# Learn Statistics the Interactive Way with Interactive Statistics 3rd Edition Online Resources and Solutions

## Interactive Statistics rd Edition Answers

#### A Comprehensive Guide

If you are taking a course in statistics or want to learn more about the subject, you might have come across Interactive Statistics, a textbook by Martha Aliaga and Brenda Gunderson that covers the essential topics in introductory statistics with an emphasis on interactivity, visualization, and simulation.

## Interactive Statistics 3rd Edition Answers

Interactive Statistics is not just a book, but also an online platform that allows you to access a variety of resources and solutions to help you master the concepts and skills in statistics. You can use the online platform to explore data, perform simulations, conduct analyses, create graphs, check your understanding, and get feedback.

In this article, we will guide you through how to access the online resources and solutions for Interactive Statistics rd Edition, and how to use the interactive features and tools in the book and the platform. We will also provide you with the answers to the exercises and problems in each chapter of the book, so you can check your work and improve your performance.

## Chapter 1: Data Collection and Sampling Techniques

The first chapter of Interactive Statistics introduces you to the basics of data collection and sampling techniques, which are essential for any statistical analysis. You will learn about the different types of data and levels of measurement, the various sampling methods and sources of bias, and how to design experiments and observational studies.

### Section 1.1: Types of Data and Levels of Measurement

In this section, you will learn how to classify data into two main categories: qualitative and quantitative. Qualitative data are data that describe the quality or characteristics of something, such as color, gender, or opinion. Quantitative data are data that measure the quantity or amount of something, such as height, weight, or income.

You will also learn how to identify the level of measurement of a variable, which indicates how much information the variable provides. There are four levels of measurement: nominal, ordinal, interval, and ratio. Nominal data are data that can only be categorized or named, such as eye color or marital status. Ordinal data are data that can be ordered or ranked, but the differences between the values are not meaningful, such as letter grades or satisfaction ratings. Interval data are data that can be ordered and have meaningful differences, but do not have a true zero point, such as temperature or IQ scores. Ratio data are data that can be ordered, have meaningful differences, and have a true zero point, such as height or weight.

Here are some examples of exercises and problems from this section, along with their answers:

Classify each variable as qualitative or quantitative.

The number of pages in a book. Answer: Quantitative

The type of car owned by a person. Answer: Qualitative

The time it takes to run a mile. Answer: Quantitative

The favorite movie genre of a person. Answer: Qualitative

Identify the level of measurement of each variable.

The number of siblings a person has. Answer: Ratio

The zip code of a person's address. Answer: Nominal

The score on a math test out of 100 points. Answer: Interval

The rank of a movie on a website from 1 to 5 stars. Answer: Ordinal

### Section 1.2: Sampling Methods and Bias

In this section, you will learn how to select a sample from a population using different sampling methods, and how to avoid bias in your sampling process. A population is the entire group of individuals or objects that you want to study, while a sample is a subset of the population that you actually collect data from.

You will learn about four main types of sampling methods: simple random sampling, stratified sampling, cluster sampling, and convenience sampling. Simple random sampling is when you select a sample from the population in such a way that every individual or object has an equal chance of being selected. Stratified sampling is when you divide the population into groups based on some characteristic, such as age or gender, and then select a simple random sample from each group. Cluster sampling is when you divide the population into groups based on some geographic or administrative criterion, such as regions or schools, and then select a simple random sample of groups and include all individuals or objects in those groups in your sample. Convenience sampling is when you select a sample from the population based on what is easy or convenient for you, such as using volunteers or online surveys.

Here are some examples of exercises and problems from this section, along with their answers:

Identify the type of sampling method used in each scenario.

A researcher selects 100 students from a list of all students enrolled in a university and surveys them about their study habits. Answer: Simple random sampling

A researcher divides the population of a city into four income groups and selects 50 households from each group to survey about their spending habits. Answer: Stratified sampling

A researcher selects 10 schools from a district and surveys all teachers and students in those schools about their attitudes toward online learning. Answer: Cluster sampling

A researcher posts an online survey on a social media platform and invites anyone who sees it to participate. Answer: Convenience sampling

Identify the source and type of bias in each scenario.

A researcher surveys only the customers who bought a product from an online store and asks them to rate their satisfaction with the product. Answer: Selection bias, because the customers who did not buy the product are excluded from the population.

A researcher mails a survey to 1000 households and receives only 200 responses. Answer: Nonresponse bias, because the households that did not respond may have different characteristics or opinions than those who did.

A researcher interviews people on the street and asks them if they support a controversial political issue. Answer: Response bias, because the people may not answer honestly or may be influenced by the presence of the interviewer or other bystanders.

