TEST BANK
First Course in Statistics, A, 12th edition
By James T McClave
TU
TO
R
G
U
R
U
, TABLE OF CONTENT
Statistics, Data, and Statistical Thinking
1.1 The Science of Statistics
1.2 Types of Statistical Applications
1.3 Fundamental Elements of Statistics
1.4 Types of Data
1.5 Collecting Data: Sampling and Related Issues
1.6 The Role of Statistics in Critical Thinking and Ethics
Statistics in Action: Social Media Network Usage-Are You Linked In?
Using Technology: MINITAB: Accessing and Listing Data
Methods for Describing Sets of Data
2.1 Describing Qualitative Data
TU
2.2 Graphical Methods for Describing Quantitative Data
2.3 Numerical Measures of Central Tendency
2.4 Numerical Measures of Variability
2.5 Using the Mean and Standard Deviation to Describe Data
2.6 Numerical Measures of Relative Standing
2.7 Methods for Detecting Outliers: Box Plots and z-Scores
TO
2.8 Graphing Bivariate Relationships (Optional)
2.9 Distorting the Truth with Descriptive Statistics
Statistics in Action: Body Image Dissatisfaction: Real or Imagined?
Using Technology: MINITAB: Describing Data
TI-83/TI—84 Plus Graphing Calculator: Describing Data
Probability
R
3.1 Events, Sample Spaces, and Probability
3.2 Unions and Intersections
3.3 Complementary Events
3.4 The Additive Rule and Mutually Exclusive Events
G
3.5 Conditional Probability
3.6 The Multiplicative Rule and Independent Events
Statistics in Action: Lotto Buster! Can You Improve Your Chance of Winning?
U
Using Technology: TI-83/TI-84 Plus Graphing Calculator: Combinations and Permutations
Random Variables and Probability Distributions
4.1 Two Types of Random Variables
R
4.2 Probability Distributions for Discrete Random Variables
4.3 The Binomial Random Variable
4.4 Probability Distributions for Continuous Random Variables
4.5 The Normal Distribution
U
4.6 Descriptive Methods for Assessing Normality
4.7 Approximating a Binomial Distribution with a Normal Distribution (Optional)
4.8 Sampling Distributions
4.9 The Sampling Distribution of x and the Central Limit Theorem
Statistics in Action: Super Weapons Development — Is the Hit Ratio Optimized?
Using Technology: MINITAB: Discrete Probabilities
Inferences Based on a Single Sample
5.1 Identifying and Estimating the Target Parameter
5.2 Confidence Interval for a Population Mean: Normal (z) Statistic
5.3 Confidence Interval for a Population Mean: Student’s t-Statistic
5.4 Large-Sample Confidence Interval for a Population Proportion
,5.5 Determining the Sample Size
5.6 Confidence Interval for a Population Variance (Optional)
Statistics in Action: Medicare Fraud Investigations
Using Technology: MINITAB: Confidence Intervals
Inferences Based on a Single Sample
6.1 The Elements of a Test of Hypothesis
6.2 Formulating Hypotheses and Setting Up the Rejection Region
6.3 Observed Significance Levels: p-values
6.4 Test of Hypothesis about a Population Mean: Normal (z) Statistic
6.5 Test of Hypothesis about a Population Mean: Student’s t-Statistic
6.6 Large-Sample Test of Hypothesis about a Population Proportion
6.7 Test of Hypothesis about a Population Variance (Optional)
6.8 A Nonparametric Test about a Population Median (Optional)
TU
Statistics in Action: Diary of a KLEENEX User How Many Tissues in a Box?
Using Technology: MINITAB: Tests of Hypotheses
TI-83/TI-84 Plus Graphing Calculator: Tests of Hypotheses
Comparing Population Means
7.1 Identifying the Target Parameter
7.2 Comparing Two Population Means: Independent Sampling
TO
7.3 Comparing Two Population Means: Paired Difference Experiments
7.4 Determining the Sample Size
7.5 A Nonparametric Test for Comparing Two Populations: Independent Samples
7.6 A Nonparametric Test for Comparing Two Populations: Paired Difference Experiment
(Optional)
7.7 Comparing Three or More Population Means: Analysis of Variance (Optional)
R
Statistics in Action: Zixlt Corp. vs. Visa USA Inc. — A Libel Case
Using Technology: MINITAB: Comparing Means
TI-83/TI-84 Plus Graphing Calculator: Comparing Means
G
Comparing Population Proportions
8.1 Comparing Two Population Proportions: Independent Sampling
8.2 Determining Sample Size
8.3 Testing Category Probabilities: Multinomial Experiment
U
8.4 Testing Categorical Probabilities: Two-Way (Contingency) Table
Simple Linear Regression
9.1 Probabilistic Models
R
9.2 Fitting the Model: The Least Squares Approach
9.3 Model Assumptions
9.4 Assessing the Utility of the Model: Making Inferences about the Slope β1
U
9.5 The Coefficients of Correlation and Determination
9.6 Using the Model for Estimation and Prediction
9.7 A Complete Example
9.8 A Nonparametric Test for Correlation (Optional)
Statistics in Action: Can “Dowsers” Really Detect Water?
