Veterinary Biostatistics PDF - 1-10-2024

‫ا‪.‬د‪ :‬ﺣﺳﻧﯾﮫ ﺳوﯾﻔﻲ ﻋﺑد اﻟﻣﺣﺳن‬ ‫ﻗﺳم ﺻﺣﮫ اﻟﺣﯾوان و اﻟدواﺟن‬ ‫ﻛﻠﯾﮫ اﻟطب اﻟﺑﯾطري – ﺟﺎﻣﻌﮫ اﺳﯾوط‬ ‫‪1-10-2024‬‬ ‫‪1‬‬  ‫— ;ﺴﻢ ﷲ اﻟﺮﲪﻦ اﻟﺮﺣﲓ‬ ‫ﺷْﻲٍء أ َْﺣ َ‬ ‫ﺼْﯿﻨَﺎهُ ِﻛﺘ َﺎﺑًﺎ«‬ ‫»َوُﻛﱠﻞ َ‬...

‫ا‪.‬د‪ :‬ﺣﺳﻧﯾﮫ ﺳوﯾﻔﻲ ﻋﺑد اﻟﻣﺣﺳن‬ ‫ﻗﺳم ﺻﺣﮫ اﻟﺣﯾوان و اﻟدواﺟن‬ ‫ﻛﻠﯾﮫ اﻟطب اﻟﺑﯾطري – ﺟﺎﻣﻌﮫ اﺳﯾوط‬ ‫‪1-10-2024‬‬ ‫‪1‬‬  ‫— ;ﺴﻢ ﷲ اﻟﺮﲪﻦ اﻟﺮﺣﲓ‬ ‫ﺷْﻲٍء أ َْﺣ َ‬ ‫ﺼْﯿﻨَﺎهُ ِﻛﺘ َﺎﺑًﺎ«‬ ‫»َوُﻛﱠﻞ َ‬ ‫ﺻﺪق ﷲ اﻟﻌﻈﲓ‬ ‫ا‪ٓ+‬ﯾﺔ )‪ (29‬ﺳﻮرة اﻟﻨﺒٔ‪9‬‬ ‫‪2‬‬  Experimental design Target: bird The effect of treatment A on the 1 week old (120 bird) bird health. Control treatment For 35 day § 7-day-old bird were 1.5 ml/liter 2ml /litter 1ml/litter randomly divided into 4 groups. At the end of the experiment § Each treatment consisted Data collection of 5 replicates of 6 birds each. Blood sample Another data  How does biostatistics work? 1.Design of experiments. The first aspect is the design of experiments. 2.Data collection. After designing an experiment, the next step is data collection. 3.Data classification. 4.Data analysis. 5.Data interpretation. 6.Evaluate a program's impact. 7.Create population-based interventions. 8.Control epidemics. 4  Coding Data It is common practice to code data by assigning numerical values to nonnumeric measurements. An example might be to code gender as 1's and 2's instead of "male" and "female". The choice of 1 for male and 2 for female is rather arbitrary (random choice). Coding the scientific articles The main objectives -facilitate the automatic treatment of data for analytical purposes -conserve storage space. 5  Coding Data There are several steps to coding: 1.Developing a preliminary coding scheme. 2.Testing and improving the coding scheme. 3.Testing inter-coder reliability. 4.Coding. 5.Reviewing data by code. 6  Data organization & presenttaion A process of organizing (ordering) raw data, by classifying them into different categories. Aims of Data organization & presenttaion, why?: Ø To describe situations. Ø Draw conclusions. Ø make inferences about events. 7 Data organization & presenttaion There are a wide variety of ways: qStem-and-leaf diagrams qFrequency distributions tables qGraphical presentation (Histograms, bar, and other graphs). 8  Data organization & presenttaion Qualitative & quantitative data 9  Data organization & presenttaion 1) Tabulation/Tabular Presentation The systematic presentation of numerical data in rows and columns is known as Tabulation. It is designed to make presentation simpler and analysis easier. A table facilitates representation of large amounts of data in an attractive, easy to read and organized manner. The data is organized in rows and columns. This is one of the most widely used forms of presentation of data since data tables are easy to construct and read. 10  Data organization & presenttaion 1) Tabulation/Tabular Presentation Tabular data has the following advantages: v Studying the Columns and rows of the table, it is easier to quickly grasp the meaning of data. v Increasing or decreasing nature of data and abrupt values are easily accessible helping to analyze the data. Tables may be: -Simple: measurement of a single set -Complex: measurement multiple sets of things. 11  Data organization & presenttaion 1) Tabulation/Tabular Presentation What are the components of a data table? A data table includes: § title § units of measurement § the independent variable in the left column § the dependent variable in the right column. 12  1) Tabulation/Tabular Presentation Frequency distribution is used to organize the collected data in table.Types of frequency distribution depending on type of variable : For Qualitative variable For Quantitative variable Categorical frequency distribution 1- Ungrouped frequency distribution table table (for nominal & ordinal data) -used when the range of data values is used for data that placed on specific relatively small (0:15) in which each class categories has single data value Ex. Blood groups, coat colors, -deal with discrete variables 2-Grouped frequency distribution table -used for large ranges more than 15 -deal with continuous variables 13  A-Qualitative variable Example1: Categorical frequency distribution Construct frequency distribution table for the following data: - Twenty-five students were given a blood test to determine blood type : A O B A AB B O B O B O B A B B O O O AB AB A O O AB A Answer: