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# Hypotheses, Prediction, and Regression Practice Test

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Review the following concepts if necessary:

## Hypotheses, Prediction, and Regression Practice Test

A good score is 8 correct.

1. Suppose that in the Happyton/Blissville situation described above, we are told at the last moment that we're not moving to Blissville after all, but instead are going to be relocated to the town of Borington. When we look at the weather data for Borington, we can hardly believe our eyes. The average temperature is 20°C for every month of the year, and the average rainfall is 4.1 cm for every month of the year. When we plot this data on a paired bar graph with coordinate axes like those in Fig. 6-8A or B, we get
1. a set of 24 alternating light and dark gray bars that increase steadily in height from left to right
2. a set of 24 alternating light and dark gray bars that decrease steadily in height from left to right
3. a set of 12 light gray bars that are all the same height, interwoven with a set of 12 dark gray bars that are all the same height
4. a set of 24 alternating light and dark gray bars, all 24 of which are the same height
2. Suppose a computer program is used in an attempt to find a least-squares line for a scatter plot. The computer says that no such line exists. Upon visual examination, we can see that the points are spread out over the graph. Evidently the correlation between the two variables
1. is between 0 and +1
2. is equal to 0
3. is between –1 and 0
4. is equal to –1
3. In a statistical experiment, error can result from any of the following, except
1. instrument imperfections
2. the use of instruments with greater precision than necessary
3. large amounts of missing data
4. limitations of the experimenter's ability to read instrument displays
4. The mathematical process of finding the least-squares line in a scatter plot is a sophisticated example of
1. curve fitting
2. an alternative hypothesis
3. a null hypothesis
4. strong correlation
5. Suppose we are about to relocate to a small town called Hoodooburg in a sparsely populated part of the western USA. We are curious as to what proportion of the people eat beef steak at least once a week. You make the assumption that the figure is 90%. I disagree, and contend that the proportion is lower than that. Your assumption, which I will try to disprove, is an example of
1. a null hypothesis
2. a type-1 error
3. an alternative hypothesis
4. a type-2 error
6. In the scenario of Question 5, my assumption (less than 90% of the people in Hoodooburg eat beef steak at least once a week) is
1. a null hypothesis
2. a type-1 error
3. an alternative hypothesis
4. a type-2 error
7. Suppose that we conduct a well-designed, unbiased survey of the population in the scenario of Question 5, and it turns out that you were right and I was wrong. This means I have made
1. a null hypothesis
2. a type-1 error
3. an alternative hypothesis
4. a type-2 error
8. Suppose we want to see how blood pressure correlates with age, and we have the medical records of 600 people (which we obtained with their permission). The clearest way to graphically illustrate this correlation, if there is any, is to put the data in the form of
1. a table
2. a null hypothesis
3. an alternative hypothesis
4. a scatter plot
9. Examine the scatter plot of Fig. 6-12. Imagine that this graph shows the relative energy available from sunlight at a number of hypothetical locations around the world, as determined by actual observations conducted over a period of several years. Then look at the three examples shown in Fig. 6-13. Which, if any, of the graphs in Fig. 6-13 best illustrates the least-squares line for the scatter plot of Fig. 6-12?
1. Figure 6-13A.
2. Figure 6-13B.
3. Figure 6-13C.
4. None of them.
10. Fig. 6-12. Illustration for Practice Test Question 9.

Fig. 6-13. Illustrations for possible answers to Practice Test Question 9. At A, choice (a); at B, choice (b); at C, choice (c).

11. The use of significance testing is an example of
1. an alternative hypothesis
2. a null hypothesis
3. confidence intervals
4. statistical inference

1. c
2. b
3. b
4. a
5. a
6. c
7. b
8. d
9. b
10. d

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