Types of variables in statistics

Oct 23, 2017Download as pptx, pdf10 likes16,799 views

This presentation introduces various types of variables commonly used in statistics. It discusses categorical variables that can be grouped into categories, continuous variables with infinite values like time or weight, and discrete variables that can only take on a certain number of values. It also covers dependent variables that are the outcome of an experiment and change based on the independent variable, control variables that must be held constant in an experiment, and confounding variables that have a hidden effect on experimental results. Finally, it defines qualitative variables that can't be counted numerically and quantitative variables that can be counted or have a numerical value.

Welcome to our presentation
on
Types of variables in statistics

What is variable?
 While a “variable” in algebra really just means one thing–an unknown
value–you’ll come across dozens of types of variables in statistics. Some
are used more than others.
 For example, you’ll be much more likely to come across continuous
variables than you would dummy variables.

Common Types of Variables
 Categorical variable: variables than can be put into categories.
For example, the category “Toothpaste Brands” might contain
the variables Colgate and Aqua fresh.
 Confounding variable: extra variables that have a hidden effect
on your experimental results.

Common Types of Variables
 Continuous variable: a variable with infinite number of values,
like “time” or “weight”.
 Control variable: a factor in an experiment which must be held
constant. For example, in an experiment to determine whether
light makes plants grow faster, you would have to control for
soil quality and water.

Common Types of Variables
 Dependent variable: the outcome of an experiment. As you
change the independent variable, you watch what happens to
the dependent variable.
 Discrete variable: a variable that can only take on a certain
number of values. For example, “number of cars in a parking
lot” is discrete because a car park can only hold so many cars.

Common Types of Variables
 Independent variable: a variable that is not affected by
anything that you, the researcher, does. Usually plotted on the
x-axis.
 A measurement variable has a number associated with it. It’s
an “amount” of something, or a number of something.

Common Types of Variables
 Nominal variable: another name for categorical variable. Ordinal
variable: similar to a categorical variable, but there is a clear
order. For example, income levels of low, middle, and high could
be considered ordinal.
 Qualitative variable: a broad category for any variable that can’t
be counted (i.e. has no numerical value). Nominal and ordinal
variables fall under this umbrella term.

Common Types of Variables
 Quantitative variable: A broad category that includes any
variable that can be counted, or has a numerical value
associated with it. Examples of variables that fall into this
category include discrete variables and ratio variables.
 Random variables are associated with random processes and
give numbers to outcomes of random events.

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What is variable? What is data type, header file? ভেরিয়েবল কি? ডেটা টাইপ, হেড...Zakaria Hossain

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Types of variables in statistics

1. Welcome to our presentation on Types of variables in statistics

2. What is variable?  While a “variable” in algebra really just means one thing–an unknown value–you’ll come across dozens of types of variables in statistics. Some are used more than others.  For example, you’ll be much more likely to come across continuous variables than you would dummy variables.

3. Common Types of Variables  Categorical variable: variables than can be put into categories. For example, the category “Toothpaste Brands” might contain the variables Colgate and Aqua fresh.  Confounding variable: extra variables that have a hidden effect on your experimental results.

4. Common Types of Variables  Continuous variable: a variable with infinite number of values, like “time” or “weight”.  Control variable: a factor in an experiment which must be held constant. For example, in an experiment to determine whether light makes plants grow faster, you would have to control for soil quality and water.

5. Common Types of Variables  Dependent variable: the outcome of an experiment. As you change the independent variable, you watch what happens to the dependent variable.  Discrete variable: a variable that can only take on a certain number of values. For example, “number of cars in a parking lot” is discrete because a car park can only hold so many cars.

6. Common Types of Variables  Independent variable: a variable that is not affected by anything that you, the researcher, does. Usually plotted on the x-axis.  A measurement variable has a number associated with it. It’s an “amount” of something, or a number of something.

7. Common Types of Variables  Nominal variable: another name for categorical variable. Ordinal variable: similar to a categorical variable, but there is a clear order. For example, income levels of low, middle, and high could be considered ordinal.  Qualitative variable: a broad category for any variable that can’t be counted (i.e. has no numerical value). Nominal and ordinal variables fall under this umbrella term.

8. Common Types of Variables  Quantitative variable: A broad category that includes any variable that can be counted, or has a numerical value associated with it. Examples of variables that fall into this category include discrete variables and ratio variables.  Random variables are associated with random processes and give numbers to outcomes of random events.

9. Any Question..

11. ZAKARIA HOSSAIN

Types of variables in statistics

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