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Questions and Answers
What is the difference between a parameter and a statistic?
What is the difference between a parameter and a statistic?
The average salary of all MBAs is a parameter.
The average salary of all MBAs is a parameter.
True (A)
What is the difference between descriptive statistics and inferential statistics?
What is the difference between descriptive statistics and inferential statistics?
Descriptive statistics summarizes data from a sample, while inferential statistics uses sample data to draw conclusions about a larger population.
The average age of people from a sample of three states is an example of a ______.
The average age of people from a sample of three states is an example of a ______.
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Match the following terms with their definitions:
Match the following terms with their definitions:
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The data set mentioned in the content is a sample.
The data set mentioned in the content is a sample.
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Which of the following is NOT an example of descriptive statistics?
Which of the following is NOT an example of descriptive statistics?
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What is the purpose of inferential statistics?
What is the purpose of inferential statistics?
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Which of the following is NOT a characteristic of data?
Which of the following is NOT a characteristic of data?
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Statistics is only concerned with collecting and organizing data.
Statistics is only concerned with collecting and organizing data.
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What is the main purpose of statistics?
What is the main purpose of statistics?
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A ______ is a subset of a population.
A ______ is a subset of a population.
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In the example survey, what does the 1708 adults represent?
In the example survey, what does the 1708 adults represent?
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The responses of all adults in the U.S. regarding global warming represents the sample.
The responses of all adults in the U.S. regarding global warming represents the sample.
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Identify the data set in the example survey.
Identify the data set in the example survey.
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Which level of measurement allows for ordering data, but does not provide meaningful differences between entries?
Which level of measurement allows for ordering data, but does not provide meaningful differences between entries?
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The base prices of vehicle models are an example of qualitative data.
The base prices of vehicle models are an example of qualitative data.
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What is the primary characteristic of the nominal level of measurement?
What is the primary characteristic of the nominal level of measurement?
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The ___ level of measurement allows for meaningful differences between data entries and has a zero point that represents a position on a scale, not an inherent absence.
The ___ level of measurement allows for meaningful differences between data entries and has a zero point that represents a position on a scale, not an inherent absence.
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Which of the following examples would be considered data at the nominal level?
Which of the following examples would be considered data at the nominal level?
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Quantitative data can only be measured at the interval or ratio levels.
Quantitative data can only be measured at the interval or ratio levels.
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Match each level of measurement with its corresponding characteristic:
Match each level of measurement with its corresponding characteristic:
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The term "" refers to the unique identifiers of an item or entity, while "" refers to the categorization scheme for those items.
The term "" refers to the unique identifiers of an item or entity, while "" refers to the categorization scheme for those items.
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What makes the 'ratio' level of measurement different from the 'interval' level?
What makes the 'ratio' level of measurement different from the 'interval' level?
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The 'interval' level of measurement allows for calculating ratios between two data values.
The 'interval' level of measurement allows for calculating ratios between two data values.
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What are the four levels of measurement described in the text?
What are the four levels of measurement described in the text?
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Which of the following is NOT a characteristic of the ratio level of measurement?
Which of the following is NOT a characteristic of the ratio level of measurement?
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Match the following level of measurement with its primary characteristic:
Match the following level of measurement with its primary characteristic:
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What is the primary difference between an observational study and an experimental study?
What is the primary difference between an observational study and an experimental study?
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Data collection is the first step in designing a statistical study.
Data collection is the first step in designing a statistical study.
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Which of the following methods of data collection would be most appropriate for studying the effect of eating oatmeal on lowering blood pressure?
Which of the following methods of data collection would be most appropriate for studying the effect of eating oatmeal on lowering blood pressure?
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A survey is the best method to collect data on US residents' approval rating of the president.
A survey is the best method to collect data on US residents' approval rating of the president.
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What is the term for an extraneous factor that can influence the outcome of an experiment, making it difficult to isolate the effect of the treatment being studied?
What is the term for an extraneous factor that can influence the outcome of an experiment, making it difficult to isolate the effect of the treatment being studied?
