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What is actually the difference between a dependent and an independent sample (paired or unpaired samples)? And why is it important to know the difference? Whether the data are from a dependent or independent sample determines which hypothesis test is used...
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What is an analysis of variance?
An analysis of variance (ANOVA) tests whether statistically significant differences exist between more than two groups. For this purpose, the means and variances of the respective samples are compared with each other.
There are different types of analysis of variance, the most common are the one-way and two-way analysis of variance, each of which can be calculated either with or without repeated measurements.
One-factor (or one-way) ANOVA
Two-factors (or two-way) ANOVA
One-factor ANOVA with repeated measurements
Two-factors ANOVA with repeated measurements
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The t-test is one of the most common hypothesis tests in statistics. The t-test determines either whether the sample mean and the mean of the population differ or if two sample means differ statistically. The t-test distinguishes between:
1) One sample t-test
2) Independent sample t-test
3) Paired samples t test
The choice of which t-test to use depends on whether one or two samples are available. If two samples are available, a distinction is made between dependent and independent samples. In this tutorial you will find everything about the one-sample t-test.
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Chi-Square test
The Chi-square test is a hypothesis test used to determine whether there is a relationship between two categorical variables.
What are categorical variables again? Categorical variables are, for example, gender, preferred newspaper, frequency of television viewing or a person's highest educational level. Therefore, whenever two categorical variables are to be tested to see if there is a relationship, one uses a Chi2 test.
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Descriptive statistics
After collecting data, one of the first things to do is to graph the data, calculate the mean and get an overview of the distributions of the data. This is the task of descriptive statistics.
Thus, the goal of descriptive statistics is to gain an overview of the distribution of data sets. Descriptive statistics helps to describe and illustrate data sets.
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Regression analysis:
Regression is a statistical method that allows modeling relationships between a dependent variable and one or more independent variables.
A regression analysis makes it possible to infer or predict another variable on the basis of one or more variables.
For example, you might be interested in what influences a person's salary. In order to find it out, you could take the highest level of education, the weekly working hours and the age of a person.
Further you could now investigate whether these three variables have an influence on a person's salary. If so, you can predict a person's salary by using the highest education level, the weekly working hours and the age of a person.
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Median: The median is the value that divides your data into a lower and an upper half.
If the measured values of a variable are ordered by size, the value in the middle is the median. The median is therefore the "middle value" of a distribution. It leads to a division of the series into two parts: one half is smaller and one is larger than the median.
Since for the calculation of the median the data are ordered, the variables must have ordinal or metric scale level.
If there is an odd number of characteristic values, then the median is a value that actually occurs.
If there is an even number of characteristic carriers (persons), the two middle characteristics are added together and their sum is divided by two.
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The most common measures of dispersion for metric variables is the standard deviation.
The standard deviation indicates the spread of a variable around its mean value. Thus, the standard deviation is the average distance of all measured values of a variable from the mean value of the distribution.
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Independent t-Test
The independent t-Test is a statistical test that determines whether there is a difference between two unrelated samples.
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DATAtab - Online Statistics Calculator and e-Learning
DATAtab offers you smart and easy support for your data analysis. It is designed for ease of use and is a compelling free alternative to statistical programs such as SPSS and STATA. On datatab.net data can be statistically evaluated directly online and very easily (e.g. t-test, regression, correlation etc.). DATAtab's wants to make the world of statistical data analysis as simple as possible - without installation and easy to use. Just insert your data into the statistics calculator and select which analyses you want to perform. Create your charts for your data directly online and uncomplicated.
On this youtube channel DATAtab offers more information in form of tutorials and "how to use"-videos. Feel free to subscribe, like and discuss!