BUS 308 Week 4 Assignment

BUS 308 wk 4 assignment

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Week 4 Confidence
Intervals and Chi Square  (Chs 11 – 12)
  Let’s look at some other factors that might
influence pay.
use right click on row numbers to insert rows to perform analysis below any
question 3 below, be sure to list the null and alternate hypothesis
statements.  Use .05 for your
significance level in making your decisions.
full credit, you need to also show the statistical outcomes – either the
Excel test result or the calculations you performed.
1 One question we might
have is if the distribution of 
graduate and undergraduate degrees independent of the grade the
(Note: this is the same
as asking if the degrees are distributed the same way.)
Based on the analysis of
our sample data (shown below), what is your answer?
Ho: The populaton
correlation between grade and degree is 0.
Ha: The population
correlation between grade and degree is > 0
Perform analysis:  
COUNT – M or 0 7 5 3 2 5 3 25  
COUNT – F or 1 8 2 2 3 7 3 25  
total 15 7 5 5 12 6 50  
7.5 3.5 2.5 2.5 6 3 25 <Highlighting each
cell with show how the value
7.5 3.5 2.5 2.5 6 3 25 is found: row total times
column total divided by
15 7 5 5 12 6 50 grand total.>
By using either the
Excel Chi Square functions or calculating the results directly as the text
shows, do we
reject or not reject the
null hypothesis?  What does your
conclusion mean?
2 Using our sample data,
we can construct a 95% confidence interval for the population’s mean salary
for each gender.
Interpret the
results.  How do they compare with the
findings in the week 2 one sample t-test outcomes (Question 1)?
Males Mean St error Low to High  
52 3.65878 44.4483 59.5517 Results are mean
+/-2.064*standard error
Females 38 3.62275 30.5226 45.4774 2.064 is t value for 95%
<Reminder: standard
error is the sample standard deviation divided by the square root of the
sample size.>
3 Based on our sample
data, can we conclude that males and females are distributed across grades in
a similar pattern within the population?
4 Using our sample data,
construct a 95% confidence interval for the population’s mean service
difference for each gender.
Do they intersect or
overlap?  How do these results compare
to the findings in week 2, question 2?
5 How do you interpret
these results in light of our question about equal pay for equal work?