Introduction to statistics Flashcards
What is the primary goal of statistics?
a) Collecting and organizing data
b) Analyzing and interpreting data
c) Deriving information from data
d) All of the above
What does statistics help us do with enormous data?
a) Collect and organize it efficiently
b) Analyze and interpret it effectively
c) Separate sense from nonsense
d) All of the above
What is the role of statistics in dealing with data?
a) To make data more confusing
b) To make data more organized
c) To make sense of data
d) None of the above
d) All of the above
d) All of the above
c) To make sense of data
Statistics is the science of __________, __________, __________, and __________ information from data.
A knowledge of statistics helps separate _________ from _________.
Statistics helps us deal with _________ data that is around us.
Statistics is the science of collecting, organizing, analyzing, and interpreting information from data.
A knowledge of statistics helps separate sense from nonsense.
Statistics helps us deal with enormous data that is around us.
- is the following example an instance of qualitative (dichotomic), qualitative (polynomic), or quantitative (discrete) data?
Example: Eye color (blue, brown, green) - Is the following example an instance of qualitative (dichotomic), qualitative (polynomic), or quantitative (discrete) data?
Example: Gender (male, female) - Is the following example an instance of qualitative (dichotomic), qualitative (polynomic), or quantitative (discrete) data?
Example: Number of siblings - Is the following example an instance of qualitative (dichotomic), qualitative (polynomic), or quantitative (continuous) data?
Example: Height (in centimeters) - Is the following example an instance of qualitative (dichotomic), qualitative (polynomic), or quantitative (discrete) data?
Example: Level of education (primary, secondary, tertiary) - Is the following example an instance of qualitative (dichotomic), qualitative (polynomic), or quantitative (continuous) data?
Example: Temperature (in degrees Celsius)
- Qualitative (polynomic
- Qualitative (dichotomic)
- Quantitative (discrete)
- Quantitative (continuous)
- Qualitative (polynomic)
- Quantitative (continuous)
Which type of variable categorizes or describes an element of a population?
a) Qualitative
b) Quantitative
Which type of variable quantifies an element of a population?
a) Qualitative
b) Quantitative
Arithmetic operations, such as addition and averaging, are not meaningful for data resulting from which type of variable?
a) Qualitative
b) Quantitative
a) Qualitative
b) Quantitative
a) Qualitative
A characteristic that may differ from one entity to another is called a ________ variable.
Qualitative variables are also known as ________ or ________ variables.
Quantitative variables are also known as ________ or ________ variables.
variable
attribute, categorical
numerical, quantitative
Explain the difference between qualitative (categorical) and quantitative (numerical) variables and why arithmetic operations are not meaningful for qualitative data.
Provide an example of a qualitative variable and explain why arithmetic operations cannot be applied to it.
Give an example of a quantitative variable and explain why arithmetic operations can be applied to it.
Qualitative (categorical) variables describe characteristics or attributes and do not involve numerical values. They can be divided into categories or groups, and arithmetic operations like addition or averaging cannot be performed on these categories as they lack numerical meaning. On the other hand, quantitative (numerical) variables provide numerical quantities that can be measured or counted, allowing for arithmetic operations.
Example: Hair color. Arithmetic operations are not meaningful for hair color because hair colors are categories like blonde, brunette, or red. It does not make sense to add or average hair colors.
Example: Age. Age is a quantitative variable because it represents a numerical quantity. Arithmetic operations like addition and averaging can be applied to age, allowing us to calculate things like average age, add ages together, or find the difference between two ages.
Categorical data are commonly summarized using:
a) Percentages
b) Averages
c) Medians
d) Standard deviations
Measurement data are typically summarized using:
a) Percentages
b) Averages
c) Medians
d) Standard deviations
Which summary measure is commonly used for categorical data?
a) Mean
b) Median
c) Percentage
d) Standard deviation
Categorical data are commonly summarized using ________ (or proportions).
Measurement data are typically summarized using ________ (or means).
Subjective:
a) Percentages
b) Averages
c) Percentage
percentages
averages
- Which of the following is an example of qualitative dichotomic data?
a) Age of students in a class
b) Height of trees in a forest
c) Gender (Male/Female)
d) Number of cars in a parking lot - Which of the following is an example of qualitative polynomic data?
a) Temperature in degrees Celsius
b) Number of siblings a person has
c) Blood pressure reading
d) Eye color (Blue, Brown, Green) - Which of the following is an example of quantitative discrete data?
a) Time taken to complete a race in seconds
b) Number of pets in a household
c) Weight of fruits in kilograms
d) Height of students in centimeters - Which of the following is an example of quantitative continuous data?
a) Number of books in a library
b) Shoe sizes of students
c) Income levels of individuals
d) Number of goals scored in a soccer match
- Answer: c) Gender (Male/Female)
- Answer: d) Eye color (Blue, Brown, Green)
- Answer: b) Number of pets in a household
- Answer: c) Income levels of individuals
Which type of variable categorizes or describes an element of a population?
a) Nominal
b) Ordinal
c) Discrete
d) Continuous
Which type of variable involves an ordering or relative ranking of measurements?
a) Nominal
b) Ordinal
c) Discrete
d) Continuous
Which type of variable can assume a countable number of values with a gap between any two values?
a) Nominal
b) Ordinal
c) Discrete
d) Continuous
Which type of variable can assume an uncountable number of values?
a) Nominal
b) Ordinal
c) Discrete
d) Continuous
Nominal variables categorize or describe an element of a population based on ________ rather than by numerical measurement.