Using Technology: MINITAB: Simple Linear Regression
TI-83/TI-84 Plus Graphing Calculator: Simple Linear Regression
, Chapter 1
Name___________________________________
MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
Solve the problem.
1) Parking at a large university has become a very big problem. University administrators are 1)
interested in determining the average parking time (e.g. the time it takes a student to find a parking
spot) of its students. An administrator inconspicuously followed 250 students and carefully
recorded their parking times. Identify the population of interest to the university administration.
A) the 250 students that data was collected from
B) the parking time, defined to be the amount of time the student spent finding a parking spot
C) the entire set of students that park at the university
TU
D) a single student that parks at the university
2) As part of an economics class project, students were asked to randomly select 500 New York Stock 2)
Exchange (NYSE) stocks from the Wall Street Journal. As part of the project, students were asked to
summarize the current prices (also referred to as the closing price of the stock for a particular
trading date) of the collected stocks using graphical and numerical techniques. Identify the
TO
experimental unit of interest for this study.
A) the current price (or closing price) of a NYSE stock
B) the 500 NYSE stocks that current prices were collected from
C) the entire set of stocks that are traded on the NYSE
D) a single stock traded on the NYSE
3) The way in which an interviewer asks a question about political party affiliation causes 3)
R
respondents to answer that they have no affiliation when they actually do. What type of problem
has occurred?
A) measurement error B) nonresponse bias C) selection bias
G
4) Parking at a large university has become a very big problem. University administrators are 4)
interested in determining the average parking time (e.g. the time it takes a student to find a parking
spot) of its students. An administrator inconspicuously followed 250 students and carefully
recorded their parking times. What type of variable is the administration interested in collecting?
U
A) qualitative data B) quantitative data
5) Does online teaching help or hinder student learning? To help answer this question, a statistics 5)
R
teacher decided to teach his three sections of a particular class using three different teaching models
- a traditional face-to-face section, a completely online section, and a hybrid or blended section
that incorporated both a face-to-face and online component in the section. Students were
randomly assigned to the different sections, taught identical information using the different
U
teaching formats, and given identical examinations to measure student learning. The goal was to
identify if the teaching method used affected student learning performance. Identify the data
collection method used in this study.
A) data from a designed experiment
B) data collected observationally
C) data from a published source
6) Which data about paintings would not be qualitative? 6)
A) the artist B) the theme C) the value D) the style
1
First Course in Statistics, A, 12th edition
By James T McClave
TU
TO
R
G
U
R
U
, TABLE OF CONTENT
Statistics, Data, and Statistical Thinking
1.1 The Science of Statistics
1.2 Types of Statistical Applications
1.3 Fundamental Elements of Statistics
1.4 Types of Data
1.5 Collecting Data: Sampling and Related Issues
1.6 The Role of Statistics in Critical Thinking and Ethics
Statistics in Action: Social Media Network Usage-Are You Linked In?
Using Technology: MINITAB: Accessing and Listing Data
Methods for Describing Sets of Data
2.1 Describing Qualitative Data
TU
2.2 Graphical Methods for Describing Quantitative Data
2.3 Numerical Measures of Central Tendency
2.4 Numerical Measures of Variability
2.5 Using the Mean and Standard Deviation to Describe Data
2.6 Numerical Measures of Relative Standing
2.7 Methods for Detecting Outliers: Box Plots and z-Scores
TO
2.8 Graphing Bivariate Relationships (Optional)
2.9 Distorting the Truth with Descriptive Statistics
Statistics in Action: Body Image Dissatisfaction: Real or Imagined?
Using Technology: MINITAB: Describing Data
TI-83/TI—84 Plus Graphing Calculator: Describing Data
Probability
R
3.1 Events, Sample Spaces, and Probability
3.2 Unions and Intersections
3.3 Complementary Events
3.4 The Additive Rule and Mutually Exclusive Events
G
3.5 Conditional Probability
3.6 The Multiplicative Rule and Independent Events
Statistics in Action: Lotto Buster! Can You Improve Your Chance of Winning?
U
Using Technology: TI-83/TI-84 Plus Graphing Calculator: Combinations and Permutations
Random Variables and Probability Distributions
4.1 Two Types of Random Variables
R
4.2 Probability Distributions for Discrete Random Variables
4.3 The Binomial Random Variable
4.4 Probability Distributions for Continuous Random Variables
4.5 The Normal Distribution
U
4.6 Descriptive Methods for Assessing Normality
4.7 Approximating a Binomial Distribution with a Normal Distribution (Optional)
4.8 Sampling Distributions
4.9 The Sampling Distribution of x and the Central Limit Theorem
Statistics in Action: Super Weapons Development — Is the Hit Ratio Optimized?
