DESCRIPTIVE STATISTICS.pptx Biostatistics
- 1. DESCRIPTIVE STATISTICS 1. Summarizing, Organizing, and Presenting data meaningfully and concisely. 2. It involves a graphical representation of data through charts, graphs, and tables, which can further aid in visualizing and interpreting the information. 3. This includes histograms, bar charts, pie charts, scatter plots, and box plots. QUALITATIVE (Categorical) QUANTITATIVE (Numerical) NOMINAL ORDINAL CONTINUOUS DISCRETE
- 2. MEASURES OF CENTRAL TENDENCY A single value that attempts to describe a set of data by identifying the central position within that set of data. Measures of central tendency are sometimes called measures of central location. MEAN Total of all the values divided by the size of the data set. Works well when the distribution is symmetric and have no outliers. MEDIAN The middle value where exactly half of the data values are above and below it. Can reduce the effect of outliers. Often used when the data is nonsymmetrical MODE Value that occurs the most often in the data set. More useful to distinguish between unimodal & multimodal distribution.
- 3. MEASURES OF DISPERSION The measures of dispersion help to interpret the variability of data i.e. to how much homogenous or heterogeneous the data is. In simple terms, it shows how squeezed or scattered the variable is. RANGE The difference between the largest & smallest value in the distribution. Standard Deviation The square root of the arithmetic mean of the square of the deviations measured from the mean. Interquartile Range Difference between upper & lower quartile i.e. Q3 - Q1 Variance The average of the square deviation from the mean of the given data set. Mean Deviation The arithmetic mean of the difference between the values and their mean.
- 4. OTHER MEASURES There are some other measures used in statistics to characterize the location and variability of a data set. SKEWNESS It assesses the asymmetry of a probability distribution. It quantifies the extent to which the data is skewed or shifted to one side. +ve skewness indicates a longer tail on the right side of the distribution, while -ve skewness indicates a longer tail on the left side. KURTOSIS A statistical measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution. It is used to find the presence of outliers in our data & gives us the total degree of outliers present.
- 5. References: 1. www.itl.nist.gov 2. www.scribbr.com 3. www.geeksforgeeks.org 4. www.investopedia.com 5. www.analyticsvidhya.com SUBMITTED BY : NANDINI S NAIR MSc Clinical Research 1st Year