ECON 2331 • ECONOMICS AND BUSINESS STATISTICS 2
PRACTICE EXAMINATION
PART A—Multiple-Choice Questions (30 marks total)
Please circle the letter of the correct answer directly on this exam paper. (1 mark each)
1. The critical value of t for a two-tailed test with 6 degrees of freedom using α = .05 is:
a. 2.447.
b. 1.943.
c. 2.365.
d. 1.985.
2. The sum of the values of α and β:
a. is always 1.
b. is always .5.
c. gives the probability of taking the correct decision.
d. is not needed in hypothesis testing.
3. What type of error occurs if you fail to reject H0 when, in fact, it is not true?
a. Type II
b. Type I
c. either Type I or Type II, depending on the level of significance
d. either Type I or Type II, depending on whether the test is one-tailed or two- tailed
4. For a given sample size in hypothesis testing:
a. The smaller the Type I error, the smaller the Type II error will be.
b. The smaller the Type I error, the larger the Type II error will be.
c. Type II error will not be effected by Type I error.
d. The sum of Type I and Type II errors must equal to 1.
5. If the null hypothesis is rejected in hypothesis testing:
a. No conclusions can be drawn from the test.
b. The alternative hypothesis is true.
c. The data must have been accumulated incorrectly.
d. The sample size has been too small.
6. When the following hypotheses are being tested at a level of significance of α
H0: μ 500
Ha: μ < 500
the null hypothesis will be rejected, if the p-value is:
a. ≤ α .
b. > α .
c. = α/2.
d. 1 - α/2.
7. A machine is designed to fill toothpaste tubes, on an average, with 5.8 ounces of toothpaste. The manufacturer does not want any underfilling or overfilling. The correct hypotheses to be tested are:
a. H0: μ ≠ 5.8 Ha: μ = 5.8.
b. H0: μ = 5.8 Ha: μ ≠ 5.8.
c. H0: μ > 5.8 Ha: μ ≤ 5.8.
d. H0: μ ≥ 5.8 Ha: μ < 5.8.
8. The sampling distribution for a goodness of fit test is the:
a. Poisson distribution.
b. t distribution.
c. normal distribution.
d. chi-square distribution.
9. The number of degrees of freedom associated with the chi-square distribution in a test of independence is:
a. number of sample items minus 1.
b. number of populations minus 1.
c. number of rows minus 1 times number of columns minus 1.
d. number of populations minus number of estimated parameters minus 1.
10. When individuals in a sample of 150 were asked whether or not they supported capital punishment, the following information was obtained.
Do you support
capital punishment?
|
Number of individuals
|
Yes
|
40
|
No
|
60
|
No Opinion
|
50
|
We are interested in determining whether or not the opinions of the individuals (as to Yes, No, and No Opinion) are uniformly distributed. The expected frequency for each group is:
a. .333.
b. .50.
c. 1/3.
d. 50.
11. The test statistic for goodness of fit has a chi-square distribution with k - 1
degrees of freedom provided that the expected frequencies for all categories are:
a. 5 or more.
b. 10 or more.
c. k or more.
d. 2k.
12. The ANOVA procedure is a statistical approach for determining whether or not the means of:
a. two samples are equal.
b. two or more samples are equal.
c. two populations are equal.
d. three or more populations are equal.
13. In an analysis of variance problem involving 3 treatments and 10 observations per treatment, SSE = 399.6. The MSE for this situation is:
a. 133.2.
b. 13.32.
c. 14.8.
d. 30.0.
14. When an analysis of variance is performed on samples drawn from k
populations, the mean square due to treatments (MSTR) is:
a. SSTR/nT.
b. SSTR/(nT - 1).
c. SSTR/k.
d. SSTR/(k - 1).
15. In an analysis of variance problem if SST = 120 and SSTR = 80, then SSE is:
a. 200.
b. 40.
c. 80.
d. 120.