Frquency distribution of blood group for Twenty-five students: Blood type Tally Frequency Relative frequency (%) A //// / 5 5/25*100 (20%) B //// /// 7 7/25*100 (28%) AB //// 4 4/25*100 (16%) O //// //// / 9 9/25*100 (36%) sum N= 25 1 or 100% 14 Example2: Number of births in a city during the period 2000-2002 Year births (n) Relative frequency (%) 2000 36 27 (0.27) 2001 46 35 (0.35) 2002 50 38 (0.38) Total 132 100.0 Relative frequency (%) : ratio (fraction or proportion), the number of times a particular value for a variable (data item) has been observed to occur in relation to the total number of values for that variable. Sum of relative frequencies = 1 or 100% Relative frequency number of values within an interval x 100 total number of values in the table 15  B- Quantitative variable A-Ungrouped frequency distribution table A type of frequency distribution displays the frequency of each individual data value instead of groups of data values. We can directly see how often different values occurred in the table. -The following data represent the number of children in a sample of 50 families. -Summarize and organize theses data in an appropriate frequency distribution table. 0 1 3 2 1 3 4 0 2 3 3 2 3 0 1 2 3 4 4301201304013423 3 3 2 3 2 3 1 23 3 2 3 3 1 3 3 16  A-Ungrouped frequency distribution table Answer: The number of children in a sample of 50 families. No. of childern Tallies Frequency Relative (class) frequency % 0 //// /// 7 14 1 //// //// 8 2 //// //// // 10 3 20 4 5 50 = N = Total sample size 17  A-Ungrouped frequency distribution table Range The difference between the maximum and minimum value. -Arrange the series in ascending or descending order. -select the highest and lowest values in the distribution. -subtract the minimum value from the maximum value. Range= Maximum value – Minimum value 18  A-Ungrouped frequency distribution table Mean The ratio of summation of observations to the number of observations. Mean= Sum of observations/number of observations Median: The middle value of the data. Arrange the data in ascending or descending order (generally ascending order). Count the number of observations which is denoted by n. Depending on whether n is even or odd, as: If n is an odd number, then the value of (n+1)/2th item is the median. If n is an even number, then median is given as: [ value of n/2 th item + value of (n/2 +1)th item]÷2 19  What is the median of the following data: 3, 7, 4, 8, 9, 6, 10, 12, 13, 15? Answer: Arrange the data: 3, 4,6, 7, 8, 9,10,12,13,15 Since there are 10 data points (even number) Median=(n/2)th term+(n/2 + 1)th term/ 2 The median is the average of the 5th and 6th values. The 5th value is 8, and the 6th value is 9. The median= 8+9/2= 8.5. 20  2-Grouped frequency distribution table It is used to arrange a large number of observations or data. In this, we form class (group) intervals to tally the frequency for the data that belongs to that particular class interval. Example: Marks obtained by 20 students in the test are as follows: 5, 10, 20, 15, 5, 20, 20, 15, 15, 15, 10, 10, 10, 20, 15, 5, 18, 18, 18, 18. ü To arrange the data in grouped table we have to make class intervals. Thus, we will make class intervals of marks like 0 – 5, 6 – 10, and so on. ü Table shows two columns one is of class intervals (marks obtained in test) and the second is of frequency (no. of students). 21  B- Quantitative variable Answer: Marks obtained by 20 students. Marks obtained in Test No. of Students (class intervals) (Frequency) 0–5 3 6 – 10 4 11 – 15 5 16 – 20 8 Total 20 22  1) Tabulation/Tabular Presentation Another solution Marks 5, 10, 20, 15, 5, 20, 20, 15, 15, 15, 10, 10, No. of obtained in Students 10, 20, 15, 5, 18, 18, 18, 18. Test Ungrouped frequency distribution table: 5 3 Don't make class intervals, 10 4 Write the accurate frequency of individual data. 15 5 The table shows two columns: one is of marks 18 4 obtained in the test and the second is of 20 4 frequency (no. of students). Total 20 23  B- Quantitative variable Example 2: Tabulate the following data of body weight (Kg) in the form of frequency distribution (n= 70 persons) 68 59 57 64 65 67 54 62 67 64 57 61 58 67 63 64 65 52 60 55 52 60 62 57 61 61 71 51 55 54 58 60 54 6265 71 63 60 61 56 6062 59 57 64 58 61 63 62 60 57 61 60 65 67 70 58 51 6162 63 62 60 67 55 6261 64 59 64 Largest value = 71 Smallest value = 51 Range= 71-51 =20 