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Real data analysis

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 an informative decision is made using data analysis. After completing this project, you should be able to determine which statistical procedures are appropriate; and use StatCrunch to carry out the procedures and interpret the output.

Directions:
In an article in the Journal of Statistics Education (vol. , no 2), Allen Shoemaker describes a study that was reported in the Journal of American Medical Association (JAMA). It is generally accepted that the mean temperature of an adult human is 98.6°F. In his article, Shoemaker uses the data from the JAMA article to test his hypothesis. The data for the JAMA article were collected from healthy men and women, ages 18 to 40, at the University of Maryland Center for Vaccine Development, Baltimore. [Elementary Statistics: Picturing the World by Ron Larson and Betsy Farber 7th Ed.]
Data: Click on “StatCrunch” on MyLab Statistics, then “Data sets from the textbook”. Scroll down to Chapter 7, then click on Chapter 7: Case Study: Human Body Temperature: What’s Normal?
Do all of the work below with the “Women” data set Download “Women” data set.

Use a statistical software package to complete the analysis of Exploratory Data Analysis and Inferential Statistics. You are strongly encouraged to use StatCrunch (in MyLab Statistics). You may also use any other software packages (s.a. MINITAB, or SPSS). However, if you use other software, you are on your own, for your instructor will only help with question with regards to StatCrunch.
A) Exploratory Data Analysis

Construct a stem and leaf display.
Construct a histogram.
Construct a boxplot and use it to investigate the data for outliers.
Obtain the sample mean, median, mode, and standard deviation.
B) Inferential Statistics

Construct a normal probability plot or quantile-quantile (QQ) plot to assess normality. Use this assessment to determine which tests are appropriate for these data for confidence interval and test of hypothesis as you will use these procedures for parts 2 and 3 below.
Construct a 95% confidence interval for µ, for mean body temperature for all adult females in the population.
At the 5% significance level, do the data provide sufficient evidence to conclude that the mean body temperature of women is 98.6°F?
Using the software output, perform the confidence interval and the full hypothesis test.
Write a summary and findings of the project
C) Write-up

Write a brief introduction of the project.
Interpret the mean, and standard deviation in context of the data given.
Compare the measures of center and comment on which measure of center would best describe the data given.
Comment on the shape of the distribution.
Are there any outliers in the data? Include results of your investigation for outliers.
Comment on your assessment of normality.
Which test will be most appropriate for the inferential statistics? Z-test or t-test? Why?
Write an interpretation for the confidence interval estimate in context of the data.
State the hypothesis (the null and the alternative)
State the conclusion and summary of the significance test and interpret the p-value using the context of the data.
Compare the results from the confidence interval and the significance test. Are they the same? Why do think this is the case?

Sample Solution

tantly, is to ensure that the senior management are best equipped to lead the organisation successfully. However, further down the hierarchy Fielder’s contingency theory begins to hold much less relevance, it becomes impractical from a organisational perspective due to the number of people at this level of leadership. The logistics of matching the leader with their least preferred co-worker is impossible to consistently achieve, so a more continuum based approach is required. Figure 1: Chelladurai’s Multi-Dimensional Model of Leadership (Miller and Cronin, 2012) There are other contingency theories that provide a more continuum based approach such as Redding’s theory of leadership and management, however Fielder’s description of how situational factors affect the leadership style required for the situation is extremely useful in understanding the fundamentals of leadership (Pettinger, 2007). Chelladurai in his Multi Dimensional Model of Leadership, expands on much of Fiedler’s theory but in a continuum based approach, in which the leader can adapt their leadership style to fit the situation (Chelladurai and Madella, 2006). Chelladurai’s theory is taken from sports psychology but can be applied to an organisational scenario. It provides a much more empirical categorisation of task structure, clearly differentiating a plethora of situations that require certain leadership styles for success. Chealldurai found three characteristics that affect the leadership style required for a situation, called antecedents, they mainly expand upon Fiedler’s situational factors and leader – member relations and ultimately affect how a leader should behave towards a situation. The first are situational characteristics, the environment in which the leader must perform, the second are leader characteristics, the experience, personal qualities and skills of the leader, and the third are member characteristics, the motivation, skill and experience levels of group members (Chelladurai and Madella, 2006). The situational characteristics and member characteristics have a required behaviour to ensure maximum group performance, they also have a preferred behaviour to ensure the satisfaction of group members, if the leaders actual behaviour matches both the required behaviour and preferred behaviour o
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