Complete The Analysis Of Exploratory Data Analysis

  Statistics Project

This project is to help you apply concepts to real world, real data analysis. In the real world computers do most of the hard work for statisticians.  This project will help you understand how the informative decision is made using data analysis.  After completing this project, you will be able to determine which statistical procedures are appropriate; and use the software (Excel, SAS, SPSS, Minitab, and in our course StatCruch) to carry out the procedures and interpret the output. Directions

• Carefully read through the following data

Previous studies have shown that urban bus drivers have an extremely stressful job, and a large proportion of drivers retire prematurely with disabilities due to occupational stress. These stresses come from a combination of physical and social sources such as traffic congestion, incessant time pressure, and unruly passengers. A study examined the effects of an intervention program to improve the conditions of urban bus drivers. Among other variables the researchers monitored diastolic blood pressure of bus drivers in downtown Stockholm, Sweden. The data, in millimeters of mercury (mm Hg), are based on the diastolic blood pressures obtained prior to intervention for a random sample of 25 bus drivers in the study.
81	74	88	65	90	89	93	73	70	80	79	85	75
83	99	94	90	81	93	79	95	95	73	83	63

(Adapted from “Hassles on the Job: A Study of a Job Intervention with Urban Bus Drivers” (Journal of Organizational Behavior, Vol. 20, pp. 199 – 208), G. Evans et al.) 

• Use a statistical software package to complete the analysis of Exploratory Data Analysis and Inferential Statistics. You are encouraged to use StatCrunch (in MyStatLab) since your instructor can help you with this.  You may also use any other software packages. However, if you use other software, you are on your own.

  A) Exploratory Data Analysis  

1. Summarize the data using a stem and leaf display

Stem	Leaf
6	3 5
7	0 3 3 4 5 9 9
8	0 1 1 3 3 5 8 9
9	0 0 3 3 4 5 5 9

 

1. 2.      Summarize the data using a histogram with 5 classes.

 

Bin	Frequency	Cumulative %
60	0	0.00%
70	3	12.00%
80	7	40.00%
90	9	76.00%
100	6	100.00%
More	0	100.00%

 The majority of the items are located in the data range 70-90 as represented by the histogram and only 24% of the data lie between the 90 and 100 mm Hg diastolic blood pressures    

1. Construct a boxplot and use it to investigate the data for outliers.

 

Min	63
First quartile	74.5
Median   value	83
Third quartile	91.5
Maximum value	99
Interquartile range	17

First quartile is (74+75)/2=74.5, the third quartile is (90+93)/2=91.5 Lower Outlier Boundary = Q1 - 1.5 * IQR = 74.5-(1.5*17) =49 Upper Outlier Boundary = Q3 + 1.5 * IQR= 91.5+ (1.5*17) = 114  The data indicates that there are no outliers as the outline boundaries lie between 49 and 119

1. Summarize the data set using the sample mean, median, and standard deviation.

Mean	82.8
Median	83
Standard Deviation	9.8192
Sample Variance	96.4167

   The median of 83 is higher than the mean at 82.8, while the standard deviation is 9.82, and the three are useful when assessing variability from the center B) Inferential Statistics

1. Since you are working with a small sample (fewer than 30 data values), construct a normal probability plot or quantile-quantile (QQ) plot to assess normality. Use this assessment to determine which procedures are appropriate for these data for confidence interval and test of hypothesis as you will use these procedures for parts b and c below.

    The student t-test is appropriate for the hypothesis test since the sample size is less small (<30) at twenty five observations.       <...