Statistics Problems : Please answer all questions with explanations. You collect the following salary


Statistics Problems :

Please answer all questions with explanations.

You collect the following salary figures for Master’s Level Students and their salaries after 2 years at work. You are interested in whether or not people with different degrees make more or less money. The data are analyzed using an ANOVA test with the following results:

Groups Count Sum Average Variance
Accounting 17 1389.8 81.75294 95.22515
Law 17 1349.2 79.36471 145.7899
Finance 17 1356.8 79.81176 139.1611
Management 17 1295.7 76.21765 184.034
ANOVA
Source of Variation SS df MS F P-value F crit
Between Groups 268.3146 3 89.43819 0.634077 0.595756 2.748195
Within Groups 9027.364 64 141.0526
Total 9295.678 67

1 .) What are the null and experimental hypotheses?

2 .) Is there a significant difference between the four majors with regard to their salaries 2 years after graduation?

3 .) Consider a situation where the ANOVA showed that there WAS NOT a significant difference between the four majors. What would you recommend doing as a next step in the research to show that people with Accounting degrees make significantly more than graduates with Management-degrees?

4 .) Consider a situation where the ANOVA showed that there WAS a significant difference between the four majors. A Tukey Critical Difference was calculated to be 4.8 for these data at 95% confidence. What does this statistic tell you about the data?

5 .) Why would it be better to use a one-tailed test in this case?

6 .) Y ou selected a one-tailed test, what are the null and experimental hypotheses (H 0 and H 1 )?

7 .) In the beer price study, what is the critical value of t used in a one-tailed test?

8 .) Is there a significant difference between the price of domestic versus imported beers? What does this mean?

D escriptive statistics for both prices of beers were calculated. The results showed the following:

imports imports domestic
Mean 5.862666667 4.728679
Median 5.68 4.1
Mode 5.99 4.02
Standard Deviation 0.902990798 1.4851
Range 3.17 5.43
Minimum 4.63 2.36
Maximum 7.8 7.79
Count 15 53
Confidence Level(95.0%) 0.500060081 0.409344

9 .) What is the one BEST measure of central tendency (mean, median, or mode) to use for these data?

You are interested in the prediction of apartment rent from square footage. You sample 25 apartments in the Glendale area and record the apartment rent costs and square footage. When you put this into Excel, you get the following output:

Regression Analysis
Regression Statistics
Multiple R 0.850060796
R Square 0.722603356
Adjusted R Square 0.710542633
Standard Error 194.5953946
Observations 25
ANOVA
df SS MS F Significance F
Regression 1 2268776.545 2268776.545 59.91376452 7.51833E-08
Residual 23 870949.4547 37867.3676
Total 24 3139726
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 177.1208202 161.0042766 1.100100096 0.282669853 -155.9414484 510.1830889
Size 1.065143906 0.137608412 7.740398215 7.51833E-08 0.780479605 1.349808208

10.) What is the regression equation for these data?

11 . ) How much of the variance in rent can be predicted by knowing the square footage?

12 .) Is this a significant regression equation?

13 . ) What does this mean?

14 ) . W hat percent of workers make less than $10.50 per hour (z = -1.00)?

15 ) . What percent of workers make more than $15 per hour ( z = 2.21)?

Price: $15.85
Solution: The downloadable solution consists of 7 pages, 885 words.
Deliverable: Word Document


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