3 pages/≈825 words
Accounting, Finance, SPSS
Elaborate Concepts On The T-test Using Calculations And Spss (Statistics Project Sample)
Answer the questions to elaborate concepts on the t-test using calculations and spss.source..
A t-test was conducted to test whether there was a significant difference between the baselines and post LDL measurements of 15 participants after a nurse-based intervention. The mean difference between the two measurements was -8.2. The test was significant at t(14)=0.0036. This implies there was a statistically significant difference between the Baseline and Pre LDL values. A non-parametric test, the sign test was also used to determine whether a significant difference between the two measures exists. The probability that the median value was less than 0= (0.0461) was significant at alpha=0.05. Similar results were obtained using the Wilcoxon Signed Rank test where the absolute value of the test statistics was significant at 0.0044<0.005 and the one –sided test significant at 0.0022<0.005. The normal Q-Q plot shows that the data was close to normal since most of the points lie close to the slope line. All the tests give a similar outcome; the nurse-based intervention method improved the LDL cholesterol levels for the patients. However parametric tests are much easier to conduct, do not require a lot of data and can be used on various shapes of data. Non-parametric tests require large sample sizes, for example in the sign test, each of the paired samples should be present. These tests do not depend on the distribution of a sample, and the results tend to be invalid.
An ACOVA analysis was conducted to determine whether LDL pre was a significant predictor of LDL post. A summary of the model fit shows that 51.68% of the variation was accounted for by the model while the rest was due to chance while the F-value=13.9014 was significant at 0.0025<0.005. A look at the parameter estimates revealed that LDL Pre was a significant predictor of LDL post at 0.0025<0.005. Finally, the normal quartile plot shows the data was normal since most of the values lie on and close to the slope line. The regression equation obtained was:
LDL Post=-74.1888+1.4779489*LDL Pre
* Predict the POST LDL and gain using the average Pre LDL
LDL Post= -74.1888+1.4779489*138.067
LDL Post= 129.8672
Gain= -8.1998; -8.2 in exercise 1
* Predictions for pre LDL of 130 and 145
LDL Post= -74.1888+1.4779489*130
Gain= -12.0554; -15 in exercise 1
LDL Post= -74.1888+1.4779489*145
Gain = - 4.8862; 0 in exercise 1
The results from exercise 2 seem more accurate since they are not rounded off to whole numbers. Furthermore, while getting the average between the Pre and Post LDL values, data accuracy may be lost due to truncation errors and rounding off. The ANCOVA method seems more appropriate and accurate.
Estimated proportion with LDL less than 130 pre and post;
The p-value using excel;
The McNemar’s test is used to determine whether there is an association between paired dichotomous variables. In this case, the p-value 0.275229<0.05. This implies the proportions less than 130 pre is not equal to the proportion less than 130 Post. We conclude that there was no association between the case and control variables.
Data Analysis Using SPSS
A 95% confidence interval was used for all the tests
Paired Samples Statistics
Std. Error Mean
Paired Samples Correlations
LDL PRE & LDL POST
Paired Samples Test
Std. Error Mean
95% Confidence Interval of the Difference
LDL PRE - LDL POST
Wilcoxon Signed Ranks Test
Sum of Ranks
LDL POST - LDL PRE
a. LDL POST < LDL PRE
b. LDL POST > LDL PRE
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