Ordinal variables involve an ordering or ________ ranking of measurements.
Discrete variables are quantitative variables that can assume a countable number of values, with a ________ between any two values.
Continuous variables are quantitative variables that can assume an ________ number of values
a) Nominal
b) Ordinal
c) Discrete
d) Continuous
Fill in the blanks:
qualities
relative
gap
uncountable
Explain the difference between nominal and ordinal variables, providing examples for each.
Give an example of a discrete variable and explain why there is a gap between any two values.
Provide an example of a continuous variable and explain why it can assume an uncountable number of values
Nominal variables categorize or describe elements based on qualities or characteristics, without any specific order or ranking. Examples include flower color (red, blue, yellow) and gender (male, female).
Ordinal variables involve an ordering or ranking of measurements, indicating the relative position or preference. Examples include academic positions (assistant professor, associate professor, full professor) and Likert scale (strongly agree, agree, neutral, disagree, strongly disagree).
Example: Number of children in a family. There is a gap between any two values (e.g., 2 and 3) because you cannot have a fractional or intermediate value for the number of children.
Example: Weight in kilograms. Weight can take an uncountable number of values (e.g., 4.3 kg, 1.9 kg) because there are infinite possible weight measurements within a given range.
Categorical data are commonly summarized using:
a) Percentages
b) Averages
c) Medians
d) Standard deviations
Measurement data are typically summarized using:
a) Percentages
b) Averages
c) Medians
d) Standard deviations
Which summary measure is commonly used for categorical data?
a) Mean
b) Median
c) Percentage
d) Standard deviation
Categorical data are commonly summarized using ________
Measurement data are typically summarized using ________
a) Percentages
b) Averages
c) Percentage
Fill in the blanks:
percentages
averages
Explain why categorical data is commonly summarized using percentages (or proportions) and provide an example.
Describe why measurement data is typically summarized using averages (or means) and provide an example.
Categorical data is summarized using percentages (or proportions) because it helps express the relative frequency or distribution of different categories in the data. For example, if we have data on the hair length of students, we can summarize it by saying, “11% of students have long hair.”
Measurement data is typically summarized using averages (or means) because it provides a central tendency or average value of the measurements. It helps understand the typical or representative value of the data. For example, if we have data on the ages of students, we can summarize it by saying, “The average age of SD students in 2020 is 24.2 years.”
What does the term “population” refer to in a study?
a) The sample size
b) The characteristics of the objects
c) The entire collection of objects of interest
d) The data collection process
What can be included as objects in a population?
a) Only people
b) Only animals
c) Only plants
d) People, animals, and more
What is the purpose of data collection in relation to the population?
a) To determine the sample size
b) To analyze the characteristics of the objects
c) To estimate the population size
d) To identify the research question
A population is an entire collection of ________ in which a study is interested.
The population size can vary from very ________ to very ________.
………………….. involves certain characteristics of the objects, such as weight in Kg
A study that involves the population is called a
……………….
c) The entire collection of objects of interest
d) People, animals, and more
b) To analyze the characteristics of the objects
Fill in the blanks:
objects
large, small
data collection
census
Explain the concept of a census and how it relates to studying a population.
Provide an example of a study where the population size is large, and explain why a census might be appropriate in that case
A census is a study that involves examining and gathering information from the entire population of objects of interest. It aims to collect data on all members of the population rather than a sample. A census provides a comprehensive understanding of the population’s characteristics, allowing researchers to draw accurate conclusions.
Example: A study on the voting preferences of a country’s citizens. The population size in this case would be very large, potentially in the millions or billions. Conducting a census would be appropriate because it would involve collecting data from every eligible voter to determine their voting preferences accurately. A census ensures that no subgroups within the population are overlooked, providing a complete picture of the entire population’s voting behavior.
What are summaries of population data called?
a) Parameters
b) Variables
c) Samples
d) Statistics
How many values are there for each population parameter?
a) One
b) Multiple
c) Depends on the population size
d) Depends on the sample size
What symbol is used to represent the population size?
a) N
b) µ (mu)
c) σ2
d) σ
Fill in the blanks:
Summaries of population data are called ________.
There is ________ value(s) for each population parameter.
The population size is denoted by ________.
The population mean is denoted by ________.
The population variance is denoted by ________.
Multiple-choice:
a) Parameters
a) One
a) N
Fill in the blanks:
Parameters
One
N
µ (mu)
σ2