These descriptive statistics help us to identify the center and spread of the data. In this section, we will examine descriptive statistics in terms of measures of center and measures of dispersion. Sample statistics are used to estimate unknown population parameters. The sample variance is s 2 and the sample standard deviation is s. The population variance is σ 2 (sigma squared) and population standard deviation is σ (sigma).ĭescriptive measures of samples are called statistics and are typically written using Roman letters. Is the variable qualitative or quantitative?ĭescriptive measures of populations are called parameters and are typically written using Greek letters. Think of continuous variables as “cows.” Cows can give 4.6713245 gallons of milk, or 7.0918754 gallons of milk, or 13.272698 gallons of milk … There are an almost infinite number of values that a continuous variable could take on. Think of discrete variables as “hens.” Hens can lay 1 egg, or 2 eggs, or 13 eggs… There are a limited, definable number of values that the variable could take on.Ĭontinuous variables have an infinite number of possible values. Discrete variables have a finite or countable number of possible values. Quantitative variables can be broken down further into two more categories: discrete and continuous variables.

Examples of quantitative variables are age, height, and length. Mathematical operations can be applied to these data. Quantitative variables have values that are typically numeric, such as measurements. Examples of qualitative variables are gender, race, and petal color. Mathematical operations cannot be applied to qualitative variables. Qualitative variables have values that are attributes or categories. Variables are divided into two major groups: qualitative and quantitative.

The weight of grizzly bears in Yellowstone National Park.

The pH of lakes in the Adirondack Park.Variables are the characteristics we are interested in. The population variance (σ 2) is estimated by the sample variance (s 2). For example, the population mean (µ) is estimated by the sample mean ( x̄). Inferences about parameters are based on sample statistics. Populations are characterized by descriptive measures called parameters. 60 grizzly bears with a home range in Yellowstone National Park.25 lakes randomly selected from the Adirondack Park.100 fish randomly sampled from Long Lake.All the grizzly bears in Yellowstone National Park.Ī sample is a subset of data drawn from the population of interest.Section 1 Descriptive StatisticsĪ population is the group to be studied, and population data is a collection of all elements in the population. Using sample statistics to estimate population parameters. We use sample statistics to estimate population parameters (the truth). Good statistics come from good samples, and are used to draw conclusions or answer questions about a population. Statistics is the science of collecting, organizing, summarizing, analyzing, and interpreting information. But before we begin exploring different types of statistical methods, a brief review of descriptive statistics is needed. The next step is inferential statistics, which allows us to use our sample statistics and extend the results to the population, while measuring the reliability of the result. We then use descriptive statistics to organize and summarize our sample data. In order to answer these questions, a good random sample must be collected from the population of interests. These are typical questions that require statistical analysis for the answers. Did fertilizer A, B, or C have an effect on the corn yield?.What proportion of white tail deer in New Hampshire have weights below the limit considered healthy?.Has there been an increase in the number of invasive species found in the Great Lakes?.Has there been a significant change in the mean sawtimber volume in the red pine stands?.As scientists, researchers, and managers working in the natural resources sector, we all rely on statistical analysis to help us answer the questions that arise in the populations we manage. Statistics has become the universal language of the sciences, and data analysis can lead to powerful results.

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