QMB-3200 Exam Quiz 2 A+ Pass New Update
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1. A population where each element of the population is assigned to one and
only one of exactly two classes or categories is a: Binomial Population
2. Goodness of fit test utilizes: Chi-square test
3. A goodness of fit test is never conducted as a: Lower tailed Test or a Two Tailed Test
4. An important application of the chi-square distribution does not include:: mak-
ing inferences about two population variances
5. A goodness of fit test is always: An upper tailed test
6. The degrees of freedom for a contingency table with 4 rows and 3 columns
is: 6 ....... (Note: DF for contingency Table (N rows - 1)(M Columns - 1).
7. In order not to violate the requirements necessary to use the chi-square
distribution, a necessary requirement concerns: a minimum expected frequency for each class
8. A chi-square goodness fit test can be used to determine whether to reject or
not reject a hypothesized probability distribution when the population has: Nor-
mal Distribution
9. In a chi-square test using a p-value approach at a level of significance of .05
and d.f.=15; the test statistic chi-square was calculated to be = 22.307, and the
area to the left of the test statistic = .90. Based on this information, the decision
of goodness of fit test would be:: Do not reject the null hypothesis
10. In a chi-square test using critical-value approach at
a level of significance of 0.05 and d.f. = 15; the test
statistic chi-square was calculated to be = 25.0, and the critical-value was
determined to be = 24.996.
Based on this information, the decision of
goodness of fit test would be:: reject the null hypothesis
11. In a goodness fit test conducted to validate whether the population has
certain probability distribution with certain assumed parameters, the decision
was to reject the null hypothesis, based on this information the conclusion of
the hypothesis test is
that:: the population does not have the probability distribution considered with assumed parameters
12. Multinomial distribution: a probability distribution for an experiment in which each trial has more than
two outcomes. is the probability distribution of the outcomes from a multinomial experiment.
, QMB-3200 Exam Quiz 2 A+ Pass New Update
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13. Goodness of fit: a statistical model describes how well it fits a set of observations. typically summarize the
discrepancy between observed values and the values expected under the model in question. Use Chi-square Test
14. Test of Independence: Contingency Table Test. used to determine if there is a significant relationship
between two nominal (categorical) variables. The frequency of each category for one nominal variable is compared
across the categories of the second nominal variable. Use Chi-square Test
15. ANOVA (analysis of variance): Statistical models and their associated estimation procedures (such
as the "variation" among and between groups) used to analyze the differences among group means in a sample. Use
F test.
16. Simple Linear Regression: statistical method that allows us to summarize and study relationships
between two continuous (quantitative) variables: One variable, denoted x, is regarded as the predictor, explanatory,
or independent variable.
17. Multiple Regression: used when we want to predict the value of a variable based on the value of two or
more other variables. The variable we want to predict is called the dependent variable (or sometimes, the outcome,
target or criterion variable).
18. Hypothesis (Goodness of Fit) Testfor Proportions of a Multinomial Popula-
tion: H0: The population follows a multinomial distribution with specified probabilities for each of the k categories
Note: The test statistic has a chi-square distribution with (k - 1) d.f. provided that the expected frequencies, ei are e5 for
all categories i.
Rejection rule:
Reject H0 if p-value < a or Chsq >= Critical value
DF: K-1
Chi-square Goodness of Fit test is always a one-sided upper-tail test.
19. Test of Independence: Contingency Tables: H0: The column variable is independent of the
row variable
Rejection rule:
Reject H0 if p -value < a or Chsq >= Critical value
DF: n rows and m columns, there are
Study online at https://quizlet.com/_hpglxc
1. A population where each element of the population is assigned to one and
only one of exactly two classes or categories is a: Binomial Population
2. Goodness of fit test utilizes: Chi-square test
3. A goodness of fit test is never conducted as a: Lower tailed Test or a Two Tailed Test
4. An important application of the chi-square distribution does not include:: mak-
ing inferences about two population variances
5. A goodness of fit test is always: An upper tailed test
6. The degrees of freedom for a contingency table with 4 rows and 3 columns
is: 6 ....... (Note: DF for contingency Table (N rows - 1)(M Columns - 1).
7. In order not to violate the requirements necessary to use the chi-square
distribution, a necessary requirement concerns: a minimum expected frequency for each class
8. A chi-square goodness fit test can be used to determine whether to reject or
not reject a hypothesized probability distribution when the population has: Nor-
mal Distribution
9. In a chi-square test using a p-value approach at a level of significance of .05
and d.f.=15; the test statistic chi-square was calculated to be = 22.307, and the
area to the left of the test statistic = .90. Based on this information, the decision
of goodness of fit test would be:: Do not reject the null hypothesis
10. In a chi-square test using critical-value approach at
a level of significance of 0.05 and d.f. = 15; the test
statistic chi-square was calculated to be = 25.0, and the critical-value was
determined to be = 24.996.
Based on this information, the decision of
goodness of fit test would be:: reject the null hypothesis
11. In a goodness fit test conducted to validate whether the population has
certain probability distribution with certain assumed parameters, the decision
was to reject the null hypothesis, based on this information the conclusion of
the hypothesis test is
that:: the population does not have the probability distribution considered with assumed parameters
12. Multinomial distribution: a probability distribution for an experiment in which each trial has more than
two outcomes. is the probability distribution of the outcomes from a multinomial experiment.
, QMB-3200 Exam Quiz 2 A+ Pass New Update
Study online at https://quizlet.com/_hpglxc
13. Goodness of fit: a statistical model describes how well it fits a set of observations. typically summarize the
discrepancy between observed values and the values expected under the model in question. Use Chi-square Test
14. Test of Independence: Contingency Table Test. used to determine if there is a significant relationship
between two nominal (categorical) variables. The frequency of each category for one nominal variable is compared
across the categories of the second nominal variable. Use Chi-square Test
15. ANOVA (analysis of variance): Statistical models and their associated estimation procedures (such
as the "variation" among and between groups) used to analyze the differences among group means in a sample. Use
F test.
16. Simple Linear Regression: statistical method that allows us to summarize and study relationships
between two continuous (quantitative) variables: One variable, denoted x, is regarded as the predictor, explanatory,
or independent variable.
17. Multiple Regression: used when we want to predict the value of a variable based on the value of two or
more other variables. The variable we want to predict is called the dependent variable (or sometimes, the outcome,
target or criterion variable).
18. Hypothesis (Goodness of Fit) Testfor Proportions of a Multinomial Popula-
tion: H0: The population follows a multinomial distribution with specified probabilities for each of the k categories
Note: The test statistic has a chi-square distribution with (k - 1) d.f. provided that the expected frequencies, ei are e5 for
all categories i.
Rejection rule:
Reject H0 if p-value < a or Chsq >= Critical value
DF: K-1
Chi-square Goodness of Fit test is always a one-sided upper-tail test.
19. Test of Independence: Contingency Tables: H0: The column variable is independent of the
row variable
Rejection rule:
Reject H0 if p -value < a or Chsq >= Critical value
DF: n rows and m columns, there are
