Statistics is a branch of applied mathematics which deals with the collection, classification, analysis and interpretation of data. Biostatistics is a branch of biological science which deals with the study and methods of collection, presentation, analysis and interpretation of data of biological research.
Biostatistics is also called as biometrics since it involves many measurements and calculations. In biostatistics, the statistical methods are applied to solve biological problems. Basic understanding of biostatistics is necessary for the study of biology particularly doing research in biological science. The statistics will help the biologist to: 1 understand the nature of variability and 2 helps in deriving general laws from small samples. Sir Galton for the first time used statistical tools to study differences among human population.
He also invented the use of questionnaires and surveys for collecting data on human communities. Statistics is classified into two categories.
Pure Statistics. Applied Statistics. Pure statistics is the basic statistics. The pure statistics is further classified into FOUR sub-categories. Descriptive statistics. Analytical statistics.
Inductive statistics. Inferential statistics. Descriptive Statistics. Analytical Statistics. Classification of Biostatistics.
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The biostatistics is conventionally divided into two aspects:. The design of experiments for getting or collecting the data. The statistical analysis or statistical method. Steps in biostatistics:. A biostatistical investigation is carried out through the following sequential steps. Collection of data variable. Classification of the collected data. Analysis of data. Interpretation of data.
MA121: Introduction to Statistics
Biostatistics has applications in all the branches of life sciences. Few applications of biostatistics are summarized below. Medical and Pharmaceutical Science:. In numerical taxonomy taxonomy with numbers.
For monitoring the community and public health. Demography study of human population.After you enable Flash, refresh this page and the presentation should play. Get the plugin now. Toggle navigation.
Help Preferences Sign up Log in. To view this presentation, you'll need to allow Flash. Click to allow Flash After you enable Flash, refresh this page and the presentation should play. View by Category Toggle navigation. Products Sold on our sister site CrystalGraphics. Description: Inferential Biostatistics: Methods of making generalizations about a larger group based on information about a subset sample Tags: biostatistics introduction biostatistics.
Latest Highest Rated. In15 articles on statistical methods by Austin Bradford Hill, were published in book form. Ina RCT of Streptomycin for pulmonary tb.
Then the growth of Statistics in Medicine from was a 8-fold increase by Errors in measurement or assessment or errors in knowledge Incomplete knowledge 16 Intrinsic variation as a source of medical uncertainties Biological due to age, gender, heredity, parity, height, weight, etc.
Also due to variation in anatomical, physiological and biochemical parameters Environmental due to nutrition, smoking, pollution, facilities of water and sanitation, road traffic, legislation, stress and strains etc.
Errors in methods such as in using incorrect quantity or quality of chemicals and reagents, misinterpretation of ECG, using inappropriate diagnostic tools, misrecording of information etc.
Instrument error due to use of non-standardized or faulty instrument and improper use of a right instrument. Not collecting full information Inconsistent response by the patients or other subjects under evaluation 19 Incomplete knowledge as a source of Uncertainties Diagnostic, therapeutic and prognostic uncertainties due to lack of knowledge Predictive uncertainties such as in survival duration of a patient of cancer Other uncertainties such as how to measure positive health 20 Biostatistics is the science that helps in managing medical uncertainties 21 Reasons to know about biostatistics Medicine is becoming increasingly quantitative.
The planning, conduct and interpretation of much of medical research are becoming increasingly reliant on the statistical methodology. Statistics pass through the medical literature. Planning and conduct of clinical studies. Evaluating the merits of different procedures. In providing methods for definition of normal and abnormal.
To find out the basic factors underlying the ill-health. To introduce and promote health legislation. In proper evaluation of the achievements and failures of a health programme 26 Role of Biostatistics in Medical Research In developing a research design that can minimize the impact of uncertainties In assessing reliability and validity of tools and instruments to collect the infromation In proper analysis of data 27 Example Evaluation of Penicillin treatment A vs Penicillin Chloramphenicol treatment B for treating bacterial pneumonia in childrenlt 2 yrs.
What is the sample size needed to demonstrate the significance of one group against other? Is treatment A is better than treatment B or vice versa? If so, how much better? What is the normal variation in clinical measurement? How reliable and valid is the measurement?It will save my voice instead of my taking attendance this is only to settle the class roster. I dont usually use slides, but am going to try to use these to save my voice somewhat.
Notes: Still working on getting the class roster settled. Has been some movement on the waitlist, will keep in touch as things develop. Be sure youve signed in! First homework is posted on our course websitebut isnt due until next Friday the 4th. The additional problem is NOT optional, that just means it is not a book problem.
Save this to use tomorrow in class. There is a second handout the anonymous survey largely designed by the class on Monday. Please go ahead and take a few minutes to fill this out no names! Well take a look at this data next week in lab. If you missed class Monday, I have extra course syllabuses at the front as well. The Ws of a Data Set Who the observations population set of all objects you are interested in obtaining the value of some parameter for since we usually cant observe all objects, we take a sample of objects a subset of the overall population of objects to observe Note: There is NO such thing as a population sample or sample population.
