Biostatistics Course Syllabus 1446 PDF

Syllabus of biostatistics First Semester 1446 Catalog Description # Week Chapters Name Page Number Week 2 Orientation Week 2 Chapter 1...

Syllabus of biostatistics First Semester 1446 Catalog Description # Week Chapters Name Page Number Week 2 Orientation Week 2 Chapter 1 Introduction To Biostatistics (Statistics , data , Biostatistics, Variable ,Population ,Sample) Week 3 Chapter 2 Strategies for understanding the meanings of Data (frequency table, bar chart ,range width of interval , mid-interval Histogram , Polygon). Week 4 Exercises Week 5 Chapter 3 Descriptive Statistics Measures of Central Tendency (Descriptive Statistic, measure of central tendency ,statistic, parameter, mean (μ) ,median, mode) Week 6 National day (holiday ) Week 7 Chapter 4 Descriptive Statistics Measures of Dispersion (Descriptive Statistic, measure of dispersion , range ,variance, coefficient of variation.) Week 8 Exercises Week 9 Mid-term Week 10 Chapter 5 Using sample statistics to Test Hypotheses about population parameters (Null hypothesis H0, Alternative hypothesis HA , testing hypothesis , test statistic , P-value 1  Week 11 Exercises Week 12 Chapter 6 Type of statistical information routinely collected in hospitals on a monthly and annual basis Week 13 Chapter 7 Formulae used for the calculation of rates and percentages used in the collection of statistical data Week 14 Exercises Week 15 Excel program practice Assignments and Exams: Exams Date Time Mark Method Midterm 25 Class and home 15 work Quizzes and 10 participation Project 10 Final 40 Attendance: Students missing more than 25% of the total class hours won't be allowed to write the final exam. 2  Chapter 1 Introduction to Biostatistics Statistics is a field of study concerned with: 1- collection, organization, summarization and analysis of data. 2- drawing of inferences about a body of data when only a part of the data is observed. Statisticians try to interpret and communicate the results to others. * Biostatistics: The tools of statistics are employed in many fields as business, education, psychology, agriculture, economics, … etc. When the data analyzed are derived from the biological science and medicine, we use the term biostatistics to distinguish this particular application of statistical tools and concepts. Data: The raw material of Statistics is data. We may define data as figures. Figures result from the process of counting or from taking a measurement. For example: When a hospital administrator counts the number of patients (counting). When a nurse weighs a patient (measurement) Sources of Data: We search for suitable data to serve as the raw material for our investigation. Such data are available from one or more of the following sources: 1- Routinely kept records. For example: Hospital medical records contain immense amounts of information on patients. Hospital accounting records contain a wealth of data on the facility’s business activities. 2- External sources. The data needed to answer a question may already exist in the form of published reports, commercially available data banks, or the research literature, i.e. someone else has already asked the same question. 3  3- Surveys: The source may be a survey, if the data needed is about answering certain questions. For example: If the administrator of a clinic wishes to obtain information regarding the mode of transportation used by patients to visit the clinic, then a survey may be conducted among patients to obtain this information 4- Experiments. Frequently the data needed to answer a question are available only as the result of an experiment. For example: If a nurse wishes to know which of several strategies is best for maximizing patient compliance, she might conduct an experiment in which the different strategies of motivating compliance are tried with different patients.  A variable: It is a characteristic that takes on different values in different persons, places, or things. For example: - Heart rate, - The heights of adult males, - The weights of preschool children, - The ages of patients seen in a dental clinic. Types of variables Quantitative Qualitative 1. Quantitative Variables It can be measured in the usual sense. For example: - The heights of adult males, - The weights of preschool children, 4  - The ages of patients seen in a dental clinic Types of quantitative variables Discrete Continuous A discrete variable Is characterized by gaps or interruptions in the values that it can assume. For example: The number of daily admissions to a general hospital, The number of decayed, missing or filled teeth per child in an elementary school. A continuous variable:  Can assume any value within a specified relevant interval of values assumed by the variable.  For example: Height, weight, skull circumference.  No matter how close together the observed heights of two people, we can find another person whose height falls somewhere in between. 2. Qualitative Variables Many characteristics are not capable of being measured. Some of them can be ordered or ranked. For example: - Classification of people into socio-economic groups, - Social classes based on income, education, etc. Types of qualitative variables 1. Nominal:  As the name implies it consist of “naming” or classifies into various mutually exclusive categories  For example: - Male – female, Sick – well, married – single - divorced 2. Ordinal:  Whenever qualitative observation can be ranked or ordered according to some criterion.  For example: Blood pressure (high-good-low) Grades (Excellent – V.good –good –fail)  A population 5 It is the largest collection of values of a random variable for which we have an interest at a particular time. For example:  The weights of all the children enrolled in a certain elementary school.  