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The ______ effect describes a situation where a subject experiences a positive response to a placebo, even though they have not received any actual treatment.
The ______ effect describes a situation where a subject experiences a positive response to a placebo, even though they have not received any actual treatment.
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Match the following methods of data collection with the appropriate example:
Match the following methods of data collection with the appropriate example:
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Which of the following is NOT a key element of experimental design?
Which of the following is NOT a key element of experimental design?
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Randomization in an experiment ensures that each subject has an equal chance of being assigned to any of the treatment groups.
Randomization in an experiment ensures that each subject has an equal chance of being assigned to any of the treatment groups.
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What is the purpose of replication in an experiment?
What is the purpose of replication in an experiment?
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What must be done within blocks divided by gender before assigning to treatment or control groups?
What must be done within blocks divided by gender before assigning to treatment or control groups?
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Random sample selection can only be done using a random number table.
Random sample selection can only be done using a random number table.
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What is the total number of students currently enrolled in statistics mentioned?
What is the total number of students currently enrolled in statistics mentioned?
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To form a simple random sample, assign numbers from 1 to ______ to each student.
To form a simple random sample, assign numbers from 1 to ______ to each student.
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Match the following sampling methods with their descriptions:
Match the following sampling methods with their descriptions:
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Flashcards
Sample
Sample
A subset of responses from a population, like adults in the U.S.
Parameter
Parameter
A numerical value that describes a characteristic of a population.
Statistic
Statistic
A numerical value that describes a characteristic of a sample.
Average Salary of MBAs
Average Salary of MBAs
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Descriptive Statistics
Descriptive Statistics
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Inferential Statistics
Inferential Statistics
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Population vs Sample
Population vs Sample
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Average Age
Average Age
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Data
Data
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Population
Population
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Data Set
Data Set
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Survey
Survey
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Response
Response
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Global Warming Survey
Global Warming Survey
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Experiment
Experiment
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Observational Study
Observational Study
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Control in Experiments
Control in Experiments
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Randomization
Randomization
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Replication
Replication
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Placebo Effect
Placebo Effect
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Confounding Variables
Confounding Variables
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Call Letters
Call Letters
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Levels of Measurement
Levels of Measurement
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Nominal Level
Nominal Level
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Ordinal Level
Ordinal Level
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Interval Level
Interval Level
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Ratio Level
Ratio Level
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Data Collection
Data Collection
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Qualitative Data
Qualitative Data
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Quantitative Data
Quantitative Data
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Categorizing Data
Categorizing Data
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Zero in Data
Zero in Data
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Random Assignment
Random Assignment
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Simple Random Sample
Simple Random Sample
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Random Number Generation
Random Number Generation
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Study Notes
Introduction to Statistics
- Data is information from observations, counts, measurements, or responses.
- Examples: People eating three daily servings of whole grains reduce stroke risk by 37%. 70% of 1500 spinal cord injuries to minors result from vehicle accidents, and 68% were not wearing seatbelts.
- Statistics is the science of collecting, organizing, analyzing, and interpreting data to make decisions.
What is Data?
- Data consists of information from observations, counts, measurements, or responses.
Data Sets
- Population: The collection of all possible outcomes, responses, measurements, or counts of interest.
- Sample: A subset of the population.
Parameter and Statistic
- Parameter: A number describing a population characteristic.
- Example: Average age of all people in a country.
- Statistic: A number describing a sample characteristic.
- Example: Average age of people from a sample of three states.
Example: Identifying Data Sets
- Population: Responses of all adults in the U.S.
- Sample: Responses of 1708 adults in the U.S. survey.
- Data Set: 939 "yes" and 769 "no" responses.
Example: Distinguish Parameter and Statistic
- Parameter: A recent survey of MBAs reported that the average salary for an MBA is over $82,000.
- This is a sample statistic, as it is based on a subset of the MBA population.
Branches of Statistics
- Descriptive Statistics: Organizing, summarizing, and displaying data (e.g., tables, charts, averages).
- Inferential Statistics: Using sample data to draw conclusions about a population.