### Section 1.3: Designing Experiments and Observational Studies

In this section, you will learn how to design experiments and observational studies to answer research questions or test hypotheses about the relationship between variables. An experiment is a study in which you manipulate one or more variables, called factors, and measure their effect on another variable, called the response. An observational study is a study in which you do not manipulate any variables, but only observe and measure them as they occur naturally.

You will learn about the key elements and principles of designing experiments, such as randomization, replication, and control. Randomization is when you assign the experimental units, such as subjects or objects, to different levels of the factors using a random method, such as flipping a coin or using a computer program. Replication is when you repeat the experiment with enough experimental units to reduce the variability and increase the precision of your results. Control is when you use a baseline or reference group, such as a placebo or a standard treatment, to compare with the other groups and eliminate confounding variables. Confounding variables are variables that are not part of the experiment but can affect the response and create a false association between the factors and the response.

You will also learn about the different types of experimental designs, such as completely randomized design, randomized block design, matched pairs design, and factorial design. A completely randomized design is when you randomly assign all experimental units to different levels of one factor. A randomized block design is when you first group the experimental units into blocks based on some characteristic that may affect the response, such as age or gender, and then randomly assign each block to different levels of one factor. A matched pairs design is when you pair two experimental units that are similar in some characteristic that may affect the response, such as twins or siblings, and then randomly assign each pair to different levels of one factor. A factorial design is when you randomly assign all experimental units to different levels of two or more factors.

You will also learn about the difference between experimental and observational studies in terms of causality and generalizability. Causality is when you can infer that one variable causes another variable to change or occur. Generalizability is when you can apply your results to a larger population or a different setting. In general, experiments can establish causality but may have limited generalizability, while observational studies can have high generalizability but cannot establish causality.

Here are some examples of exercises and problems from this section, along with their answers:

Identify whether each study is an experiment or an observational study.

A researcher randomly assigns 50 smokers to either receive nicotine patches or placebo patches and measures their craving levels after four weeks. Answer: Experiment

A researcher surveys 1000 adults and asks them about their coffee consumption and sleep quality. Answer: Observational study

A researcher randomly selects 20 schools and divides them into two groups: one group receives a new math curriculum and the other group receives the standard math curriculum. The researcher then compares the test scores of the students in both groups after one year. Answer: Experiment

A researcher records the blood pressure and heart rate of 100 patients before and after they undergo a surgery. Answer: Observational study

Identify the type of experimental design used in each experiment.

A researcher randomly assigns 60 plants to three groups: one group receives water, one group receives fertilizer, and one group receives water and fertilizer. The researcher then measures the height of the plants after six weeks. Answer: Completely randomized design

A researcher randomly selects 40 students from four different majors: math, biology, psychology, and history. The researcher then randomly assigns 10 students from each major to either watch a comedy show or a drama show. The researcher then measures the mood of the students after the show. Answer: Randomized block design

A researcher pairs 20 married couples based on their age and income. The researcher then randomly assigns one spouse from each pair to either receive a massage or a relaxation CD. The researcher then measures the stress level of both spouses after the treatment. Answer: Matched pairs design

A researcher randomly assigns 80 rats to four groups: one group receives a high-fat diet and a high-dose of a drug, one group receives a high-fat diet and a low-dose of a drug, one group receives a low-fat diet and a high-dose of a drug, and one group receives a low-fat diet and a low-dose of a drug. The researcher then measures the weight gain of the rats after eight weeks. Answer: Factorial design

## Chapter 2: Organizing and Summarizing Data

The second chapter of Interactive Statistics teaches you how to organize and summarize data using frequency distributions and graphs, measures of central tendency, and measures of variation. You will learn how to display and describe data in different ways, depending on the type and level of measurement of the data.

### Section 2.1: Frequency Distributions and Graphs

In this section, you will learn how to create frequency distributions and graphs for qualitative and quantitative data. A frequency distribution is a table that shows how often each value or category of a variable occurs in a data set. A graph is a visual representation of a frequency distribution that helps you see patterns, trends, and outliers in the data.

You will learn how to create different types of graphs for qualitative data, such as bar graphs, pie charts, and Pareto charts. A bar graph is a graph that uses bars of equal width to show the frequencies or relative frequencies of each category of a variable. A pie chart is a graph that uses sectors of a circle to show the relative frequencies or percentages of each category of a variable. A Pareto chart is a graph that combines a bar graph and a line graph to show the frequencies or relative frequencies of each category of a variable in descending order, along with the cumulative frequency or relative frequency.

