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MATH 110 Introduction to Statistics – Portage Learning
Module 1-10 EXAM QUESTIONS AND CORRECT VERIFIED
SOLUTIONS LATEST UPDATE THIS YEAR – JUST RELEASED
MATH 110 Introduction to Statistics – Portage Learning
Module 1-10 Exam Questions with Rationales
10-Line Exam Coverage in Points Form
1. Module 1: Data and Statistics – Data types (qualitative/quantitative), levels of
measurement (nominal, ordinal, interval, ratio), descriptive vs. inferential statistics,
sampling methods (simple random, stratified, cluster, systematic), and data collection
methods .
2. Module 2: Descriptive Statistics – Tabular and graphical methods (frequency
distributions, histograms, bar charts, pie charts, stem-and-leaf plots), measures of
central tendency (mean, median, mode), measures of variability (range, variance,
standard deviation, coefficient of variation) .
3. Module 3: Introduction to Probability – Sample spaces, events, complement, union,
intersection, mutually exclusive events, conditional probability, independent events,
permutations, combinations, Bayes' theorem .
4. Module 4: Probability Distributions – Random variables (discrete and continuous),
expected value, binomial probability distribution, normal distribution, standard normal
distribution (Z-scores), and the Standard Normal Table .
5. Module 5: Sampling and Sampling Distributions – Statistical inference, simple random
samples, sample mean, sample proportions, Central Limit Theorem, sampling error, and
sample size determination .
6. Module 6: Interval Estimation – Confidence intervals for population means, confidence
levels, margin of error, t-distribution, and confidence intervals for proportions .
7. Module 7: Hypothesis Testing – Null and alternative hypotheses, one-tailed and two-
tailed tests, Type I and Type II errors, level of significance, test statistics, p-values, and
decision rules .
8. Module 8: Comparisons Involving Means and Proportions – Independent samples,
dependent samples (paired t-test), hypothesis testing for differences between two
means, and hypothesis testing for two proportions .
9. Module 9: Regression Analysis – Linear correlation coefficient, positive and negative
correlations, critical values, coefficient of determination, and linear regression (least
squares method) .
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10. Module 10: Various Tests – Chi-square distribution, goodness-of-fit tests, tests for
independence, F-distribution, and analysis of variance (ANOVA) .
MODULE 1: Data and Statistics
100 MCQ Questions with Rationales
1. Which of the following is an example of quantitative data?
A) Hair color
B) Gender
C) Temperature in degrees Fahrenheit
D) Type of car
Correct Answer: C
Rationale: Quantitative data are numerical measurements that can be counted or measured.
Temperature in degrees Fahrenheit is quantitative. Hair color, gender, and type of car are
qualitative/categorical data .
2. The number of students in a classroom is an example of which type of data?
A) Qualitative
B) Continuous quantitative
C) Discrete quantitative
D) Nominal
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Correct Answer: C
Rationale: The number of students is countable and takes on integer values, making it discrete
quantitative data .
3. Which level of measurement involves categories that have a meaningful order but no
consistent interval between them?
A) Nominal
B) Ordinal
C) Interval
D) Ratio
Correct Answer: B
Rationale: Ordinal data has categories with a meaningful order (e.g., class rankings) but the
intervals between ranks are not necessarily equal .
4. A researcher collects data on the favorite colors of students. This is an example of:
A) Quantitative data
B) Nominal data
C) Ordinal data
D) Ratio data
Correct Answer: B
Rationale: Favorite colors are categories with no inherent order, making this nominal data .
5. What type of statistic is used to summarize and describe the main features of a dataset?
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A) Inferential statistics
B) Descriptive statistics
C) Predictive statistics
D) Diagnostic statistics
Correct Answer: B
Rationale: Descriptive statistics summarize and describe the main features of a dataset, while
inferential statistics make predictions or inferences about a population .
6. The entire group of individuals being studied is called the:
A) Sample
B) Parameter
C) Population
D) Statistic
Correct Answer: C
Rationale: The population is the entire group of individuals being studied. A sample is a subset
of the population .
