# Basic statistics

Before starting any statistical data analysis, we need to explore data more and more.

Some researchers use quantitative methods to exclude outliers. Unfortunately, the two most precise methods are not easy to use and require a good deal of "experimentation" with the data. Our Over and Under Sampling can combat that. The following data set can be analyzed with a t-test comparing the average WCC score in males and females. Because of the way in which the regression line is determined especially the fact that it is based on minimizing not the sum of simple distances but the sum of squares of distances of data points from the line , outliers have a profound influence on the slope of the regression line and consequently on the value of the correlation coefficient. The graph allows you to evaluate the normality of the empirical distribution because it also shows the normal curve superimposed over the histogram. You will not only learn about all these statistical concepts, you will also be trained to calculate and generate these statistics yourself using freely available statistical software. See also Confidence Ellipse. As an Amazon Associate I earn from qualifying purchases. An important aspect of the "description" of a variable is the shape of its distribution, which tells you the frequency of values from different ranges of the variable.

There is a third variable the initial size of the fire that influences both the amount of losses and the number of firemen. Basically, if the median line is not in the middle of the box then it is an indication of skewed data.

### Basic statistics

The mean value shifts the distribution spatially and the standard deviation controls the spread. With dimensionality reduction we would then project the 3D data onto a 2D plane. Because of the way in which the regression line is determined especially the fact that it is based on minimizing not the sum of simple distances but the sum of squares of distances of data points from the line , outliers have a profound influence on the slope of the regression line and consequently on the value of the correlation coefficient. The t-test is the most commonly used method to evaluate the differences in means between two groups. If these conditions are not met, then you can evaluate the differences in means between two groups using one of the nonparametric alternatives to the t- test see Nonparametrics and Distribution Fitting. Because it substitutes missing data with artificially created "average" data points, mean substitution may considerably change the values of correlations. Significance of Correlations. It also allows you to examine various aspects of the distribution qualitatively. Outliers are atypical by definition , infrequent observations. If you "control" for this variable e. Only this way will you get a "true" correlation matrix, where all correlations are obtained from the same set of observations. The second part of the course is concerned with the basics of probability: calculating probabilities, probability distributions and sampling distributions. It involves applying math to analyze the probability of some event occurring, where specifically the only data we compute on is prior data. A basic box plot The line in the middle is the median value of the data.

Technically speaking, this is the probability of error associated with rejecting the hypothesis of no difference between the two categories of observations corresponding to the groups in the population when, in fact, the hypothesis is true. Proportional means linearly related; that is, the correlation is high if it can be "summarized" by a straight line sloped upwards or downwards.

We can quickly see and interpret our categorical variables with a Uniform Distribution. However, if missing data are randomly distributed across cases, you could easily end up with no "valid" cases in the data set, because each of them will have at least one missing data in some variable.

Recommended Reading Want to learn more about Data Science? We have a dataset and we would like to reduce the number of dimensions it has. Some researchers use quantitative methods to exclude outliers. For example, a typical transformation used in such cases is the logarithmic function which will "squeeze" together the values at one end of the range. Alternatively, you could experiment with dividing one of the variables into a number of segments e. In such cases, in order to understand the nature of the variable in question, you should look for a way to quantitatively identify the two sub-samples. Especially data from more diverse sources helps to do this job easier way.

This will helps you to understand you ca determine the limitations of the generalizability of results and conduct a proper analysis.

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