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Test Bank for Introduction to Statistics and Data Analysis, 7th Edition by Roxy Peck

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Test Bank for Introduction to Statistics and Data Analysis, 7e 7th Edition by Roxy Peck, Chris Olsen. Complete Chapters (Chap 1- 16) are included with answers. 1. THE ROLE OF STATISTICS AND THE DATA ANALYSIS PROCESS: Why Study Statistics? The Nature and Role of Variability. Statistics and the Data Analysis Process. Types of Data and Some Simple Graphical Displays. 2. COLLECTING DATA SENSIBLY: Statistical Studies: Observation and Experimentation. Sampling. Simple Comparative Experiments. More on Experimental Design. Interpreting and Communicating the Results of Statistical Analyses. More on Observational Studies: Designing Surveys (online). 3. GRAPHICAL METHODS FOR DESCRIBING DATA: Displaying Categorical Data: Comparative Bar Charts and Pie Charts. Displaying Numerical Data: Stem-and-Leaf Displays. Displaying Numerical Data: Frequency Distributions and Histograms. Displaying Bivariate Numerical Data. Bivariate and Multivariable Graphical Displays. Interpreting and Communicating the Results of Statistical Analyses. 4. NUMERICAL METHODS FOR DESCRIBING DATA: Describing the Center of a Data Set. Describing Variability in a Data Set. Summarizing a Data Set: Boxplots. Interpreting Center and Variability: Chebyshev’s Rule, the Empirical Rule, and z Scores. Interpreting and Communicating the Results of Statistical Analyses. 5. SUMMARIZING BIVARIATE DATA: Correlation. Linear Regression: Fitting a Line to Bivariate Data. Assessing the Fit of a Line. Nonlinear Relationships and Transformations. Interpreting and Communicating the Results of Statistical Analyses. Logistic Regression (online). 6. PROBABILITY: Chance Experiments and Events. Definition of Probability. Basic Properties of Probability. Conditional Probability. Independence. Some General Probability Rules. Estimating Probabilities Empirically Using Simulation. 7. RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS: Random Variables. Probability Distributions for Discrete Random Variables. Probability Distributions for Continuous Random Variables. Mean and Standard Deviation of a Random Variable. Binomial and Geometric Distributions. Normal Distributions. Checking for Normality and Normalizing Transformations. Using the Normal Distribution to Approximate a Discrete Distribution. 8. SAMPLING VARIABILITY AND SAMPLING DISTRIBUTIONS: Statistics and Sampling Variability. The Sampling Distribution of a Sample Mean. The Sampling Distribution of a Sample Proportion. 9. ESTIMATION USING A SINGLE SAMPLE: Point Estimation. Large-Sample Confidence Interval for a Population Proportion. Confidence Interval for a Population Mean. Interpreting and Communicating the Results of Statistical Analyses. Bootstrap Confidence Intervals for a Population Proportion (optional). Bootstrap Confidence Intervals for a Population Mean (optional). 10. HYPOTHESIS TESTING USING A SINGLE SAMPLE: Hypotheses and Test Procedures. Errors in Hypothesis Testing. Large-Sample Hypothesis Tests for a Population Proportion. Hypothesis Tests for a Population Mean. Power and Probability of Type II Error. Interpreting and Communicating the Results of Statistical Analyses. Exact Binomial Test and Randomization Test for a Population Proportion (optional). Randomization Test for a Population Mean (optional). 11. COMPARING TWO POPULATIONS OR TREATMENTS: Inferences Concerning the Difference Between Two Population or Treatment Means Using Independent Samples. Inferences Concerning the Difference Between Two Population or Treatment Means Using Paired Samples. Large-Sample Inferences Concerning the Difference Between Two Population or Treatment Proportions. Interpreting and Communicating the Results of Statistical Analyses. Randomization-Based Inference for a Difference in Proportions (optional). Randomization-Based Inference for a Difference in Means (optional). 12. THE ANALYSIS OF CATEGORICAL DATA AND GOODNESS-OF-FIT TESTS: Chi-Square Tests for Univariate Data. Tests for Homogeneity and Independence in a Two-way Table. Interpreting and Communicating the Results of Statistical Analyses. 13. SIMPLE LINEAR REGRESSION AND CORRELATION: INFERENTIAL METHODS: Simple Linear Regression Model. Inferences about the Slope of the Population Regression Line. Checking Model Adequacy. Inferences Based on the Estimated Regression Line (online). Inferences About the Population Correlation Coefficient (online). Interpreting and Communicating the Results of Statistical Analyses (online). 14. MULTIPLE REGRESSION ANALYSIS: Multiple Regression Models. Fitting a Model and Assessing Its Utility. Inferences Based on an Estimated Model (online). Other Issues in Multiple Regression (online). Interpreting and Communicating the Results of Statistical Analyses (online). 15. ANALYSIS OF VARIANCE: Single-Factor ANOVA and the F Test. Multiple Comparisons. The F Test for a Randomized Block Experiment (online). Two-Factor ANOVA (online). Interpreting and Communicating the Results of Statistical Analyses (online). 16. NONPARAMETRIC (DISTRIBUTION-FREE) STATISTICAL METHODS (ONLINE): Distribution-Free Procedures for Inferences About a Difference Between Two Population or Treatment Means Using Independent Samples (Optional). Distribution Free Procedures for Inferences About a Difference Between Two Population or Treatment Means Using Paired Samples. Distribution-Free ANOVA.

