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What else can I help you with?
What variable is the data being collected?
Controlled
Which of the following is not a variable data type real integer number or string?
FILE, struct stat and struct tm are some examples.
Which data type can store any data?
There is no such data type. However, when we use user-defined data types of our own type, then that type of data can be stored in a variable. So as a term, you may say that user-defined data type can store any data. As the data-type used in any variable will be depending upon us.
What specifies a variable's name and data type?
Most programming languages require that you declare all of the variables that you intend to use in a program. A variable declaration is a statement that typically specifies two things about a variable:* The variable's name* The variable's data typePosted by Special:Contributions***EDIT***This can be found on page 56 in "Programming Logic and Design" by Tony Gladdis
What is a valid variable data type?
1. If its natural or integer numbers- Integer(Int) data type. 2. If it consists of decimal or fraction part- Double or float data type. 3. If it has a single letter or sign- Character(Char) data type. 4. If its got many words(alpha-numerical)- String data type. 5. If the result has to be "true" or "false"- Boolean data type.
What is the difference between parametric and nonparametric statistical tests in Health care?
Parametric tests draw conclusions based on the data that are drawn from populations that have certain distributions. Non-parametric tests draw fewer conclusions about the data set. The majority of elementary statistical methods are parametric because they generally have larger statistical outcomes. However, if the necessary conclusions cannot be drawn about a data set, non-parametric tests are then used.
Are Non-parametric tests more powerful than parametric tests?
Non-parametric tests are not inherently more powerful than parametric tests; their effectiveness depends on the data characteristics and the underlying assumptions. Parametric tests, which assume a specific distribution (typically normality), tend to be more powerful when these assumptions are met, as they utilize more information from the data. However, non-parametric tests are advantageous when these assumptions are violated, as they do not rely on distributional assumptions and can be used for ordinal data or when sample sizes are small. In summary, the power of each type of test depends on the context and the data being analyzed.
How do you know whether data requires you to use a parametric or non parametric test?
Parametric tests assume that your data are normally distributed (i.e. follow a classic bell-shaped "Gaussian" curve). Non-parametric tests make no assumption about the shape of the distribution.
What serves as a standard of comparisons to evaluate the effect of the independent variable on the dependent variable variable?
There cannot be one since the answer depends on the form in which the effect is measured: whether the effect is qualitative or quantitative. There are various non-parametric measures of correlation or concordance. For data that are more quantitative there are more powerful tests such as the F-test for independent Normal distributions.
What is the difference between parametric and non parametric?
Nonparametric tests are sometimes called distribution free statistics because they do not require that the data fit a normal distribution. Nonparametric tests require less restrictive assumptions about the data than parametric restrictions. We can perform the analysis of categorical and rank data using nonparametric tests.
What are examples of parametric and nonparametric statistical tests?
Parametric statistical tests assume that your data are normally distributed (follow a classic bell-shaped curve). An example of a parametric statistical test is the Student's t-test.Non-parametric tests make no such assumption. An example of a non-parametric statistical test is the Sign Test.
What serves as a comparison to evaluate the effect of the the independent variable on the dependent variable?
There cannot be one since the answer depends on the form in which the effect is measured: whether the effect is qualitative or quantitative. There are various non-parametric measures of correlation or concordance. For data that are more quantitative there are more powerful tests such as the F-test for independent Normal distributions.
What as a standard of comparison to evaluate the effect of the independent variable on the dependent variable?
There cannot be one since the answer depends on the form in which the effect is measured: whether the effect is qualitative or quantitative. There are various non-parametric measures of correlation or concordance. For data that are more quantitative there are more powerful tests such as the F-test for independent Normal distributions.
What serves as standard of comparison to evaluate the effect of the the independent variable on the dependent variable?
There cannot be one since the answer depends on the form in which the effect is measured: whether the effect is qualitative or quantitative. There are various non-parametric measures of correlation or concordance. For data that are more quantitative there are more powerful tests such as the F-test for independent Normal distributions.
What serves as a standard of comparison to evaluate the effect of the independent variable on the the dependent variable?
There cannot be one since the answer depends on the form in which the effect is measured: whether the effect is qualitative or quantitative. There are various non-parametric measures of correlation or concordance. For data that are more quantitative there are more powerful tests such as the F-test for independent Normal distributions.
What serves as a standard of comparison to evaluate the effect to the independent variable of the dependent variable?
There cannot be one since the answer depends on the form in which the effect is measured: whether the effect is qualitative or quantitative. There are various non-parametric measures of correlation or concordance. For data that are more quantitative there are more powerful tests such as the F-test for independent Normal distributions.
What serves as a standard of comparison to evaluate the effect of the independents variable on the dependent variable?
There cannot be one since the answer depends on the form in which the effect is measured: whether the effect is qualitative or quantitative. There are various non-parametric measures of correlation or concordance. For data that are more quantitative there are more powerful tests such as the F-test for independent Normal distributions.