Using Technology: MINITAB: Discrete Probabilities
Inferences Based on a Single Sample
5.1 Identifying and Estimating the Target Parameter
5.2 Confidence Interval for a Population Mean: Normal (z) Statistic
5.3 Confidence Interval for a Population Mean: Student’s t-Statistic
5.4 Large-Sample Confidence Interval for a Population Proportion
,5.5 Determining the Sample Size
5.6 Confidence Interval for a Population Variance (Optional)
Statistics in Action: Medicare Fraud Investigations
Using Technology: MINITAB: Confidence Intervals
Inferences Based on a Single Sample
6.1 The Elements of a Test of Hypothesis
6.2 Formulating Hypotheses and Setting Up the Rejection Region
6.3 Observed Significance Levels: p-values
6.4 Test of Hypothesis about a Population Mean: Normal (z) Statistic
6.5 Test of Hypothesis about a Population Mean: Student’s t-Statistic
6.6 Large-Sample Test of Hypothesis about a Population Proportion
6.7 Test of Hypothesis about a Population Variance (Optional)
6.8 A Nonparametric Test about a Population Median (Optional)
TU
Statistics in Action: Diary of a KLEENEX User How Many Tissues in a Box?
Using Technology: MINITAB: Tests of Hypotheses
TI-83/TI-84 Plus Graphing Calculator: Tests of Hypotheses
Comparing Population Means
7.1 Identifying the Target Parameter
7.2 Comparing Two Population Means: Independent Sampling
TO
7.3 Comparing Two Population Means: Paired Difference Experiments
7.4 Determining the Sample Size
7.5 A Nonparametric Test for Comparing Two Populations: Independent Samples
7.6 A Nonparametric Test for Comparing Two Populations: Paired Difference Experiment
(Optional)
7.7 Comparing Three or More Population Means: Analysis of Variance (Optional)
R
Statistics in Action: Zixlt Corp. vs. Visa USA Inc. — A Libel Case
Using Technology: MINITAB: Comparing Means
TI-83/TI-84 Plus Graphing Calculator: Comparing Means
G
Comparing Population Proportions
8.1 Comparing Two Population Proportions: Independent Sampling
8.2 Determining Sample Size
8.3 Testing Category Probabilities: Multinomial Experiment
U
8.4 Testing Categorical Probabilities: Two-Way (Contingency) Table
Simple Linear Regression
9.1 Probabilistic Models
R
9.2 Fitting the Model: The Least Squares Approach
9.3 Model Assumptions
9.4 Assessing the Utility of the Model: Making Inferences about the Slope β1
U
9.5 The Coefficients of Correlation and Determination
9.6 Using the Model for Estimation and Prediction
9.7 A Complete Example
9.8 A Nonparametric Test for Correlation (Optional)
Statistics in Action: Can “Dowsers” Really Detect Water?
Using Technology: MINITAB: Simple Linear Regression
TI-83/TI-84 Plus Graphing Calculator: Simple Linear Regression
, Chapter 1
Name___________________________________
MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
Solve the problem.
1) Parking at a large university has become a very big problem. University administrators are 1)
interested in determining the average parking time (e.g. the time it takes a student to find a parking
spot) of its students. An administrator inconspicuously followed 250 students and carefully
recorded their parking times. Identify the population of interest to the university administration.
A) the 250 students that data was collected from
B) the parking time, defined to be the amount of time the student spent finding a parking spot
C) the entire set of students that park at the university
TU
D) a single student that parks at the university
2) As part of an economics class project, students were asked to randomly select 500 New York Stock 2)
Exchange (NYSE) stocks from the Wall Street Journal. As part of the project, students were asked to
summarize the current prices (also referred to as the closing price of the stock for a particular
trading date) of the collected stocks using graphical and numerical techniques. Identify the
TO
experimental unit of interest for this study.
A) the current price (or closing price) of a NYSE stock
B) the 500 NYSE stocks that current prices were collected from
C) the entire set of stocks that are traded on the NYSE
D) a single stock traded on the NYSE
3) The way in which an interviewer asks a question about political party affiliation causes 3)
R
respondents to answer that they have no affiliation when they actually do. What type of problem
has occurred?
A) measurement error B) nonresponse bias C) selection bias
G
4) Parking at a large university has become a very big problem. University administrators are 4)
interested in determining the average parking time (e.g. the time it takes a student to find a parking
spot) of its students. An administrator inconspicuously followed 250 students and carefully
recorded their parking times. What type of variable is the administration interested in collecting?
U
A) qualitative data B) quantitative data
5) Does online teaching help or hinder student learning? To help answer this question, a statistics 5)
R
teacher decided to teach his three sections of a particular class using three different teaching models
- a traditional face-to-face section, a completely online section, and a hybrid or blended section
that incorporated both a face-to-face and online component in the section. Students were
randomly assigned to the different sections, taught identical information using the different
U
teaching formats, and given identical examinations to measure student learning. The goal was to
identify if the teaching method used affected student learning performance. Identify the data
collection method used in this study.
A) data from a designed experiment
B) data collected observationally
C) data from a published source
6) Which data about paintings would not be qualitative? 6)
A) the artist B) the theme C) the value D) the style
1