16. In an analysis of variance, one estimate of σ2 is based upon the differences between the treatment means and the:
a. Means of each sample.
b. Overall sample mean.
c. Sum of observations.
d. Population means.
17. Part of an ANOVA table is shown below.
Source of Sum of Degrees of Mean
Variation Squares Freedom Square F
Between Treatments 180 3
Within Treatments (Error)
TOTAL 480 18
The mean square due to treatments (MSTR) is:
a. 20.
b. 60.
c. 18.
d. 15.
18. If we are testing for the equality of three population means, we should use the:
a. test statistic t.
b. test statistic z.
c. test statistic F.
d. test statistic χ2.
19. A test used to determine whether or not first-order autocorrelation is present is:
a. serial-autocorrelation test.
b. t test.
c. chi-square test.
d. Durbin-Watson Test.
20. In multiple regression analysis, the general linear model:
a. Cannot be used to accommodate curvilinear relationships between dependent variables and independent variables.
b. Can be used to accommodate curvilinear relationships between the independent variables and dependent variable.
c. Must contain more than two independent variables.
d. Cannot use the standard multiple regression procedures for estimation and prediction.
21. Which of the following tests is used to determine whether an additional variable makes a significant contribution to a multiple regression model?
a. a t test
b. a z test
c. an F test
d. a chi-square test
22. In regression analysis, the error term ε is a random variable with a mean or expected value of:
a. 0
b. 1
c. μ
d. x
23. The coefficient of determination:
a. Cannot be negative.
b. Is the square root of the coefficient of correlation.
c. Is the same as the coefficient of correlation.
d. Can be negative or positive.
24. If the coefficient of determination is a positive value, then the coefficient of correlation:
a. Must also be positive.
b. Must be zero.
c. Can be either positive or negative.
d. Can be larger than 1.
25. In regression analysis, the unbiased estimate of the variance is:
a. Coefficient of correlation.
b. Coefficient of determination.
c. Mean square error.
d. Slope of the regression equation.
26. Which of the following is not present in a time series?
a. seasonality
b. cross-sectional pattern
c. trend
d. cyclical pattern
27. The component that reflects unexplained variability in the time series is called
a. A trend component.
b. Seasonal component.
c. Cyclical component.
d. Irregular component.
28. The following linear trend expression was estimated using a time series with 17 time periods.
Tt = 129.2 + 3.8t
The trend projection for time period 18 is:
a. 68.4.
b. 193.8.
c. 197.6.
d. 6.84.
29. The forecasting method that is appropriate when the time series has no significant trend, cyclical, or seasonal effects is:
a. Moving averages approach.
b. Decomposition model.
c. Simple linear regression.
d. Qualitative forecasting method.
30. Using a naive forecasting method, the forecast for next week’s sales volume equals:
a. The most recent week’s forecast.
b. The most recent week’s sales volume.
c. Tthe average of the last four weeks’ sales volumes.
d. Next week’s production volume.
PART B—Define/Describe/Distinguish (12 marks total)
In your own words, define/describe/distinguish between the following. Write your answers in one of the exam answer booklets. (3 marks each)
1. Multiple Regression Analyses
2. Time Series Analyses
3. Tests for Goodness of Fit
Part C—Short-Answer Questions (38 marks total)
In one of the exam answer booklets, write your answers to all of the following questions (marks as indicated)
1. Assume that the mean wage of employees is $25.00 and the standard deviation is $3.00. A sample of 36 employees finds a sample mean of $26.00. Conduct a test to determine if the mean wage is different from the population. (5 marks)
a. State the null and alternative hypotheses
b. Compute the standard error of the mea
c. Compute the value of the appropriate test statistic
d. Make a decision based on a critical z-value of 1.96
2. The following two tables summarize the amount of time in minutes that it took an employee to drive to and from the office each day for one week. (6 marks)
DAY OF WEEK
|
TO OFFICE
|
FROM OFFICE
|
Monday
|
9.2
|
11.7
|
Tuesday
|
9.3
|
11.8
|
Wednesday
|
9.6
|
11.1
|
Thursday
|
9.2
|
11.5
|
Friday
|
9.6
|
11.3
|
a. Name two possible ways to test if the amount of time it takes the employee to commute to and from work is different in the morning and late afternoon.
b. How do the assumptions of these two methods differ?
c. Which of these methods would be the most appropriate for this data?