These chapters focus on mostly univariate single variable analyses. There is one comparative graph a side-by-side boxplot in Chapter 5. Hence it gives proportions for each category out of the total. Bar Charts One bar per category height is determined by frequency or relative frequency Order of categories is arbitrary. Does NOT let you talk about the shape of a distribution. Area principle areas are supposed to be relative.
This is often violated when people try to make graphs cool and go 3-D, etc. You could look at the top three risk factors for a disease, etc. You can also look at conditional distributions. Fix a row or column and work solely within that row or column. Concept of independence will formalize later : If the distribution of one variable is the same for all categories of another variable, then the two variables are independent.
Look what happens when you breakdown by the 6 largest departments though! Is there evidence of discrimination against females at the dept. What is going on? Basics: You take your values and set a stem maybe tens. Then the leaves are the ones place. For each stem, you list the leaves that coincide in numeric order. Usually works decently for fewer than observations Try it.
Count the number of observations in each bin or pile. Plot the frequencies per bin. Usually no spaces between bins if there is, it is a gap NOT like a bar chart. You DO need to know the boundaries.
Technology lets us vary the width of bins effectively the number You can also use unequal bin widths but then you need something called density, not frequency. Examples See page 2 of the handout Try to describe the shape of each histogram.Note : These notes and accompanying spreadsheets are preliminary and incomplete and they are not guaranteed to be free of errors.
Check the revision dates for updates. Please let me know if you find typos or other errors. Comments and suggestions are welcome. Book chapter: a sset return calculations. Revised June 2420 Class slides: a sset return calculations. Revised June 24, Revised October 24, Book chapter: r eview of random variables and probability distributions. Revised January 16, Revised January 12, Class slides: r eview of univariate random variables and probability distributions.
Revised January 12 Class slides: review of bivariate distributions, and linear combinations of random variables. Revised July 7, Revised July 3, Revised June 26, Book chapter reviewing some basic results from matrix algebra. Revised August 15, 20 Class slides on review of matrix algebra. Revised July 11, Powerpoint examples.
R script file used for Powerpoint examples. Revised July 11, 20 Book chapter on basic time series concepts. Class slides: review of basic time series concepts stationarity, MA and AR models. Revised: July 8, Revised July 8, Book chapter on descriptive statistics for financial time series. Class slides.Time: 93 hours College Credit Recommended Free Certificate If you invest in financial markets, you may want to predict the price of a stock in six months from now on the basis of company performance measures and other economic factors.
As a college student, you may be interested in knowing the dependence of the mean starting salary of a college graduate, based on your GPA. These are just some examples that highlight how statistics are used in our modern society. To figure out the desired information for each example, you need data to analyze.
The purpose of this course is to introduce you to the subject of statistics as a science of data. There is data abound in this information age; how to extract useful knowledge and gain a sound understanding in complex data sets has been more of a challenge. In this course, we will focus on the fundamentals of statistics, which may be broadly described as the techniques to collect, clarify, summarize, organize, analyze, and interpret numerical information.
This course will begin with a brief overview of the discipline of statistics and will then quickly focus on descriptive statistics, introducing graphical methods of describing data.
You will learn about combinatorial probability and random distributions, the latter of which serves as the foundation for statistical inference. On the side of inference, we will focus on both estimation and hypothesis testing issues. We will also examine the techniques to study the relationship between two or more variables; this is known as regression.Introduction to ANOVA
By the end of this course, you should gain a sound understanding about what statistics represent, how to use statistics to organize and display data, and how to draw valid inferences based on data by using appropriate statistical tools.
First, read the course syllabus. Then, enroll in the course by clicking "Enroll me in this course". Click Unit 1 to read its introduction and learning outcomes.
You will then see the learning materials and instructions on how to use them. In today's technologically advanced world, we have access to large volumes of data. The first step of data analysis is to accurately summarize all of this data, both graphically and numerically, so that we can understand what the data reveals. To be able to use and interpret the data correctly is essential to making informed decisions.
For instance, when you see a survey of opinion about a certain TV program, you may be interested in the proportion of those people who indeed like the program. In this unit, you will learn about descriptive statistics, which are used to summarize and display data. After completing this unit, you will know how to present your findings once you have collected data. For example, suppose you want to buy a new mobile phone with a particular type of a camera. Suppose you are not sure about the prices of any of the phones with this feature, so you access a website that provides you with a sample data set of prices, given your desired features.
Looking at all of the prices in a sample can sometimes be confusing. A better way to compare this data might be to look at the median price and the variation of prices. The median and variation are two ways out of several ways that you can describe data.Flipped classroom. If you are an an instructor teaching introductory computer science, an effective way for you to teach the material in a typical college class is to adhere to a weekly cadence, as follows: Each week, send an email to all students in the class that briefly describes activities for that week lectures, reading, and programming assignments drawn from the book or from this booksite.
