Unformatted text preview: STAT 350 – Fall 2008
Your Name: ____________________________________ Your Seat: __________
Section Time (circle): 10:30 12:30 1:30 Note:
• You are responsible for upholding the Honor Code of Purdue University. This includes protecting
your work from other students.
• Show your work on all questions. Unsupported work will not receive full credit. Credit will not be
given for dumb luck. You do not need to show work for multiple choice questions.
• Decimal answers should be exact or to at least four significant digits.
• Unless otherwise stated, assume the significance level for any hypothesis test is 0.05.
• Standard Normal (Z) and values/probabilities must be taken from the tables provided.
Probabilities, p-values, critical values, etc., for χ2, t, and F distributions must also be taken from
the tables provided, unless this information is available on SAS output provided, in which case the
values from SAS should be used.
• You are allowed the following aids: a one-page (8.5×11 inch) cheat sheet, a scientific calculator,
• Turn off and put away your cell phone before the exam begins! Question Points Possible Points Missed
2 10 3 23 4 16 Total 100 Final Score: _______________ / 100 1. Jason is a sociology major. For his senior thesis, Jason randomly selected a number of residents from
his hometown to survey. He asked each subject a range of demographic questions. Among the
questions he asked were: "How many years of schooling have you had?" and "What is your annual
income?" Limiting his sample to just those 30 subjects who were no longer in school (that is, who
had completed their schooling), the number of years of schooling ranged from 9 to 22 years (mean
15.4 years) and the annual incomes ranged from $28,984 to $61,267 (mean $44790). Using these 30
subjects, he conducted a regression analysis to explore whether the amount of schooling affects
income. The SAS output from this analysis is given below.
The REG Procedure
Dependent Variable: income
Number of Observations Read
Number of Observations Used 30
30 Analysis of Variance Source DF Sum of
Corrected Total 1
42110505 Root MSE
Coeff Var 6489.26074
Adj R-Sq F Value Pr > F 7.05 0.0129 0.2012
0.1727 Parameter Estimates Variable
years_school DF Parameter
Error t Value Pr > |t| 1
0.0129 a. (2 pts) What percent of the variation in incomes is explained by the linear relationship between
income and schooling? b. (2 pts) What is the correlation between income and years of schooling? c. (5 pts) Based on the above analysis, what is the income you would expect for an individual from
this town who has had 17 years of schooling? STAT 350 Final Exam Page 2 of 9 1 (continued)
d. (2 pts) The 5th subject in this analysis had 17 years of schooling and has an annual income of
$41,019. What is the value of the 5th residual? e. How much extra money should an individual in this town expect to earn for every additional year
of school he or she has completed?
(i) (2 pts) Give a point estimate. (ii) (5 pts) Give a 95% confidence interval f. (8 pts) Jason wants to determine if the relationship between years of schooling and annual income
is "statistically significant"?
(i) Give the value of the appropriate test statistic (ii) Give the degrees of freedom for that test statistic (iii) Give the p-value. (iv) Based on this, is the relationship "statistically significant"? Just answer "yes" or "no". g. (6 pts) Now assume that Jason wants to test the null hypothesis that years of schooling does not
affect annual income (that is, average annual income does not change with an increase in the
number of years of schooling) versus the alternative hypothesis that average annual income
increases as the number of years of schooling increases.
(i) Give the value of the appropriate test statistic
(ii) Give the degrees of freedom for that test statistic (iii) Give the p-value. STAT 350 Final Exam Page 3 of 9 1 (continued)
h. According to the Bureau of Labor Statistics, nationwide, average income increased $1750 for each
additional year of schooling. Jason wants to compare his town to the national average. He will
test the null hypothesis that the trend in his town is the same as the national average against the
alternative that the trend in his town is different than the national average.
(i) (4 pts) Give the value of the appropriate test statistic (ii) (2 pts) Give the degrees of freedom for that test statistic (iii) (2 pts) Give the p-value. i. (6 pts) Give a 95% confidence interval for the true mean income of all residents of this town (who
have completed their schooling). j. (5 pts) Based solely on the preceding analysis, would it be appropriate for Jason to conclude that
additional schooling causes increased income? Justify your answer. STAT 350 Final Exam Page 4 of 9 2. Short Answer
a. (5 pts) Think back to the experiment done on mice we used in Lectures 18 and 19. Mice were
randomly assigned to receive varying doses of alcohol and then timed running a maze. The
correlation between the dose of alcohol and the time to run the maze was 0.91626. Would it be
appropriate to conclude that increased doses of alcohol causes an increase in the time to run the
maze? Justify your answer. b. (5 pts) Define "p-value". You shouldn't need more space than this! STAT 350 Final Exam Page 5 of 9 Histogram of var1
StD ev 2.038
Frequency 3. Background: In Lab #3 we looked at a number
of different ways to assess whether a sample
may have come from a Normal distribution.