No. of classes, groups (K) = (Yule equation) Class limit: 1- lower limit of 1st class = the smallest value 2- lower class limit of 2nd class = smallest value + class width (W) [class width (W) = R/K ] 3- upper limit of 1st class = less than the lower -class limit of 2nd class 24  B- Quantitative variable Answer: No. of classes, groups (K) = 7 Class (group) limit: 1- lower limit of 1st class = the smallest value (51) Class width (W)= R/K= 20/7=2.85=3 2- lower class limit of 2nd class = smallest value + class width (W) =51+3=54 3- upper limit of 1st class = less than the lower class limit of 2nd class =53 25  B- Quantitative variable Answer: The frequency distribution of the body weight (Kg) (n= 70 persons) Group (class limit) Frequency Relative frequency 51-53 4 4/70*100 54-56 7 57-59 12 60-62 25 63-65 13 66-68 6 69-71 3 26  B- Quantitative variable Suppose we conduct a survey in which we ask 15 households how many pets they have in their home. The results are as follows: 1, 1, 1, 1, 2, 2, 2, 3, 3, 4, 5, 5, 6, 7, 8 Largest value = 8 1-Grouped Smallest value = 1 Range= 8-1 =7 No. of classes, groups (K) =4 Class width (W) = 2 1- lower limit of 1st class =1 2- lower class limit of 2nd class = 3 27  B- Quantitative variable Suppose we conduct a survey in which we ask 15 households how many pets they have in their home. The results are as follows: 1, 1, 1, 1, 2, 2, 2, 3, 3, 4, 5, 5, 6, 7, 8 2- Ungrouped displays the frequency of each individual data value rather groups. 28  1) Tabulation/Tabular Presentation Note: ungrouped frequency distributions work best with small datasets in which there are only a few unique values. For example, in a survey data with only 8 unique values so it made sense to create an ungrouped frequency distribution. However, if we had a dataset with hundreds or thousands of unique values, an ungrouped frequency distribution would be incredibly long and difficult to gather information from. For larger datasets, it makes sense to construct grouped frequency distributions. 29  1) Tabulation/Tabular Presentation Rules for Making Frequency Distribution Table v The class or group interval should not be excessively broad or narrow. Too large a group will omit the details and too small will defeat the purpose of making the data concise. v The number of groups or classes should not be too many or very few but ordinarily between 6 and 16 depending on the details necessary and the size of the sample. v The class interval should be the same. v The headings must be clear for instance “height” in inches or in centimeters, “age” in years or months. If the data are expressed as rates mention percent or per thousand. 30  1) Tabulation/Tabular Presentation Rules for Making Frequency Distribution Table v The rates and proportions, if given the actual number in the group must also be noted. v Groups should be tabulated in ascending or descending order. v If certain data are omitted or excluded deliberately, the reasons for doing that should be given. 31  1) Tabulation/Tabular Presentation Using tables to display information has several advantages: Clarity: making it easier for readers to understand relationships and comparisons between different pieces of information. Organization: Tables allow for categorization of information into rows and columns, help to group related data together and reduce visual clutter. Efficiency: Readers can quickly scan tables to find specific information, whereas scanning through lists or paragraphs may require more time and effort. Visual Appeal: Well-designed tables can enhance the visual presentation of information, making it more engaging and easier to interpret. Comparison: Tables facilitate direct comparisons between different data points, 32  1) Tabulation/Tabular Presentation Quantitative Data: When displaying numerical data, tables can effectively convey precise values, making it easier to perform calculations or analyze data without misinterpretation. Space Management: Tables can condense a large amount of information into a smaller space. Accessibility: For some users, tables can be more accessible than long paragraphs, as they provide a clear layout that can be easier to navigate Overall, while lists and paragraphs are useful for certain types of information (like narratives or instructions), tables are particularly effective for presenting structured data and facilitating comparisons. 33