Distribution
Definition
In statistics, distribution refers to the way data is spread out or organized. It describes how frequently each value occurs and provides insights into patterns and characteristics of the dataset.
Analogy
Think about distributing party invitations. The distribution of invitations can vary, with some people receiving more and others receiving fewer. Similarly, in statistics, the distribution of data points can be uneven or follow specific patterns.
Related terms
Normal Distribution: A normal distribution is a specific type of distribution where the data forms a symmetric bell-shaped curve around the mean.
Skewed Distribution: A skewed distribution is a type of distribution where the data is not evenly distributed around the mean and has a longer tail on one side.
Uniform Distribution: A uniform distribution refers to a type of distribution where all values have equal probability and are evenly spread out across the range.
"Distribution" appears in:
Additional resources (1)
Practice Questions (6)
Suppose we conduct a chi-square goodness of fit test to examine the distribution of eye colors in a population. We collect data from a random sample of 500 individuals and obtain the following observed frequencies: 100 with brown eyes, 200 with blue eyes, 120 with green eyes, and 80 with hazel eyes. The expected proportions are 40%, 15%, 20%, and 25%, respectively. To calculate the test statistic for this scenario, what would be the next step?
What is the distribution of the slope estimate when the assumptions of a linear regression model are satisfied and the null hypothesis is true?
A researcher wants to investigate if the distribution of car colors in a city is different from the expected distribution. They collect the color values of a random sample of 500 cars. Which of the following procedures should be used?
A researcher wants to investigate if the distribution of the level of education (high school, college, graduate) within different income level groups (low, medium, high) are the same. She collects data from a random sample of 500 individuals and creates a contingency table. Which of the following procedures is most appropriate for her to use?
In constructing a confidence interval for a population mean when the population standard deviation is unknown and the sample size is small, which distribution should be used?
If the graph of a discrete random variable is roughly symmetric, what does it suggest about the distribution?
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