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BYU Statistics Exam 2
BYU Independent Stats 121 - Exam 2
80
Mathematics
Undergraduate 4
03/05/2011
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Term
1. A measure of the strength of the linear relationship between two variables.
 Definition
1. What is correlation coefficient.
Term
2. The type of data required for regression analysis.
 Definition
2. What are bivariate quantitative data.
Term
3. A variable that gives the value (may not be a number) of the outcome of a study on each individual
 Definition
3. What is the response variable.
Term
4. The two requirements for computing correlation coefficient.
 Definition
4. What are the two variables must be quantitative and their relationship must be linear.
Term
5. A two dimensional plot used to examine strength of relationship between two variables as well as direction
and type of relationship.
 Definition
5. What is a scatterplot.
Term
6. An observation that substantially alters the correlation coefficient value.
 Definition
6. What is an outlier.
Term
7. Type of association where high values of one variable tend to associate with high values of another variable.
 Definition
7. What is a positive association.
Term
8. The maximum and minimum possible values of correlation coefficient.
 Definition
8. What are plus one and minus on.
Term
9. The unit of measure for the correlation coefficient.
 Definition
9. What is “no” unit of measure”.
Term
10. The value of the correlation coefficient when there is no linear association between two quantitative
variables.
 Definition
10. What is zero? Note a non-linear relationship and no relationship can have an r of zero.
Term
1. A graph for displaying bivariate quantitative data.
 Definition
1. What is scatterplot.
Term
2. The symbol for sample correlation coefficient.
 Definition
2. What is lower case r.
Term
3. A measure of the strength of the linear relationship between X and Y.
 Definition
3. What is correlation coefficient.
Term
4. The line with the smallest sum of squared residuals.
 Definition
4. What is least squares regression line.
Term
5. A plot of the residuals versus the observed x values.
 Definition
5. What is residual plot.
Term
6. Requirements for computing correlation coefficient.
 Definition
6. What is bivariate quantitative data with a linear relationship.
Term
7. The name of the value computed from observed y minus predicted y.
 Definition
7. What is residual.
Term
8. A diagnostic tool used to determine if a regression model is a good fit.
 Definition
8. What is residual plot.
Term
9. The pattern in a residual plot indicating lack of linearity.
 Definition
9. What is a smile or a frown.
Term
10. The pattern in a residual plot indicating that the variability of the y’s is not constant across all x values.
 Definition
10. What is a megaphone.
Term
11. A measure of the percentage of variation in Y explained by X.
 Definition
11. What is r2 (squared).
Term
12. A measure of the average change in Y for every one unit increase in X.
 Definition
12. What is slope.
Term
13. A measure of how far a data point is vertically from the regression line.
 Definition
13. What is residual.
Term
14. The unit of measure for correlation coefficient.
 Definition
14. What is none.
Term
15. The commonality between slope and correlation coefficient.
 Definition
15. What is both have the same sign.
Term
1. A distribution computed from only one row or one column of a two-way table.
 Definition
1. What is conditional distribution.
Term
2. A distribution computed from the row totals or the column totals.
 Definition
2. What is marginal distribution.
Term
3. How the conditional distributions compare when an association exists between the explanatory and response
variables.
 Definition
3. What is “They are different.”
Term
4. A reversal in the association between two variables depending on whether a third variable is considered or
ignored.
 Definition
4. What is Simpson’s paradox.
Term
5. Percentages found by dividing the counts in a row by the row total (or counts in a column by the column
total).
 Definition
5. What is a conditional distribution.
Term
1. The name of the statement telling us that the sampling distribution of x-bar is approximately normal whenever
the sample is large and random.
 Definition
1. What is Central Limit Theorem.
Term
2. A list of the possible values of a statistic together with the frequency (or probability) of each value.
 Definition
2. What is sampling distribution.
Term
3. The shape of the sampling distribution of x-bar when the sample is random from a non-Normal population and
the sample size is large.
 Definition
3. What is approximately Normal.
Term
4. The symbol for the standard deviation of the theoretical sampling distribution of x-bar.
 Definition
4. What is sigma over the square root of n .
Term
5. The value of the mean of the theoretical sampling distribution of x-bar.
 Definition
5. What is mu.
Term
6. A measure of the variability of the values of the statistic x-bar about µ.
 Definition
6. What is Standard deviation of the sampling distribution. of x-bar.
Term
7. Shape of the sampling distribution of x-bar when the sample is small and randomly selected from a Normal
population.
 Definition
7. What is Normal.
Term
8. A measure of the variability of the sampling distribution of x-bar.
 Definition
8. What is Standard deviation of the sampling distribution. of x-bar.
Term
9. A measure of the center of the sampling distribution of x-bar.
 Definition
9. What is mean of the sampling distribution of x-bar, namely, mu.
Term
10. The name of the fact that the average of the data in a sample will get closer and closer to the population
mean as we increase the sample size.
 Definition
10. What is Law of Large Numbers.
Term
1. A characteristic of a population that is usually unknown.
 Definition
1. What is parameter.
Term
2. A subset of the population.
 Definition
2. What is sample.
Term
3. x (x-bar), s, pˆ(p-hat), r .
 Definition
3. What are statistic symbols.
Term
4. (sigma) σ, μ (mu), and p.
