Get our Updates on BIOSTATISTICS in your E-mail Inboxĭon’t forget to Activate your Subscription…. You might also Biostatistics Lecture Biostatistics Type I and Type II Errors in Graphical Representation of Data: Part – 1 | Part – 2 | Part – 3 | We do not know whether an error has been committed or not.
BASIC STATISTICAL CALCULATIONS PC
( Type I and Type II Errors in Statistics) The calculation of the mean and the standard deviation can easily be done on a calculator but most conveniently on a PC with computer programs such as dBASE, Lotus 123, Quattro-Pro, Excel, and others, which have simple ready-to-use functions.(Warning: some programs use n rather than n-1). Some errors are possible in the statistical decisions. Statistical results are not always beyond doubt. Statistics does not depict the entire story or the phenomenon. There are too many methods to study a single problem in statistics Only a person expert in statistics can handle the statistical tools efficiently. If sufficient care is not exercised in collecting, analyzing and interpreting the data, the statistical results might be misleading.
Statistical method cannot be applied to highly heterogeneous data. Statistical methods are best applicable on quantitative data. Statistics cannot be applied to single / individual data. Statistics helps to implement policies by the government For monitoring the community and public health In numerical taxonomy (taxonomy with numbers) Mean / Median /Mode/ Variance /Standard Deviation are all very basic but very important concept of statistics used in data science. Ø Studying the behavior of genes in a population ( Population Genetics) Ø For the studying the genetic structure of a population Ø Essential for the study of Mendelian genetics Ø Study the inheritance patterns of genes Ø For conduction of drug treatment trials Ø To find out the possible side effects of drugs Ø Deriving single values from a group of variables Ø Deriving logical conclusions from the data Ø Selecting the method of collection of data Ø Every result (data) in the research need to be statistically validated. Ø Research is incomplete without the statistics Few applications of biostatistics are summarized below. Interpretation of data Importance of Statistics in Biological Scienceīiostatistics has applications in all the branches of life sciences. The statistical analysis or statistical method.Ī biostatistical investigation is carried out through the following sequential steps. The design of experiments for getting or collecting the data. The biostatistics is conventionally divided into two aspects: Ø Example: Index numbers, statistical quality control, vital statistics etc. Ø It Includes very complex calculations, analysis and comparisons. Ø Inferential statistics is the application of statistical theories to analyze the research problems. Ø Inductive statistics is the use of statistical tools to generate conclusions on the basis of random observations. Ø Analytical statistics helps to establish functional relationship between variables (data). Ø Analytical statistics deals with all tools in the statistics used to compare different variables. Ø They reduce the complexities of the data into simple and logical summaries. Ø The descriptive statistics explains the characteristics of the data. Ø Example: Measure of central tendency (mean, median, mode), Measure of dispersion (range, standard deviation, mean deviation) etc. Standard deviation, depending on whether the data set represents theĮntire population or a sample of the population.Ø These are the statistical tools and analysis which describe and summarize the main features of the data. Python includes two sets of functions for computing variance and strip ()) data = list ( get_line_lengths ()) lengths = for d in data ] sample = lengths print ( 'Basic statistics:' ) print ( ' count : '. strip ()) if not nlines : continue # skip empty files yield ( nlines, parts.
lower () = 'total' : break nlines = int ( parts. From statistics import * import subprocess def get_line_lengths (): cmd = 'wc -l. If you need to develop complex statistical or engineering analyses, you can save steps and time by using the Analysis ToolPak.
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