BUS 308 Week 4 Assignment Ashford latest Version

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Week 4Confidence Intervals and Chi Square  (Chs 11 - 12)          
For questions 3 and 4 below, be sure to list the null and alternate hypothesis statements.  Use .05 for your significance level in making your decisions.   
For full credit, you need to also show the statistical outcomes - either the Excel test result or the calculations you performed.     
                
1Using our sample data, 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)?    
  MeanSt error t value Low to High       
 Males              
 Females              
 <Reminder: standard error is the sample standard deviation divided by the square root of the sample size.>     
Interpretation:               
                
                
2Using our sample data, construct a 95% confidence interval for the mean salary difference between the genders in the population.      
  How does this compare to the findings in week 2, question 2?         
                
 DifferenceSt Err.T value  Low to High       
                
                
                
    Yes/No           
 Can the means be equal? Why?          
                
 How does this compare to the week 2, question 2 result (2 sampe t-test)?       
                
a.Why is using a two sample tool (t-test, confidence interval) a better choice than using 2 one-sample techniques when comparing two samples? 
                
                
3We found last week that the degrees compa values within the population.        
  do not impact compa rates.  This does not mean that degrees are distributed evenly across the grades and genders.    
 Do males and females have athe same distribution of degrees by grade?        
 (Note: while technically the sample size might not be large enough to perform this test, ignore this limitation for this exercise.)   
                
 What are the hypothesis statements:           
 Ho:               
 Ha:              
Note:  You can either use the Excel Chi-related functions or do the calculations manually.        
 Data input tables - graduate degrees by gender and grade level         
OBSERVEDBCDEFTotal Do manual calculations per cell here (if desired)  
M Grad        BCDEF 
Fem Grad       M Grad       
Male Und       Fem Grad       
Female Und       Male Und       
        Female Und       
                
         Sum =      
EXPECTED               
M Grad       For this exercise - ignore the requirement for a correction  
Fem Grad       for expected values less than 5.     
Male Und               
Female Und               
                
                
                
                
                
Interpretation:               
    What is the value of the chi square statistic:            
    What is the p-value associated with this value:            
    Is the p-value <0.05?           
    Do you reject or not reject the null hypothesis:            
    If you rejected the null, what is the Cramer's V correlation:           
    What does this correlation mean?           
    What does this decision mean for our equal pay question:            
                
                
4Based on our sample data, can we conclude that males and females are distributed across grades in a similar pattern    
 within the population?            
                
 What are the hypothesis statements:           
 Ho:               
 Ha:              
                
          Do manual calculations per cell here (if desired) 
  BCDEF  BCDEF
 OBS COUNT - m       M      
 OBS COUNT - f       F      
                
         Sum =       
 EXPECTED              
                
                
                
    What is the value of the chi square statistic:            
    What is the p-value associated with this value:            
    Is the p-value <0.05?           
    Do you reject or not reject the null hypothesis:            
    If you rejected the null, what is the Phi correlation:           
    What does this correlation mean?           
                
    What does this decision mean for our equal pay question:            
                
5.      How do you interpret these results in light of our question about equal pay for equal work?        
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