Final Exam Practice Questions
1. A restaurant thinks that the average check amount of takeout orders is less than $20. A sample of 27
checks are noted and the amounts are found to be $19.50 with a sample standard deviation is $4.00.
Use hypothesis testing at the 10% level to see if the restaurant is right.
For 9.27, calculate the 90%, 95% and 99% confidence interval as well.
3.
4. The population mean income for elementary school teachers is $37,600. A sample of 120 reveals a
mean of $36,900 with a sample standard deviation of $2900. Conducting a hypothesis test at the
significance level of 0.05 to see if there is a difference.
5. A coffee shop manager thinks they sell 45 cups of coffee every morning. A sample of 100 mornings
reveals a mean of 43 cups of coffee with δ2 = 4.4. Test the hypothesis at α = .05.
6. The CEO of a large electric utility fears that 80% of his customers are dissatisfied with their service. Of
100 customers surveyed, 73 said they were dissatisfied. Test the hypothesis at α = 0.05.
7.
8.
9. A company that fills one-gallon containers of water has four machines. Each of these machines is from
a different manufacturer. The quality control manager needs to determine whether the average fill for
these machines is the same. For a total sample of 19 one-gallon containers, an ANOVA test was run.
The total sum of squares was 0.010579 and variance between groups was 0.002359. What conclusion
can you make from this data with 95% confidence?
10.
Is there equal preference for all brands? Conduct the test at the 0.10 significance level.
11.
12.
13. This regression output shows the relationship between house price (in 1000s) and house characteristics
(number of bathrooms, size of house, number of bedrooms and age of the house).
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
0.96
0.91
0.90
4.49
28.00
ANOVA
df
4
23
27
SS
4947.09
464.10
5411.19
Coefficient
s
4.22
16.31
Standard
Error
4.63
4.66
t Stat
0.91
3.50
12.71
-0.54
-0.11
4.05
2.19
0.07
3.13
-0.25
-1.63
Regression
Residual
Total
Intercept
Number of Bathrooms
Size of Living Space (in 1000s
sq ft)
Number of Bedrooms
Age of house in years
MS
1236.77
20.18
F
61.29
Significanc
eF
0.00
P-value
Lower 95%
0.37
-5.35
0.00
6.67
0.00
0.81
0.12
4.32
-5.07
-0.26
Upper 95%
13.79
25.94
21.09
4.00
0.03
1. Which independent variable(s) is most significant in this regression relationship?
2. Which independent variable is least significant in this regression relationship?
3. Explain the relationship quantitatively between each of the independent variables and dependent
variable
4. Explain the goodness of fit