 Definition
4. What are parameter symbols.
Term
5. Using results from a sample to draw conclusions about the entire population.
 Definition
5. What is inference.
Term
6. A number computed from sample data used to estimate a parameter.
 Definition
6. What is statistic.
Term
7. A collection of all of the individuals about which we wish information.
 Definition
7. What is population.
Term
8. Type of samples required for valid inference.
 Definition
8. What is probability samples.
Term
9. Shape of the histogram of a sample when the sample is large and random.
 Definition
9. What is similar to population histogram.
Term
10. The difference between the value of a statistic from a sample and the parameter it estimates.
 Definition
10. What is error in the estimate.
Term
1. A list of the possible values of a variable together with the frequencies of each value.
 Definition
1. What is distribution.
Term
2. The sum of the probabilities of all possible outcomes.
 Definition
2. What is one.
Term
3. Using random numbers to imitate chance behavior.
 Definition
3. What is simulation.
Term
4. The probability of event A or event B where events A and B are disjoint.
 Definition
4. What is probability of event A plus probability of event B.
Term
5. A measure of the proportion of times an outcome occurs in the long run that gives us an indication of the
likelihood of the outcome.
 Definition
5. What is probability of an outcome.
Term
1. Value of the center line.
 Definition
1. What is mu.
Term
2) mu minus 3 times sigma over the square of n
 Definition
2. What is lower control limit.
Term
3) mu plus 3 times sigma over the square of n
 Definition
3. What is upper control limit.
Term
4. A procedure used to check a process at regular intervals to detect problems and correct them before they
become serious.
 Definition
4. What is process control.
Term
5. A chart plotting the means ( x-bars) of regular samples of size n against time; this chart is used to access
whether the process is in control.
 Definition
5. What is control chart or quality control chart.
Term
1. The grouping of experimental units according to some similar characteristic where the random allocation is
carried out separately within each group.
 Definition
1. What is blocking.
Term
2. The condition eliminated by randomly allocating individuals to treatments.
 Definition
2. What is bias.
Term
3. Results of a study that differ too much from what we expect due to just randomization to attribute to chance.
 Definition
3. What is statistically significant.
Term
4. The condition of having more than one individual in each treatment combination.
 Definition
4. What is replication.
Term
5. Fill in the blanks: The advantage of _______________ over _____________ is to remove variation
associated with the blocking variable from experimental error.
 Definition
5. What is “randomized block experiment” over “completely randomized experiment”.
Term
T/F 1. x-bar is the value of the mean of the sampling distribution of x-bar.
 Definition
1. What is False—the mean of the sampling distribution of x bar equals mu.
Term
T/F 2. The standard deviation of the population is less than the standard deviation of the sampling distribution of
x-bar.
 Definition
2. What is false—The standard deviation of the population is greater than the standard deviation of the
sampling distribution of x-bar.
Term
T/F 3. The sampling distribution of x-bar is always taller and skinnier than the population.
 Definition
3. What is true.
Term
T/F 4. The mean of the sampling distribution of x-bar gets closer and closer to mu as the sample size increases.
 Definition
4. What is false—The mean of the sampling distribution of x-bar exactly equals mu.
Term
T/F 5. The shape of the sampling distribution of x-bar gets closer and closer to the shape of the population as sample
size increases.
 Definition
5. What is false—The shape of the sampling distribution of x-bar gets closer and closer to Normal.
Term
T/F 6. The shape of the histogram of data in a sample gets closer and closer to the shape of the population as
sample size increases.
 Definition
6. What is True.
Term
T/F 7. The shape of the sampling distribution of x-bar gets closer and closer to Normal as the sample size increases
when the population is normal.
 Definition
7. What is false—The shape of the sampling distribution of x-bar is Normal regardless of sample size if the
population is Normal.
Term
T/F 8. The shape of the sampling distribution of x-bar gets closer and closer to Normal as the sample size increases
when the population is non-normal.
 Definition
8. What is true.
Term
T/F 9. The shape of the sampling distribution of x-bar is always Normal when the population shape is Normal.
 Definition
9. What is true.
Term
T/F 10. The standard deviation of the sampling distribution of x-bar gets closer and closer to sigma as n increases.
 Definition
10. What is false—The standard deviation of the sampling distribution of x-bar equals sigma over the square of n.
Term
T/F 11. The standard deviation of the sampling distribution of x-bar gets smaller and smaller as n increases.
 Definition
11. What is true.
Term
T/F 12. The standard deviation of the sampling distribution of x-bar gets closer and closer to sigma over the square of n
as n increases.
 Definition
12. What is false—The standard deviation of the sampling distribution of x-bar is equal to sigma over the square of n regardless of
sample size.
Term
T/F 13. We measure the variability of the sampling distribution of x-bar with sigma over the square of n
 .
 Definition
13. What is true.
Term
T/F 14. The sampling distribution of x-bar tells us the possible values for x-bar together with how often each occurs.
 Definition
14. What is true.
Term
T/F 15. The sampling distribution of x-bar tells us all possible values we could get in our sample of size n.
 Definition
23. What is false—The sampling distribution of x-bar tells us possible values we could get for the sample mean.
That’s because the sampling distribution gives all possible values for x-bar together with their frequencies (or probabilities).
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