plots located below.

In the following I will assess the methodologies both studies employ and the way they proceed with the differing datasets as this forms another part of the reason why they reach different conclusions.

A more detailed analysis of both sets of data can be found in the following pages of this report.

Answer (e): I think that Models 3 and 4 are in consistent with Model 2. According to me, the difference lies in the way we interpret them. There is only 1 variable in Model 3 as well as in Model 4, hence, the interpreted answer misses out on the control of the other variable. Model 3 shows us the consequence of

When looking at the problem that A-CAT is facing we see either overstocking or under stocking transformers. Over stocking causes capital to be tied up in the inventory. Under stocking causes A-CAT to be unable to deliver order quickly. I chose to use ANOVA and a regression analysis so that I could establish definitively if the transformers required is dependent or independent on the sales of refrigerators and by how much does the sales of refrigerators influence the number of