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Introduction To Statistics
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Institution
Introduction to Statistics
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Introduction to Statistics

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Uploaded on
September 16, 2024
Number of pages
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Written in
2024/2025
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Complete Chapters Test Bank ✅

Answers Included ✅

Instant PDF download ✅

A + Grade ✅

,Chapter 01 7e Peck
Complete Chapters Included ✅
Indicate whether the statement is true or false.
INSTRUCTIONS:
The following questions are in a True/False format. The answers to these questions will frequently depend on
remembering facts, understanding of the concepts, and knowing the statistical vocabulary. Before answering
these questions, be sure to read them carefully!
1. Methods for summarizing data make up the branch of statistics called inferential statistics.
a. True
b. False

ANSWER: False


2. A primary use of inferential statistics is to make generalizations from a sample to a population.
a. True
b. False

ANSWER: True
3. The relative frequency for a particular category is the number of times the category appears in the data.
a. True
b. False

ANSWER: False
4. A data set consisting of many observations of a single characteristic is a categorical data set.
a. True
b. False

ANSWER: False
5. The entire collection of individuals or objects about which information is desired is called a sample.
a. True
b. False

ANSWER: False
6. A data set is discrete if the possible values are isolated points on the number line.
a. True
b. False

ANSWER: True




Page 1

,Name: Class: Date:

Chapter 01 7e Peck

INSTRUCTIONS:
The following questions are in a True/False format. The answers to these questions will frequently depend on
remembering facts, understanding of the concepts, and knowing the statistical vocabulary. Before answering
these questions, be sure to read them carefully!
7. Dotplots work best for small and moderate sized numerical data sets.
a. True
b. False

ANSWER: True

8. Bar charts should be used with categorical data.
a. True
b. False

ANSWER: True
9. A data set is discrete if the possible values are isolated points on the number line.
a. True
b. False

ANSWER: True
10. Frequency distributions can only be used with categorical data.
a. True
b. False

ANSWER: False
Indicate the answer choice that best completes the statement or answers the question.
11. The following variables are commonly used to describe human appearance:
I) Eye color (blue, green, brown, grey)
II) Hair color (brown, black, blond, grey)
III) Hair length
IV) Waist circumference
Which of these variables are categorical?
a. I only
b. II only
c. II and III only
d. I and II only

ANSWER: d




Page 2

, Name: Class: Date:

Chapter 01 7e Peck

12. Your laboratory procedure asks you to record the following information about each sample you analyze:
I) Sample weight in grams
II) Name of the customer who submitted the sample
III) Age of the customer who submitted the sample
IV) Sample temperature at the time of submission
Which of these represents numerical data?
a. I only
b. I and II only
c. I and III only
d. I, III, and IV only

ANSWER: d
13. A consumer information survey form solicits the following responses:
I) Age of the respondent
II) Gender of the respondent
III) Level of job satisfaction (satisfied, somewhat satisfied, dissatisfied)
IV) Annual income
Which of the responses represents categorical data?
a. I only
b. II only
c. III only
d. II and III only

ANSWER: d
14. You are gathering data on the sales of packaged hard candies sold in a hotel gift shop. Your variables of interest
include:
I) The type of container in which the candies are packaged (glass jar, cardboard box, or plastic bag)
II) The weight of the container
III) The number of candies in the container
IV) The height above the floor at which the candy-filled container was displayed
Which of the variables of interest are numerical and discrete?
a. I only
b. II only
c. III only
d. II and III only

ANSWER: c




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