3. Telus wants to charge new consumers a premium to connect them if the service person spends more than 15 minutes to connect the new customers. A sample of 36 connections indicate that the mean time is 17 minutes. Based on this sample should Telus charge a premium? The population standard deviation is 4 minutes. (6 marks total)
a. State the null and alternative hypotheses and compute the value of the test statistic. (4 marks)
b. What conclusion would you reach? Explain why? (2 marks)
Stock prices for 10 large corporations for last year and current year.
4. Use the data above to conduct an appropriate test to see if the mean value of stock prices has changed between last year and the current year. Level of significance α = 0.05. (6 marks)
5. Complete the table, and test if there is sufficient evidence, at α = 0.05, that the population means corresponding to the treatments are not all equal. (6 marks)
Source
|
Sum of
Squares
|
df
|
Mean Square
|
F
|
Treatment
|
|
2
|
|
|
Error
|
|
|
20
|
|
Total
|
500
|
11
|
|
|
6. Vancouver’s Mayor claims that 20 percent of young people, aged 20-30, purchase condos rather than rent. A random sample of 200 of young people in this age group found that 56 had purchased condos. Use this sample result to test the hypothesis that more than 20 percent of young people buy rather than rent. Use a significance level of 0.01. (6 marks)
7. Suppose a claim is made that wage rates for plumbers are higher than wage rates for electricians. You take random samples from both populations and compute the sample means. You apply the z-test for the difference of population means when the standard deviations of the two populations are known. You proceed to compute an interval estimate and get an interval of -$2.00 to +$3.50. Can you conclude that that wage rates for plumbers are higher than wage rates for carpenters? (3 marks)
Part D—Short-Answer Question (10 marks total)
Write your answer in one of the answer booklets.
Economists recognize that Investment is a positive function of GDP. Use the following sample data on GDP and Investment to answer the questions below.
Year GDP (X) Investment (Y)
1991 164,692 27,892
1992 178,660 28,840
1993 191,844 31,136
1994 210,196 37,544
1955 231,720 43,512
1996 259,272 50,604
1997 278,792 51,808
1998 304,524 52,788
1999 335,300 59,248
2000 360,716 62,500
2001 393,716 70,108
2002 439,652 78,644
2003 515,824 97,572
2004 616,152 121,180
2005 694,484 141,508
2006 799,976 160,556
2007 883,892 172,532
2008 979,508 189,284
2009 1,118,308 222,968
2010 1,257,560 255,044
2011 1,441,884 310,164
2012 1,519,436 284,300
2013 1,645,544 287,788
2014 1,798,328 300,820
2015 1,942,856 333,500
a. Determine the regression equation using Investment as the dependent
variable. To use a calculator, use the equation given in the footnote on page 605 of your text.
b. Can you determine if the coefficient on the GDP variable is statistically significant? Use the 0.05 significance level and an estimated standard deviation for the slope coefficient of 0.00540.
c. Estimate what the level of investment would be if GDP were $800,000. Also construct a 95 percent confidence interval for this estimate. Assume that the estimated standard deviation of your estimated value for investment is $300.
Part E—Short-Answer Question (10 marks total) Write your answer in one of the answer booklets.
Use the following data to determine the seasonal index
Year
|
Q I
|
Q II
|
Q III
|
Q IV
|
2008
|
5.3
|
4.1
|
6.8
|
6.7
|
2009
|
4.8
|
3.8
|
5.6
|
6.8
|
2010
|
4.3
|
3.8
|
5.7
|
6.0
|
2011
|
5.6
|
4.6
|
6.4
|
5.9
|