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Correlation, its types- (Positive Correlation, Perfect Positive Correlation, Negative correlation, Perfect negative correlation, Zero correlation ) and Causation

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  In statistics, we study the movement or variability in variables and attributes. The effect of their past values on present and future. Also, sometimes the effect of other variables or attributes on them. Suppose we want to study the future price of car. Then, just studying the past trend values might not be enough. There can be other factors affecting the price, like price of fuel, raw materials etc. So, in such cases, we study the relationship between/among variables. Data are basically information collected in various forms (numbers, texts, audio, video etc…) about various individuals or objects. These can be both quantitative and qualitative type. Quantitative data are measurable numerically while qualitative data can’t be measured numerically. Quantitative data are called variables and qualitative data are called attributes. Height is an example of variables and beauty is an example of attributes. The study of relationship between/ among them is called association. The study of
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Moments-Central and Raw Moments- Mean, kurtosis, Variance and Skewness

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  Data are information collected from various sources in various forms. These data are analyzed to get meaningful conclusion and insights about the dataset. A single datum is hardly useful. We usually require a dataset consisting a series of datapoints to carry out any analysis or get to a conclusion. This series of datapoint traces the path on which the variable under consideration moves. This traced path can be very useful to understand the pattern of the data and also predicts its future movement. To understand the pattern, we need to consider certain characteristics of the path. The points when plotted on graph paper, the traced path takes the form of a curve. And the characteristics of the path such as mean, variance, kurtosis and skewness can be found out using moments. These are two types of moments- raw and central. Central moment measures the deviation of datapoints from mean. Whereas, raw moment measures the deviation of datapoints from 0.   The 1 st order raw moment is mean
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Measures of Dispersion and its Types- Absolute and Relative

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Dispersion is the act of dispersing from one location. One may say that people left the arrival place in dispersion. Similar to this, dispersion in statistics refers to the distribution of observations away from a central location. The mean, median, and mode are three different metrics of central tendency, as we have seen in earlier blogs. However, not all values are equal to the central value; instead, most of them fall within its range, with some values being far from it. Furthermore, we employ a variety of dispersion techniques to determine the extent of the spread. The average's precision decreases with increasing spread. A single number that represents the complete collection of data is used to calculate measures of central tendency. However, that primary value won't always match up with every observation. Some might differ more than others. Measures of dispersion are used to quantify this degree of variation. Assume that ten pupils in the class took the test, and the
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