You will also learn how to create different types of graphs for quantitative data, such as histograms, frequency polygons, ogives, stem-and-leaf plots, dot plots, and boxplots. A histogram is a graph that uses bars of varying width to show the frequencies or relative frequencies of each class or interval of a variable. A frequency polygon is a graph that uses line segments to connect the midpoints of each class or interval of a variable. An ogive is a graph that uses line segments to connect the cumulative frequencies or relative frequencies of each class or interval of a variable. A stem-and-leaf plot is a graph that uses digits to separate each value of a variable into two parts: the stem (the leftmost digit or digits) and the leaf (the rightmost digit). A dot plot is a graph that uses dots to show the frequency or relative frequency of each value or class of a variable. A boxplot is a graph that uses a box and whiskers to show the five-number summary (minimum, first quartile, median, third quartile, maximum) of a variable.

Here are some examples of exercises and problems from this section, along with their answers:

0, 1, 2, 3, 0, 1, 4, 2, 1, 0, 3, 2, 1, 0, 2, 3, 1, 0, 4, 2. Number of Pets Frequency Relative Frequency --- --- --- 0 5 0.25 1 5 0.25 2 5 0.25 3 3 0.15 4 2 0.10 Total 20 1.00

Create a bar graph for the following data on the favorite ice cream flavor of 30 students: chocolate (12), vanilla (8), strawberry (6), mint (4).

Create a pie chart for the following data on the market share of four smartphone brands: Apple (40%), Samsung (30%), Huawei (20%), Xiaomi (10%).

2, 4, 8, 12, 10, 8, 4, 2.

Create a boxplot for the following data on the weights (in pounds) of 15 dogs: 12, 15, 18, 20, 22, 24, 25, 26, 27, 28, 30, 32, 35, 38, 40.

### Section 2.2: Measures of Central Tendency

In this section, you will learn how to calculate and interpret measures of central tendency for quantitative data. A measure of central tendency is a single value that summarizes or represents the center or typical value of a data set. There are three common measures of central tendency: mean, median, and mode.

The mean is the arithmetic average of the data values. To find the mean of a data set, you add up all the data values and divide by the number of data values. The mean is sensitive to outliers or extreme values in the data set.

The median is the middle value of the data values when they are arranged in ascending or descending order. To find the median of a data set, you sort the data values and locate the middle one. If there are an even number of data values, you take the average of the two middle ones. The median is resistant to outliers or extreme values in the data set.

The mode is the most frequent or common value in the data set. To find the mode of a data set, you count how many times each data value occurs and choose the one with the highest frequency. There can be more than one mode or no mode in a data set. The mode can be used for both qualitative and quantitative data.

Here are some examples of exercises and problems from this section, along with their answers:

5, 7, 9, 10, 12, 15, 18, 20. Answer:

Mean = (5 + 7 + 9 + 10 + 12 + 15 + 18 + 20) / 8 = 12 Median = (10 + 12) / 2 = 11 Mode = None

3, 5, 5, 6, 7, 8, 9, 10, 10. Answer:

Mean = (3 + 5 + 5 + 6 + 7 + 8 + 9 + 10 + 10) / 9 = 7 Median = 7 Mode = 5 and 10

Find the mean, median, and mode of the following data set: red, blue, green, yellow, red, purple, blue, red. Answer:

Mean = Not applicable for qualitative data. Median = Not applicable for qualitative data. Mode = Red

### Section 2.3: Measures of Variation

In this section, you will learn how to calculate and interpret measures of variation for quantitative data. A measure of variation is a single value that summarizes or represents the spread or variability of a data set. There are four common measures of variation: range, standard deviation, variance, and coefficient of variation.

The range is the difference between the maximum and minimum values in the data set. To find the range of a data set, you subtract the minimum value from the maximum value. The range is sensitive to outliers or extreme values in the data set.

The standard deviation is the average distance of the data values from the mean. To find the standard deviation of a data set, you use the following formula:

where s is the sample standard deviation, xi is the i data value, x̄ is the sample mean, and n is the sample size. The standard deviation is sensitive to outliers or extreme values in the data set.

The variance is the square of the standard deviation. To find the variance of a data set, you use the following formula:

where s is the sample variance. The variance is sensitive to outliers or extreme values in the data set.

The coefficient of variation is the ratio of the standard deviation to the mean expressed as a percentage. To find the coefficient of variation of a data set, you use the following formula:

where CV is the coefficient of variation. The coefficient of variation is useful for comparing the variability of two or more data sets with different units or means.

Here are some examples of exercises and problems from this section, along with their answers:

5, 7, 9, 10, 12, 15, 18, 20. Answer:

Range = 20 - 5 = 15 Standard deviation = √[(5 - 12) + (7 - 12) + (9 - 12) + (10 - 12) + (12 - 12) + (15 - 12) + (18 - 12) + (20 - 12)] / 8 ≈ 4.95 Variance = (4.95) ≈ 24.50 Coefficient of variation = (4.95 / 12) x 100% &