Create a 2 pages page paper that discusses statistics minitab. Statistics Minitab Question Ensuring Good and Strong Relationship between Two Data Sets To determine whether the relationship between abs

Create a 2 pages page paper that discusses statistics minitab. Statistics Minitab Question Ensuring Good and Strong Relationship between Two Data Sets To determine whether the relationship between absorbance and concentration is good and strong, first ensure that the sample selected is adequate to make meaningful observations. Then one needs to check for any unusual data which can affect regression. The normality assumption must also be met. Then the model fit can be checked using the F-test. It is also important that the right model is selected in the beginning.

Question 2: Absorbance Data

a) This is continuous data as it can pick any value (with decimals too).

b) Calculating mean, SE mean and Std deviation.

Variable Mean SE Mean StDev

Sample 1 0.2141 0.0222 0.0969

Sample 2 1.5747 0.0893 0.3893

Sample 3 1.5927 0.0846 0.3686

Sample 4 1.693 0.115 0.501

Sample 5 0.6458 0.0862 0.3757

Sample 6 1.6758 0.0876 0.3819

c) The variability of this data would be looked into by first calculating the standard deviation for each level of absorbance with Minitab’s preprocess response command. Then analyse the variability in the designed experiment. Then the results would be interpreted.

d) Paired T-Test

Paired T for Sample 2 - Sample 3

N Mean StDev SE Mean

Sample 2 19 1.5747 0.3893 0.0893

Sample 3 19 1.5927 0.3686 0.0846

Difference 19 -0.0181 0.2493 0.0572

95% CI for mean difference: (-0.1382, 0.1021)

T-Test of mean difference = 0 (vs not = 0): T-Value = -0.32 P-Value = 0.756

The paired-t test has been selected because we needed to test whether the two samples had difference means and if these differences were significant. We therefore needed to pair these two samples and test the differences at 95% confidence level. As can be seen, p &gt. .05 therefore the differences are not significant.

Caffeine Consumption and Marital Status

0 1-150 151-300 &gt.300 Total

Married 652 1537 598 242 3029

705.83 1488.01 578.07 257.09

4.106 1.613 0.687 0.886

Divorced 36 46 38 21 141

32.86 69.27 26.91 11.97

0.301 7.815 4.571 6.817

Single 218 327 106 67 718

167.31 352.72 137.03 60.94

15.356 1.876 7.025 0.602

Total 906 1910 742 330 3888

Chi-Sq = 51.656, DF = 6, P-Value = 0.000

The chi-square analysis was run for the categorical values as were in the table. The chi-square tested the hypothesis that there is no association between caffeine consumption (column factors) and marital status (raw factors). The results show that the chi-square statistic was 51.656. The p value is &lt. .001 which suggests that we reject the null hypothesis of no association between marital status and caffeine consumption in favour of the alternative that there is indeed an association between them. Thus, marital status affects caffeine consumption. The reason for this association could be attributed to the fact that we had more married participants in the sample and hence the results may be biased towards the same. As can be seen, there were 3029 married participants out of the 3888 participants in total. I recommend that to improve results, same number of samples should be selected from the marital status categories for the study.

References

University of Reading, 2013, Minitab Tip Sheet 11, Department of Mathematics and Statistics, 20 March 2013, http://www.reading.ac.uk/web/FILES/maths/MINITAB_Tip_sheet_11.pdf

Minitab, 2013, Chi-Square Analysis: Powerful, Versatile, Statistically Objective, 20 March 2013, http://www.minitab.com/en-US/training/tutorials/accessing-the-power.