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What does the normal probability density function describe?
A probability density function (pdf) for a continuous random variable (RV), is a function that describes the probability that the RV random variable will fall within a range of values. The probability of the RV falling between two values is the integral of the relevant PDF. The normal or Gaussian distribution is one of the most common distributions in probability theory. Whatever the underlying distribution of a RV, the average of a set of independent observations for that RV will by approximately Gaussian.
What is the difference between interpolation and extrapolation?
Both, interpolation and extrapolation are used to predict, or estimate, the value of one variable when the value (or values) of other variable (or variables) is known. This is done by extending evaluating the underlying function. For interpolation, the point in question is within the domain of the observed values (there are observations for greater and for smaller values of the variables) wheres for extrapolation the point in question is outside the domain.
When can the standard error of the estimate be used to construct a prediction interval about a value y'?
You need the data to be homoscedastic, the errors to be independent. The independent variable(s) should lie within (or very close to) the range of observed values.
What does variable held constant mean?
The distinction between these two types of variables is whether the variable regress on another variable or not. Like in a linear regression the dependent variable (DV) regresses on the independent variable (IV), meaning that the DV is being predicted by the IV. Within SEM modelling this means that the exogenous variable is the variable that another variable regresses on. Exogenous variables can be recognized in a graphical version of the model, as the variables sending out arrowheads, denoting which variable it is predicting. A variable that regresses on a variable is always an endogenous variable even if this same variable is used as an variable to be regressed on.
What is the difference between a simple random sample and a stratified random sample?
Sometimes a population consists of a number of subsets (strata) such that members within any particular strata are alike while difference between strata are more than simply random variations. In such a case, the population can be split up into strata. Then a stratified random sample consists of simple random samples, with the same sampling proportion, taken within each stratum.
What is probability distibution?
A statistical function that describes all the possible values and likelihoods that a random variable can take within a given range.
What is a discrete variable?
A discrete variable is one that cannot take on all values within the limits of the variable.
How are dependent variables and independent variable the related?
Independent variables can take values within a given boundary. The dependent variable will take values based on the independent variable and a given relationship at which the former can take its values.
How are the independent and dependent variables related?
Independent variables can take values within a given boundary. The dependent variable will take values based on the independent variable and a given relationship at which the former can take its values.
What does the normal probability density function describe?
A probability density function (pdf) for a continuous random variable (RV), is a function that describes the probability that the RV random variable will fall within a range of values. The probability of the RV falling between two values is the integral of the relevant PDF. The normal or Gaussian distribution is one of the most common distributions in probability theory. Whatever the underlying distribution of a RV, the average of a set of independent observations for that RV will by approximately Gaussian.
Define numeric data?
Numeric data refers to any data that is represented as numerical values, such as integers, decimals, or fractions. This type of data is used for quantitative analysis and calculations in various fields such as mathematics, statistics, and science. Numeric data can be manipulated and processed mathematically to uncover patterns, trends, and relationships within the data.
How are independent and dependent variable related?
Independent variables can take values within a given boundary. The dependent variable will take values based on the independent variable and a given relationship at which the former can take its values.
What kind of variable is temperature change?
Temperature change is a continuous and interval variable, meaning it can take any real value within a certain range and the differences between values are consistent.
Is mating within a population random?
Yes, mating within a population is random. However, it is possible for non random mating to occur within a population.
What do error bar show within?
The centre of the error bar shows the point estimate for a variable and the bits that stick out are the likely minimum and maximum values.
What is field property in database?
In a database, a field property defines the characteristics of a specific field within a table. These properties can include data type (such as text or numeric), length constraints, default values, and whether the field is required or allows null values. Field properties help ensure data integrity and consistency within the database.
How do you find the range of distribution?
The largest value minus the smallest value. In statistics, a distribution is the set of all possible values for terms that represent defined events. There are two major types of statistical distributions. The first type has a discrete random variable. This means that every term has a precise, isolated numerical value. An example of a distribution with a discrete random variable is the set of results for a test taken by a class in school. The second major type of distribution has a continuous random variable. In this situation, a term can acquire any value within an unbroken interval or span. Such a distribution is called a probability density function. This is the sort of function that might, for example, be used by a computer in an attempt to forecast the path of a weather system.