Probability or QQ-plots are one approach. We
also looked at a hypothesis test (the AndersonDarling Test) provided by Minitab along with
the probability plot. Another approach we used
was to visually compare the sample histogram
with a normal distribution curve with the μ =
x and σ = s (for example, see Figure 1).
Another approach is to quantitatively compare
the sample histogram to the normal distribution
curve. A chi-squared test can then be used to
determine whether the observed frequencies in
the various bins are close enough to the
frequencies that would be expected if the
sample did come from a normal distribution. 5
0 4 6 8
var1 10 12 Figure 1. Histogram with Normal Distribution curve from
Lab #3, Problem #1. Note, this is not the data for the
problem below) A sample of 35 observations was taken. You wish to assess whether these observations may have
come from a Normal distribution. The sample had x = 100 and s = 10. Of the 35 observations, 7
were less than 90, 10 were between 90 and 100, 10 were between 100 and 110, and the remaining 8
observations were greater than 110 * (see table below).
<90 90 to 100 100 to 110 >110
observed frequency 7
a. (8 pts) The null hypothesis is that the sample did come from a normal distribution with μ = 100
and σ = 10. This null hypothesis can be restated in terms of the true proportions of each category,
the πi's. Give the values of the πi's (to 4 decimal places)
Hint: π1 = P(X < 90), where X ~ Normal(μ = 100 and σ = 10).
Another hint: π1 + π2 + π3 + π4 = 1. π1 = _________________
π2 = _________________
π3 = _________________
π4 = _________________ * Note: Ideally more bins would be used, but then the analysis would take a lot longer (this is me being nice). Also, the data
were continuous, so there is no problem with observations falling on the cut-point for a bin (e.g., no observation was exactly
100). STAT 350 Final Exam Page 6 of 9 3 (continued)
b. (4 pts) Give the expected counts (to 4 decimal places) for each bin (fill in the table below).
90 to 100
100 to 110
Counts c. (4 pts) Give the value of the chi-square statistic for this analysis. d. (2 pts) What is the critical value for this test (α = 0.05)? e. (5 pts) Based on your analysis above, what is your conclusion? Circle one of the options below.
The sample did come from a normal distribution
The sample did NOT come from a normal distribution STAT 350 Final Exam Page 7 of 9 4. Susan is majoring in wood science. For her senior thesis, she wanted to examine the durability of
wood used for decking. Deterioration of wood in use is commonly caused by decay fungi, certain
insects (including termites) as well as other organisms, and weathering. She wanted to examine
various species of wood and various preservatives. She used 4 species of wood: Red Oak, Spruce,
Eastern Red Cedar, and Redwood. She used 3 types of preservatives: creosote, pentachlorophenol
(PCP), and ammoniacal copper arsenate (ACA). She used 2 boards for each species-preservative
combination. The boards were dried and weighed then placed outdoors (all in the same experimental
plot) for 10 months. At the end of 10 months, the boards were collected, dried, and re-weighed. For
each board, the dry weight-loss (in grams) was recorded. The SAS results from the ANOVA are
given below. Some information has been omitted and replaced with asterisks (*).
The SAS System 1 The ANOVA Procedure
Class Level Information
Class Levels Values species 4 RedCedar RedOak Redwood Spruce preserv 3 ACA Creosote PCP Number of Observations Read
Number of Observations Used 24
24 The SAS System 2 The ANOVA Procedure
Dependent Variable: loss Source DF Sum of
Squares Mean Square F Value Pr > F Model ** 8948.33833 ********* ***** ****** Error ** 3483.66000 ********* Corrected Total ** 12431.99833 R-Square Coeff Var Root MSE loss Mean 0.719783 37.85597 17.03834 45.00833 Source DF Anova SS Mean Square F Value Pr > F species
****** STAT 350 Final Exam Page 8 of 9 4 (continued)
a. Is there a statistically significant difference among the 3 preservatives?
(i) (4 pts) Give the value of the appropriate test statistic. (ii) (2 pts) Give the degrees of freedom for this test statistic. (iii) (2 pts) Give the critical value for this test (α = 0.05). (iv) (4 pts) Based on your analysis above, what is your conclusion? Circle one of the options
There is NO statistically significant difference in the mean weight loss among the 3 types
of preservatives studied
The mean weight loss for each preservative is significantly different from each of the other
At least one of the preservatives has a mean weight loss that is significantly different from
the other preservatives.
b. (4 pts) Assume that the p-value for "species" had been large (e.g., 0.80), but the p-value for
"species*preserv" had been small (e.g., 0.001) ** . Would it then be reasonable to expect the same
performance (loss) for all species of wood for a given preservative? Imagine you are a statistical
consultant trying to explain the implication of these results to Susan. ** The p-values given for part (b) are not actually the p-values from the analysis above. But you are to assume they are for the
sake of answering part (b) STAT 350 Final Exam Page 9 of 9 ...
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This note was uploaded on 02/16/2010 for the course MA 350 taught by Professor Sellke during the Spring '10 term at Purdue.
- Spring '10