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# SUMMARISING DATA.UNDERTAKING STATISTICAL TESTS. APPLIED MEDICAL STATISTICS - Essay Example

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SUMMARISING DATA.UNDERTAKING STATISTICAL TESTS. APPLIED MEDICAL STATISTICS TASK A: DATA SUMMARY Statistics Weight1 Weight2 babyweight Weightgain         Mean 59.03118 70.00118 3.274412 10.97 Median 58.2 69.75 3.255 10.95 Mode 60.5 82.7 3.64 12.7 Standard Deviation 9.761816 10.84406 0.621206 4.705402 Kurtosis -0.04144 -0.54081 4.757353 -0.07777 Skewness 0.414652 0.01683 1.345788 0.33703 Confidence Level(95.0%) 1.478003 1.641862 0.094055 0.712429 The above table explains the descriptive statistics for all the given variables that were picked out to examine the sample of 170 pregnant ladies…

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Download file to see previous pages categorized by 0 and 1 where 0 shows little or no pain and 1 shows severe or troublesome pain (Pain), Baby’s Birth weight (kg) (babyweight), Age entered in categories where 1 shows under 25, 2 shows 25-29, 3 shows 30-34, 4 shows 35 and over(agecat), the patient had any previous children or not recorded as 1 and 0 where 1 shows yes and 0 shows no (prevChildren), Depression level that was also scaled where the highest number show worse depression (depression) while the last variable that was not included but was needed to be calculated for section B is weight gained during pregnancy in kgs calculated by subtracting Weight1 from Weight2. All the statistics seem to lie within the normal range i.e., -2 to +2, thus it can be inferred that the given sample has somewhat symmetrical normal distribution. However, in the given case, the descriptive statistics for some variables seem meaningless including pain, idnum, agecat, prevChildren and depression and therefore not incorporated in the table provided. Section B B1. Hypothesis Testing In order to explore the relationship between age and back pain, the following hypothesis was drawn. Ho: There exists no association between back pain and age of patient H1: There exists an association between back pain and age of patient Since both variables involved are ordinal, therefore in order to estimate a significant association between them, Chi-square test has been conducted on SPSS. The Chi squared test requires fulfilment of two assumptions. One of these require that both of the variables employed should be assessed at nominal level while the second requires that the independent variables should also be consisted of two or more independent groups (categorical). Both of these assumptions have been met by our sample data. It appears that the highest percent of patients having severe back pain fall in the 2nd category of age while most of the patients that experience little or no pain were of age category 1. Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 8.657a 3 .034 Likelihood Ratio 8.758 3 .033 Linear-by-Linear Association 7.042 1 .008 N of Valid Cases 170 a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 8.68. The observed value of Chi-squared statistic is 8.657 with 3 degrees of freedom. Since the cross tabulation table involved 2 rows and 4 columns, the Pearson seems to be the suitable chi-squared statistic. From the above table, the p-value is found to be equal to 0.034 which is less than 0.05 significance level. This P- value suggests that we can reject the null hypothesis indicating no association between back pain and agecategory. Therefore, it can be concluded that at 0.05 significance level, there exists a statistically significant relationship between back pain and age category of patient as stated under the alternate hypothesis. B2. Hypothesis Testing In order to explore the association between weight gained throughout pregnancy and the absence or occurrence of back pain, the following hypothesis is drawn. Ho: There ...Download file to see next pagesRead More
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