Populations may be finite or infinite.  A sample: It is a part of a population. For example: The weights of only a fraction of these children. Exercises: Q1. For each of the following variables indicate whether it is quantitative or qualitative variable: (a) Class standing of the members of this class relative to each other. (b) Admitting diagnoses of patients admitted to a mental health clinic. (c) Weights of babies born in a hospital during a year. (d) Gender of babies born in a hospital during a year. (e) Range of motion of elbow joint of students enrolled in a university health sciences curriculum. (f) Under-arm temperature of day-old infants born in a hospital.  Q7: For each of the following situations, answer questions a through d: o What is the population? o What is the sample in the study? o What is the variable of interest? o What is the type of the variable? Situation A: A study of 300 households in a small southern town revealed that 20 percent had at least one school-age child present. a. Population: b. Sample: c. Variable: d. Variable Type Situation B: A study of 250 patients admitted to a hospital during the past year revealed that, on the average, the patients lived 15 miles from the hospital. a. Population: b. Sample: 6  c. Variable: d. Variable Type Chapter (2 ) Strategies for understanding the meanings of Data Descriptive Statistics- Frequency Distribution for Discrete Random Variables: Example: The study was conducted in a primary school, were only16 children were enrolled in a study as a sample. We collected the following data about the number of their decayed teeth: 3, 5, 2, 4, 0, 1, 3, 5, 2, 3, 2, 3, 3, 2, 4,1 To make a frequency table we should: 1- Order the values from the smallest to the largest. 0,1,1,2,2,2,2,3,3,3,3,3,4,4,5,5 2- Find the repeated values (Frequency). No. of decayed teeth Frequency Relative Frequency 0 1 0.0625 1 2 0.125 2 4 0.25 3 5 0.3125 4 2 0.125 5 2 0.125 Total 16 1 Representing the simple frequency table using the bar chart We can represent the above simple frequency table using the bar chart as follow: 7  6 5 5 4 4 3 2 2 2 2 Frequency 1 1 0.00 1.00 2.00 3.00 4.00 5.00 Number of decayed teeth Frequency Distribution for Continuous Random Variables For large samples, we can’t use the simple frequency table to represent the data. We need to divide the data into groups or intervals or classes. So, we need to determine: 1- The number of intervals ( classes)(k).  The number of intervals pretty much depends on the size of the data. In statistics, it is a common practice to keep the number of classes between 6 and 15. Too many classes will kill the purpose of data condensation into meaningful groups. At the same time, too few classes will result in a loss of information. Therefore, we always need to strike an appropriate balance  A commonly followed rule is that 6 ≤ k ≤ 15, or the following formula may be used: k = 1 + 3.322 (log N) 2- The range (R). It is the difference between the largest and the smallest observation in the data set. 3- The Width of the interval (w).  Class intervals generally should be of the same width. Thus, divide range (R) (from step 2) by the number of classes (K) and round to next higher whole number.  The result of the division will give us equal class-interval.  But in practice, intervals that are multiples of 5 or 10, are commonly used as people can understand them easily  w ≥ R / k. Example:  The number of observations is100, find the width. 8 k = 1+3.322(log 100) = 1 + 3.322 (2) = 7.6 ᵙ 8.  Assume that the smallest value = 5 and while the largest is = 61, then R = 61 – 5 = 56 and w = 56 / 8 = 7. To make the summarization more comprehensible, the class width may be 5 or 10 or the multiples of 10. Example: for the next table which represents age data of 189 subjects who participated in a study about the smoking cessation, how many class interval should we consider? Solution : Since the number of observations equal 189, then k = 1+3.322(log 189) k = 1 + 3.3222 (2.276) ᵙ 9 R = 82 – 30 = 52 and w = 52 / 9 = 5.778 It is better to consider w = 10, then the intervals will be in the form: Class interval Frequency 30 – 39 11 40 – 49 46 50 – 59 70 60 – 69 45 70 – 79 16 80 – 89 1 Total 189 Sum of frequency =sample size=n The Cumulative Frequency: 9 It can be computed by adding successive frequencies. The Cumulative Relative Frequency: It can be computed by adding successive relative frequencies. The Mid-interval: It can be computed by adding the lower bound of the interval plus the upper bound of it and then divide over 2. For the above example, the following table represents the cumulative frequency, the relative frequency, the cumulative relative frequency and the mid-interval. Class interval Mid –interval Frequency Cumulative Relative Cumulative Freq (f) Frequency Frequency Relative R.f Frequency 30 – 39 34.5 11 11 0.0582 0.0582 40 – 49 44.5 46 57 0.2434 - 50 – 59 54.5 - 127 - 0.6720 60 – 69 - 45 - 0.2381 0.9101 70 – 79 74.5 16 188 0.0847 0.9948 80 – 89 84.5 1 189 0.0053 1 Total 189 1 Example From the above frequency table, complete the table then answer the following questions: 1-The number of objects with age less than 50 years ? 2-The number of objects with age between 40-69 years ? 3-Relative frequency of objects with age between 70-79 years ? 4-Relative frequency of objects with age more than 69 years ? 5-The percentage of objects with age between 40-49 years ? 6- The percentage of objects with age less than 60 years ? 7-The Range (R) ? 10 8- Number of intervals (K)? 9- The width of the interval ( W) ? Representing the grouped frequency table using the histogram: To draw the histogram, the true classes limits should be used. They can be computed by subtracting 0.5 from the lower limit and adding 0.5 to the upper limit for each interval. True class limits Frequency 29.5 –