Students watch the lecture videos at their own pace, do the readings, and work on the programming assignments. This is just one suggestion—this material can support many different teaching styles and formats. Important note: A common mistake in teaching a flipped class is to add too much enrichment material. Our experience is that time in class meetings is much better spent preparing students for success on programming assignments and exams.
If an instructor makes it clear that the best way to prepare for exams is to watch the lecture videos and do the reading, most students will do so. Class meetings then can involve interacting with students and with the material in such a way as to reinforce understanding. For example, working with potential exam questions is an excellent activity.
An effective way to learn the material on your own is to watch the lecture videos on some regular schedule, do the associated reading, and attempt to solve some of the exercises in the book or on the booksite on your own.
If you get stuck on a particular exercise, find some others or try to solve some of the problems given in the lectures without looking at the solutions there.
Available lectures. During the spring ofthe lecture videos are freely available. When watching a lecture video, it is very important to choose an appropriate speed. If it is too slow, you are likely to be bored; if it is too fast, you are likely to get lost. Also be sure to make liberal use of pause and rewind. The lecture videos are available from CUvids ; the lecture slides are in pdf format.
We illustrate our basic approach to developing and analyzing algorithms by considering the dynamic connectivity problem. We introduce the union-find data type and consider several implementations quick find, quick union, weighted quick union, and weighted quick union with path compression. Finally, we apply the union-find data type to the percolation problem from physical chemistry. Lecture 2: Analysis of Algorithms. The basis of our approach for analyzing the performance of algorithms is the scientific method.
We begin by performing computational experiments to measure the running times of our programs. We use these measurements to develop hypotheses about performance. Next, we create mathematical models to explain their behavior. Finally, we consider analyzing the memory usage of our Java programs. Lecture 3: Stacks and Queues.
We consider two fundamental data types for storing collections of objects: the stack and the queue. We implement each using either a singly-linked list or a resizing array. Finally, we consider various applications of stacks and queues ranging from parsing arithmetic expressions to simulating queueing systems.Toggle navigation. Help Preferences Sign up Log in. View by Category Toggle navigation. Products Sold on our sister site CrystalGraphics.
Title: Introduction to Statistics. It is used to give information about unknown values in the corresponding population.
Provided by: elizabeth Tags: introduction investigation statistics. Latest Highest Rated. Title: Introduction to Statistics 1 Introduction to Statistics February 21, 2 Statistics and Research Design Statistics Theory and method of analyzing quantitative data from samples of observations to help make decisions about hypothesized relations. Tools used in research design Research Design Plan and structure of the investigation so as to answer the research questions or hypotheses 3 Statistics and Research Design Analogy Research design is the blueprint of the study.
In quantitative designs, statistical design and procedures are the craft and tools used to conduct quantitative studies. The logic of hypothesis testing is the decision-making process that links statistical design to research design. A way to classify groups or categories. No particular value is placed between the numbers in the rating scale. Interval Difference between the numbers on the scale is meaningful and intervals are equal in size. NO absolute zero. Allows for comparisons between things being measured Temperatures on a thermometer The difference between 60 and 70 is the same as the difference between 90 and You cannot say that 70 degrees is twice as hot as 35 degrees, it is only 35 degrees warmer.
Ratio Scales that do have an absolute zero point than indicated the absence of the variable being studied.
INTRODUCTION TO BIOSTATISTICS - PowerPoint PPT Presentation
Can form ratios. Time 7 Descriptive Statistics Frequency Distributions In tables, the frequency distribution is constructed by summarizing data in terms of the number or frequency of observations in each category, score, or score interval In graphs, the data can be concisely summarized into bar graphs, histograms, or frequency polygons 8 No Transcript 9 Descriptive Statistics Normal Curve Bimodal Curve 10 Descriptive Statistics Positively Skewed Negatively Skewed 11 Descriptive Statistics Measures of Central Tendency Mode The most frequently occurring score 3 3 3 4 4 4 5 5 5 6 6 6 6 Mode is 6 3 3 3 4 4 4 5 5 6 6 7 7 8 Mode is 3 and 4 Median The score that divides a group of scores in half with 50 falling above and 50 falling below the median.
Add up all scores and divide by total number of scores. Mode Example 2 3 4 4 4 6 8 9 10 11 11 Median Example 2 3 4 4 4 6 8 9 10 11 11 Mean Example 2 3 4 4 4 6 8 9 10 11 11 13 Descriptive Statistics Measures of Variability Dispersion Range Calculated by subtracting the lowest score from the highest score.
Used only for Ordinal, Interval, and Ratio scales as the data must be ordered Example 2 3 4 6 8 11 24 Range is 22 Variance The extent to which individual scores in a distribution of scores differ from one another Standard Deviation The square root of the variance Most widely used measure to describe the dispersion among a set of observations in a distribution. A Z score of 1. For example, the population mean is a parameter that is often used to indicate the average value of a quantity A statistic is a quantity that is calculated from a sample of data.
For example, the average of the data in a sample is used to give information about the overall average in the population from which that sample was drawn. The standard error of the mean is used as an estimate of the magnitude of sampling error.