BUS 308 Weeks-1

Week 1.	Measurement and Description - chapters 1 and 2


1	Measurement issues. Data, even numerically coded variables, can be one of 4 levels -
	nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, as
	this impact the kind of analysis we can do with the data. For example, descriptive statistics
	such as means can only be done on interval or ratio level data.
	Please list under each label, the variables in our data set that belong in each group.
	Nominal	Ordinal	Interval	Ratio
	Gender	ID
	Degree	Salary
	Gender1	Compa
	Grade	Mid point
		Performance
		Servics
		raise


b.	For each variable that you did not call ratio, why did you make that decision?
	Ratio scales are the ultimate nirvana when it comes to measurement scales because they tell us about the order, they tell us the exact value between units
	No one variable is ratio because no variable values tells about the order among them so they are ratio variables.



2	The first step in analyzing data sets is to find some summary descriptive statistics for key variables.
	For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: overall sample, Females, and Males.
	You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =stdev functions.
	(the range must be found using the difference between the =max and =min functions with Fx) functions.
	Note: Place data to the right, if you use Descriptive statistics, place that to the right as well.
			Salary	Compa	Age	Perf. Rat.	Service
	Overall	Mean	45.0	1.0625	35.7	85.9	9.0
		Standard Deviation	19.20	0.08	8.25	11.41	5.72
		Range	55	0.34	30	45	21
	Female	Mean	38.0	1.0687	32.5	84.2	7.9
		Standard Deviation	18.29	0.07	6.88	13.59	4.91
		Range	55	0.254	26	45	18
	Male	Mean	52.0	1.0562	38.9	87.6	10.0
		Standard Deviation	17.78	0.08	8.39	8.67	6.36
		Range	53	0.305	28	30	21

3	What is the probability for a:							Probability
	a. Randomly selected person being a male in grade E?							0.4
	b. Randomly selected male being in grade E?							0.83
		Note part b is the same as given a male, what is probabilty of being in grade E?
	c. Why are the results different?							The results are different because population and samples are different for both the cases. In the first case male is the population and we are choosing those males who got grade E
								In the second case among the grade E we choose thos emales who are male.
4	For each group (overall, females, and males) find:							Overall	Female	Male
a.	The value that cuts off the top 1/3 salary in each group.							41	24	40
b.	The z score for each value:							-0.208	-1.094	-0.260
c.	The normal curve probability of exceeding this score:							0.583	0.863	0.603
d.	What is the empirical probability of being at or exceeding this salary value?							0.583	0.778	0.750
e.	The value that cuts off the top 1/3 compa in each group.							1.025	1.043	1.075
f.	The z score for each value:							-0.488	-0.366	0.224
g.	The normal curve probability of exceeding this score:							0.687	0.643	0.411
h.	What is the empirical probability of being at or exceeding this compa value?							0.687	0.643	0.411
i.	How do you interpret the relationship between the data sets? What do they mean about our equal pay for equal work question?
	Answer:	we will find the correlation matrix to find the relationship among the variables.
		Equal pay for equal work means the correlation of salaries with the remaining variable in the data set is high, actually thy are dependent to each other.

5.	What conclusions can you make about the issue of male and female pay equality? Are all of the results consistent?
	What is the difference between the sal and compa measures of pay?
	The salary male and females are not equal
	Yes, all of the result is consistent
	The means of salaries and Compa are not equal.
	Conclusions from looking at salary results:
	looking at the salaries the male and femaly payments are not equal

	Conclusions from looking at compa results:
	Looking at the Compa result the payments are not equal

	Do both salary measures show the same results?
	Yes, in both the case we see that the the payments are not equal for the male and female.

	Can we make any conclusions about equal pay for equal work yet?

	No, because in both the case we see that male and females payments according to salary and compa are not equal therefore we canot say that equal pay for equal work

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