# Free Statistical analysis Dissertation Example

Statistical Analysis

The table below shows the provision of the data in tabular form. The table has two variables.

Group

Population Group i Group ii Group iii Group iv

Oestrogen Level

1.3416

4.7104

2.0937

39.734

Progesterone Level

31.31

136.51

25.685

16.455

no plug

3 2 4 1

Restatement of Hypotheses in Symbol Form

From the data given above, the chi-square analysis gives the distribution of the variables and the statistical significance of the data. Planned number of animals and no plug does not affect group size is represented in the percentage form hence giving the fractional value for the effect of group size. The confidence level of the data is assumed to be 95% in the calculations. Planned number of animals means good group size is expected to be independent of the no plug results. Alternative hypothesis (Ha) shows that the high group size means the good planned number of animals depends on the size population being tested. Level of significance for the analysis is taken to be 0.05 while the degree of freedom as 4. Null hypothesis (Ha) assumed that planned number of animals means good group size independent of the plug results.

Conditions for Your Significance Test

Probability test involves rejection region which is taken to have an area of 5.643 square units. Value of the level of significance for the data analysis is therefore to be less than the rejection region. There is not enough evidence that the evidence for the 5% significance. The variables group size and planned number of animals are assumed to be either dependent or independent before the hypothesis test. The degree of freedom for the data is taken as 4.Chi-square independence is assumed to give the correct dependency of the two variables (Rice, 224). Test statistic and critical values for the data gives the evidence for the hypothesis to be either null or alternative depending on the results of the chart.

Significance tests are necessary for the analysis of any data set with two variables. Level of significance is directly proportional to the degree of dispersion. Common methods of data analysis such as the median, average and standard deviation are related to the significance ratio. Rejection region gives the overview of the independence of variables. A large rejection region shows that the variables are not correlated (Rice, 223). Comparison of the variable and the rejection region gives the validity of the hypothesis. Chi-square records the frequency of variables and gives the critical value for the given set.

The degree of freedom gives provisional results on how the chosen variables relate. For example, how a given set of statistical data is tailed. The Chi-square produces contingency table to analyze the data easily. Significance level chosen should correspond to the type of the variables and their correlation.

Significance Test Calculations

A degree of freedom is given as (3-1) (3-1) = 4

Level of significance is given as 0.05

The rejection region = X> 5.643

X^2 = 12.692

12.692 > 5.63

The above calculation shows that the null hypothesis is rejected. Therefore, alternative hypothesis is valid. Level of significance is a tool that is used to rank parameters according to their closeness to the median. High ranked parameters are above median while the low ranked parameters fall below the median. Planned number of animals and plug results in each group does not affect the distribution of the results depending on the level of significance concept. Assignment of the parameters to various respondents is done in a manner that ensures the assignment remains optimum.

ANOVA Test

The table below shows the summary of the results for the four groups.

estrogen Progest Cycle

Group1 1.3416 31.31 596908445500

3.7631 71.69

17.774 14.311

6.216 8.7231

3.6214 84.718

4.6725 155.62

3.7082 22.264 no plug

2.9835 70.874

6.2084 6.9319

7.6024 12.136

8.8616 13.635

5.1157 15.13

Group2 4.7104 136.51

3.7971 171.43

11.947 73.702 146050-16637000

36.504 no plug

5.1935 3.8277

7.8263 7.3828

4.47 42.787

3.4837 93.425

8.7799 13.035

4.6549 83.913

9.1517 14.945

4.7129 150.12

Group 3 2.0937 25.685

7.4254 13.893 62865-11049000

10.511 14.097

5.088 136.51

6.5469 10.367

2.7406 13.174

9.0573 14.816

2.0036 121.31

2.7176 12.477

1.0174 8.0899

3.8752 158.66

5.8571 14.469

Group 4 18.551 11.693

no plug

15.121 60.173 no sample

14.799 4.9625 no plug

36.621 25.774 62230-58102500

16.713 82.325

24.719 4.5617

36.093 14.391 no plug, bleeding

31.618 5.8213

4.6296 10.753

16.547 3.72

18.658 57.715

39.734 16.455 no plug

Conclusion

Ways to improve your project: Experiment can be improved by having planned number of animals with a wide range of variables to record the accurate data. Participants should differentiate between estrogen and progesterone and how they affect the accuracy of the results. Again the project can be improved by use of the ARENA software gives the simulation of the planned number of animals and the group size. The simulation shows how the adopted parameters work and give the results regarding the cost incurred.

Calculations to show the order in which the parameters should be assigned to a different planned number of animals and group size. Allocations are done to the cells with the least improvement index.

Works Cited

Rice, William R. “Analyzing tables of statistical tests.” Evolution 43.1 (2015): 223-225.

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