AN ANALYSIS PLAN INFERENTIAL STATISTICS

ANANALYSIS PLAN: INFERENTIAL STATISTICS

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Dateof submission:

RegressionAnalysis

Regression Statistics

Multiple R

0.659105426

R Square

0.534419963

Adjusted R Square

0.72555083

Standard Error

3.415419192

Observations

10

Rsquared (R²)

Inthe regression statistic, the R square (R²) tests the goodness offit of the model, in this case, it means that 53.4% of the variationsin GDP growth rate are explained by variations in vaccinationcoverage in the 10 states, both top five and bottom five. 46.6% isexplained by other factors that are not incorporated in the model[ CITATION Rox15 l 1033 ].

AdjustedR squared (R̃²)

Variationsin children healthcare are explained up to 72.55% by the model with27.45% explained by other exogenous factors that are not in themodel.

Standarderror (SE)

Thestandard error (SE) is a measure of the magnitude of errors ofprediction. It measures the variation around the line of best fit.SE= 3.415isthe error of predicting the value of the efficiency of PPACA policiesin the control of MMR in children using regression analysis. Itdenotes the standard error of the regression analysis conducted onthe data obtained.

T-Statistics

Itdenotes how a variable varies or differentiate from the mean. Thelarger the value, the more the variable varies from the data’s meanhence resulting in more error chances.

ANOVAAnalysis

ANOVA

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df

SS

MS

F

Significance F

Regression

5

35.83964697

7.167929393

0.614477082

0.036997375

Residual

4

46.66035303

11.66508826

Total

9

82.5

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ANOVAis used to determine the usefulness of the overall model testing itif its

Letthe null and alternative hypotheses be such that:

Ho:no impact

Ha:impact to increase immunization.

Sincethe p value or significance F of (0.037) is less than thesignificance level (0.05), therefore, we reject the null hypothesisin favour of the alternative hypothesis, that is 0.037 &lt0.05

Decision:Reject Ho meaning that there is sufficient evidence to show that the model isuseful[CITATION Jen08 l 1033 ].

Chi-SquareAnalysis

Chi-squareanalysis cannot be conducted in such data as there has to be a set ofdata of both actual results of immunization and vaccination of theuninsured people, as well as the expected outcome. It requirescomparison of two sets of data. It can only be applicable if thecomparison is between the top five and the bottom five.

Inthe case of comparing between the top and bottom five, assuming thebottom five to the actual data and top five the expected, thechi-square value is 2.50899. If the value equals or is more than thecomputed chi-square, it means there is a relationship between thevariables at hand[ CITATION DRe11 l 1033 ].

Calculatingchi-square is through the determination of degree of freedom. Formulais df = (No of rows – 1) x (No of columns – 1) = (5-1) * (5-1).It will be 4*4 = 16. In this case, calculating was conducted with a pvalue of 0.05.

References

Barton, J. P. (2008). Medical Statistics: A guide to Data Analysis and Critical Appraisal. Massachusetts, USA: John Wiley &amp Sons.

D. Remenyi, D. R. (2011). An Introduction to Statistics Using Microsoft Excel. UK: Academic Conferences Limited.

Roxy Peck, C. O. (2015). Introduction to Statistics and Data Anlaysis. Boston, USA: Cengage Learning.