Example: Descriptive and Inferential Statistics
- Descriptive: For unmarried men, approximately 70% were alive at age 65. For married men, 90% were alive at age 65.
- Inferential: Being married is associated with longer life for men.
Types of Data
- Qualitative Data: Attributes, labels, or non-numerical entries (e.g., place of birth, eye color).
- Quantitative Data: Numerical measurements or counts (e.g., age, weight, temperature).
Example: Classifying Data by Type
- Qualitative Data: Model names (e.g., Fusion 14 S, F-150 XL).
- Quantitative Data: Base prices of vehicles.
Levels of Measurement
- Nominal Level: Categorized using names, labels, or qualities (e.g., names of network affiliates, categories of survey responses).
- Ordinal Level: Qualitative or quantitative data that can be arranged in order (e.g., ranking of TV programs, socioeconomic levels).
- Interval Level: Quantitative data where differences between data entries are meaningful; zero does not imply none (e.g., temperatures in Celsius or Fahrenheit).
- Ratio Level: Quantitative data where differences between data entries are meaningful; zero does imply none and ratios are meaningful (e.g., height, weight, age, income, etc).
Example: Classifying Data by Level
- Nominal: Network affiliates for top five TV programs
- Ordinal: Ranking of five TV programs.
Levels of Measurement (Ratio)
- Similar to interval level
- Zero entry is inherent zero
- Ratio of two data values can be formed
- One data value can be expressed as a multiple of another
Example: Classifying Data by Level (Interval/Ratio)
- Interval: Home run totals by team
- Ratio: World Series victories by year
Designing a Statistical Study
- Identify variables of interest and the population.
- Develop a data collection plan (e.g., sample vs. population).
- Collect data.
- Describe data with descriptive stats.
- Interpret data with inferential stats.
Data Collection
- Observational Study: Observing and measuring characteristics of a population.
- Experiment: Applying a treatment to part of the population and observing responses.
- Simulation: Using a model to reproduce conditions of a situation or process.
Example: Methods of Data Collection
- Survey: Investigation of population characteristics.
- Observation: Measuring characteristics without intervention.
- Experiment: Applying a treatment and observing responses.
- Simulation: Using a mathematical/physical model (e.g. crash testing).
Key Elements of Experimental Design
- Control: Controlling for other effects not related to the treatment.
- Randomization: Randomly assigning subjects to treatment or control groups.
- Replication: Repeating the experiment with a large enough sample.
- Confounding Variables: Variables whose effects cannot be separated.
Key Elements of Experimental Design (Cont.)
- Placebos: A treatment with no active effect given to subjects in a control group.
- Blinding: Keeping subjects unaware of who is receiving treatment or a placebo.
- Double-Blind Experiment: Neither the subjects nor the experimenters know the treatment status.
Key Elements of Experimental Design (Replication)
- Replication: Repeating an experiment using a large sample size (e.g., an experiment testing a vaccine by giving it and a placebo to 2 groups of 10,000 people).
Example: Experimental Design
- A gum company wants to test its effectiveness for quitting smoking. The company identifies smokers and puts them into male and female groups. It is important to note that the sample size of one thousand is small. In this example the males and females are not considered similar enough. The company could divide smokers into groups based on health issues, habits, and other variables before assigning them or testing them.
Sampling Techniques
- Simple Random Sample: Every member of a population has an equal chance of being selected.
Example: Simple Random Sample
- Assign numbers to all members of the population. Randomly choose numbers for the sample.
Other Sampling Techniques
- Stratified Sample: Divide the population into subgroups (strata) and collect a random sample from each stratum.
- Cluster Sample: Divide population into groups and randomly select some of these groups to collect the sample data.
- Systematic Sample: Select members at regular intervals from a sorted list.
Example: Identifying Sampling Techniques
- Stratified: Dividing students by major before selecting a sample.
- Simple Random: Randomly selecting students from a numbered list.
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Description
This quiz covers the basics of statistics, focusing on the definitions of data, populations, samples, parameters, and statistics. You'll explore how to collect, analyze, and interpret data effectively. Test your understanding of the fundamental concepts that are essential for making informed decisions using statistical information.