ASPC & Statistics - Assignment Example

SPC & Statistics We ed the data on a statistical package Minitab, and we analysed the data as follows. A) Descriptive statistics Table 1 Descriptive statistics   Product weight Count 20 Mean 9.835 sample variance 0.163 sample standard deviation 0.404 minimum 8.9 maximum 10.4 Range 1.5 skewness -0.421 kurtosis 0.077 coefficient of variation (CV) 4.11% 1st quartile 9.600 Median 9.850 3rd quartile 10.125 interquartile range 0.525 Mode 9.900 A population sample N = 20 with a population mean μ = 9.8. The SD = 0.

4 and a Range of 1.5. B) Histogram and normal curve The graph indicates a Platykurtic distribution as Kurtosis = 0.07 indicating that most values are around the mean and also indicates an asymmetrical distribution with a negative skew. = -0.42. The graph shows a linear correlation between the product weight and the percentage of occurring at a mean 9.8 and SD = 0.40. Hence, the normality test indicates that the distribution data sample is normal. C) At +/- 1.96 σ = 95% from the graph above of normality test real score = 10.

5 and +/-3σ = At 99% from the graph the real score = 10.8 D). Confidence Intervals 95% CI = ((9.8-1.96*0.09), (9.8+1.96*0.09)) At 95% = [9.6, 10] an indication that the mean lies between the product weight and the sample rate can lie between 9.6 to 10 E) One-Sample T Test of μ = 10 vs ≠ 10 N Mean StDev SE Mean 95% CI T P 20 9.8350 0.0904 0.0202 (9.7927, 9.8773) -8.16 0.000 P-value < .05 Thus, we reject null hypothesis. Type I error occurs by indicating that the two product weights mean are not equal.

Hence, the product weight is almost equal, and the mean lies in between 95% CI.