Design Project on Digital Logic Circuits
As we delve into a new design project centered around digital logic circuits, we encounter intriguing puzzles and practical applications. From determining switch control over hidden light bulbs to exploring the construction of digital circuits, this journey offers insights into problem-solving and circuit design. Embrace the challenge of deciphering circuits, creating truth tables, and selecting optimal designs using techniques like the Pugh matrix. Join the exploration of electrical circuits and logical operations to sharpen your skills in digital design.
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BIHAR ANIMAL SCIENCES UNIVERSITY, PATNA, BIHAR Bihar Veterinary College, Patna UNIT-I Data and its types Speaker: Ramesh Kumar Singh Assistant Professor cum Jr. Scientist Department of Animal Genetics and Breeding Bihar Veterinary College, Patna
Introduction Once it is decided what type of study is to be made. , it becomes necessary to collect information about the concerned? Study performed, mostly in the form of data. Data is generally generated or gathered with surveys or experiments.
Survey method Survey method collect data from certain individuals directly or indirectly. In surveys, the required information is supplied by the individual under study or is based on measurements of certain units. Generally, the respondents or units are selected from a population using some standard sampling techniques.
Experiment Method Another way of collecting data is by experimentation An actual experiment is conducted on certain individuals or units about which the inference is to be drawn. Such experimental studies are common in agriculture, biology, medical science, chemistry, industry etc.
Data Statistics require data as a raw material upon which its methods or techniques applied to arrive at inferences to make wise decisions. Data are a collection of facts such as values or measurements. It can be numbers, words, measurements, observations, or even just descriptions of things. It is of two types: constant and variable.
Constant Constant is a situation/value that does not change, Due to unchangeable property, constant is not used
Variable A characteristic, number, or quantity that increases or decreases over time or takes different values in different situations is called variable. only variable is used for summary measures and analysis. Data of variables may be quantitative as well as qualitative. The qualitative data are quantified to assign numbers using techniques like ranking, scoring, scaling or coding etc. Data are tabulated and analyzed statistically. The numerical values obtained from analysis are used to drawn proper and correct inferences The inferences lead to a final decision. This entire process of statistics ultimately makes sense of data in the wake of uncertainty.
Raw material of statistics --------Data ----- Data which is defined as numbers or a set of numbers The data consisted of numbers which obtained either from counting or measurement or both. The Singular of data is called as datum. Data are observations of random variables made on the elements of a population or sample. A collection of data is often called a data set (Singular).
Statistics Contribution Numbers ------- data ------- contain information --------- extract information -------- purpose of statistics to investigate and evaluate the nature and meaning of this information. The basic ideas in statistics assist us in thinking clearly about the problems, provide some guidance to the conditions that must be satisfied that have no good logical foundation.
Types of variables There are four types of variables: nominal, ordinal, discrete, and continuous. These variables are grouped into two groups qualitative and quantitative types. The qualitative variables include nominal and ordinal data which are assessed in terms of words or attributes. The quantitative data incorporates discrete and continuous variables. The quantitative data obtained as result of measurement of various characteristics with a set of standard rules.
Qualitative variable Qualitative variable is also called categorical variable It shows the quality or properties of the data. It is represented by a name, a symbol, or a number code. These scales are mutually exclusive (no overlap) and none of them have any numerical significance. It is two types: nominal and ordinal.
Nominal variable: Nominal data are simply names orproperties having two or more categories There is no intrinsic ordering to the categories, i.e., data have no natural ranking or ordering. For example, gender (male and female) and marital status (married/unmarried) have two categories, but these categories have no natural order or ranking.
Ordinal variable An ordinal variable is similar to anominal variable. The difference between the two is that there is a clear ordering in the data, i.e., ordinal data, unlike nominal data, have some order. For example, ordinal scales are seen in questions that call for ratings of quality (very good, good, fair, poor, very poor), agreement (strongly agree, agree, disagree, strongly disagree), economic status (low, medium, and high), etc. All the ranking data including Like scales, Bristol stool scale, and all the other scales which are ranked between 0 and 10 are also called ordinal data.
Quantitative variable Quantitative variable is the data that show some quantity through numerical value. Quantitative data are the numeric variables (e.g., how many, how much, or how often). Age, blood pressure, body temperature, haemoglobin level, and serum creatinine level are some examples of quantitative data. It is also called metric data. It is two types: discrete and continuous.
Discrete variable: Discrete variable is the quantitativedata, but its values cannot be expressed or presented in the form of a decimal; for example, number of males, number of females, number of patients, and family size cannot express in decimal in meaningful way.
Continuous data: Data are measured in values and can bequantified and presented in decimals. Age, height, weight, body mass index, serum creatinine, heart rate, systolic blood pressure, and diastolic blood pressure are some examples. The variables such as heart rate, platelet count, respiration rate, systolic blood pressure, and diastolic blood pressure are in fact discrete (measuring in complete number) but are considered continuous because of large number of possible values. Only those variables which can take a small number of values, say, <10, are generally considered discrete. Summary is that if discrete variables values are at least 10 or more, then discrete variables can be considered as continuous variable and we analyse them as per the methods applicable on continuous data.
Sources of Data In order to answer any particular questions, we need suitable data as raw material. Such data are usually available from one or more of the following sources. Routinely kept records. Surveys Experiments External Sources
Types of Data There are two categories of data namely, (i) primary data and (ii) secondary data. Primary Data The data, which are collected from the units or individual respondents directly for the purpose of certain study or information, are known as primary data. The data obtained in a study by the investigator are termed as primary data.
Secondary Data The primary data, which had been collected by certain people or agency treated statistically and now if the information contained in primary data it is used again i.e. from records, processed and statistically analyzed data to extract some information for other purpose, is termed as secondary data. Usually, secondary data is obtained from year books, census reports, survey reports, official records or reported experimental findings. Processing of Data Before tabulation data, is should be scrutinized for (i) completeness, (ii) consistency, (iii) Accuracy and (iv) editing.
