statistics

statistics

1. importance

   • it allows us to assess the validity of claims and conclusions based on data. We may use statistical techniques to identify biases, analyze the quality of the samples, and assess the reliability of the results.

2. variable types

   • qualitative variables
     • Nominal qualitative variable
       • A nominal qualitative variable presents non-numerical modalities that do not admit an order criterion.
     • Ordinal qualitative variable
       • An ordinal qualitative variable presents non-numerical modalities, in which there is an order.
     • Qualitative variables refer to characteristics or qualities that cannot be measured with numbers.
   • Quantitative variables
     • discrete variable
       • A discrete variable is one that can only take on a finite number of values ​​between any two values ​​of a characteristic.
     • Continuous variable
       • A continuous variable is one that can take on an infinite number of values ​​between any two values ​​of a characteristic.
     • A quantitative variable is one that is expressed by a number, therefore arithmetic operations can be performed with it

3. measures:

   • Measures of dispersion While measures of central tendency show us how much the rest of the data tends to concentrate, it is also important to identify how spread out the data is from its measure of central tendency. And it is that in many situations the average will not necessarily be a faithful representative of the rest of the data. Dispersion measures, therefore, indicate how much the observations deviate from their mean (Allen, 2001).
   • Position or location measurements The measures that we call quantiles are values ​​of the variable that divide the distribution into proportional parts, that is, into intervals that contain the same number of observations" (Sánchez & Peñas, 2002)
   • Measures of central tendency Numbers just like people enjoy a set of activities or company, and are prone to gather around a central point called a measure of central tendency. Measures of central tendency "locate and identify the point around which the data is centered" (Allen, 2001). The most commonly used measures of central tendency to locate the center of the data are the mean, median, and mode; when the value obtained in these three measures is not different from one another, it can be ensured that one is working with data that follow a normal distribution.
   • Measurements of the shape of the frequency distribution curve Among the measures that can be classified as shape are asymmetry or bias and kurtosis. The bias denotes the horizontal deformation of the frequency distribution curve, in this way there can be three types of biases or asymmetries for a data set A bias greater than zero will denote that the frequency distribution curve has a longer tail to the right (positive skewness). A bias equal to zero will denote a symmetric frequency distribution curve without any kind of deformation neither to the left nor to the right (symmetric.) A bias less than zero will denote a frequency distribution curve with a longer tail to the left (negative skewness). Abrir en Google Traductor • Comentarios Traductor de Google Google https://translate.google.com › ... El servicio de Google, que se ofrece sin coste económico, traduce al instante palabras, frases y páginas web a más de 100 idiomas. Elige un archivo El servicio de Google, que se ofrece si