7. A numerical summary of a population is called a:
A) Statistic
B) Parameter
C) Variable
D) Sample
MATH 110 Introduction to Statistics – Portage Learning
Module 1-10 EXAM QUESTIONS AND CORRECT VERIFIED
SOLUTIONS LATEST UPDATE THIS YEAR – JUST RELEASED
MATH 110 Introduction to Statistics – Portage Learning
Module 1-10 Exam Questions with Rationales
10-Line Exam Coverage in Points Form
1. Module 1: Data and Statistics – Data types (qualitative/quantitative), levels of
measurement (nominal, ordinal, interval, ratio), descriptive vs. inferential statistics,
sampling methods (simple random, stratified, cluster, systematic), and data collection
methods .
2. Module 2: Descriptive Statistics – Tabular and graphical methods (frequency
distributions, histograms, bar charts, pie charts, stem-and-leaf plots), measures of
central tendency (mean, median, mode), measures of variability (range, variance,
standard deviation, coefficient of variation) .
3. Module 3: Introduction to Probability – Sample spaces, events, complement, union,
intersection, mutually exclusive events, conditional probability, independent events,
permutations, combinations, Bayes' theorem .
4. Module 4: Probability Distributions – Random variables (discrete and continuous),
expected value, binomial probability distribution, normal distribution, standard normal
distribution (Z-scores), and the Standard Normal Table .
5. Module 5: Sampling and Sampling Distributions – Statistical inference, simple random
samples, sample mean, sample proportions, Central Limit Theorem, sampling error, and
sample size determination .
6. Module 6: Interval Estimation – Confidence intervals for population means, confidence
levels, margin of error, t-distribution, and confidence intervals for proportions .
7. Module 7: Hypothesis Testing – Null and alternative hypotheses, one-tailed and two-
tailed tests, Type I and Type II errors, level of significance, test statistics, p-values, and
decision rules .
8. Module 8: Comparisons Involving Means and Proportions – Independent samples,
dependent samples (paired t-test), hypothesis testing for differences between two
means, and hypothesis testing for two proportions .
9. Module 9: Regression Analysis – Linear correlation coefficient, positive and negative
correlations, critical values, coefficient of determination, and linear regression (least
squares method) .
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10. Module 10: Various Tests – Chi-square distribution, goodness-of-fit tests, tests for
independence, F-distribution, and analysis of variance (ANOVA) .
MODULE 1: Data and Statistics
100 MCQ Questions with Rationales
1. Which of the following is an example of quantitative data?
A) Hair color
B) Gender
C) Temperature in degrees Fahrenheit
D) Type of car
Correct Answer: C
Rationale: Quantitative data are numerical measurements that can be counted or measured.
Temperature in degrees Fahrenheit is quantitative. Hair color, gender, and type of car are
qualitative/categorical data .
2. The number of students in a classroom is an example of which type of data?
A) Qualitative
B) Continuous quantitative
C) Discrete quantitative
D) Nominal
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Correct Answer: C
Rationale: The number of students is countable and takes on integer values, making it discrete
quantitative data .
3. Which level of measurement involves categories that have a meaningful order but no
consistent interval between them?
A) Nominal
B) Ordinal
C) Interval
D) Ratio
Correct Answer: B
Rationale: Ordinal data has categories with a meaningful order (e.g., class rankings) but the
intervals between ranks are not necessarily equal .
4. A researcher collects data on the favorite colors of students. This is an example of:
A) Quantitative data
B) Nominal data
C) Ordinal data
D) Ratio data
Correct Answer: B
Rationale: Favorite colors are categories with no inherent order, making this nominal data .
5. What type of statistic is used to summarize and describe the main features of a dataset?
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A) Inferential statistics
B) Descriptive statistics
C) Predictive statistics
D) Diagnostic statistics
Correct Answer: B
Rationale: Descriptive statistics summarize and describe the main features of a dataset, while
inferential statistics make predictions or inferences about a population .
6. The entire group of individuals being studied is called the:
A) Sample
B) Parameter
C) Population
D) Statistic
Correct Answer: C
Rationale: The population is the entire group of individuals being studied. A sample is a subset
of the population .
7. A numerical summary of a population is called a:
A) Statistic
B) Parameter
C) Variable
D) Sample
