 Welcome back to a new session on dentistry and more. So today we have a small topic that is a continuation of biostatistics that is collection of data and its presentation. So regarding data we have already seen the types of data like types of variables, biostatistics, the measures of central tendency, measures of dispersion and normal curve and various tests of significance. So everywhere this data is coming. So today's video is about the very basic step of any research process that is after the collection of data through various methods like interviews, questionnaires and hospital records or any other methods we do for our data collection. So the second step is we need to present it in a tabular form or in a graphical representation. So that is an easy way to convey the message or the convey the data what we get in a very comprehensive manner. So today's video is about collection of data and presentation of data. It is the first step of any research. So let's see how do we collect data. Question is commonly done using interviews that is face-to-face interviews maybe we may have questionnaires with us or we ask questions, open-ended or closed-ended questions and we can mail the questionnaires postly or via any electronic media or we can give the questionnaires not like interviews. Interviews like we are one-to-one interaction is happening but questionnaires we just give questionnaires and asking the participant to fill it out. It can be open-ended and closed-ended. So open-ended means the participants have an option to write whatever he feels but closed-ended means he has to just tick whatever the option is given. He has no other inclusion from his side and we can collect data from hospital records, any other registries, any health department registries, all the records we can collect. So this is some common example for collection of data but this also may be asked for exam as how do you collect data. So you can just elaborate it how interviews are done, how the questionnaires are done. So commonly we conduct questionnaires in our research purposes. The questionnaires we prepare and distribute it to students or the participants of the particular study. Then we can collect hospital records in case of case control study, in case control study or any other studies usually we conduct interviews. So that is all about collection of data. Now we go to the presentation of data. How do we present data? So we can use tabular columns and graphical methods. Tabular column is the data we present in tables. It can be simple or complex table. In graphical it is graphs, it can be for quantitative data and qualitative data. So data classification I have mentioned in my previous video in types of variables. So quantitative data is nothing but data with value, qualitative is without any particular value. It is like eye color of the categories of eye color like blue, brown, black, gender classification like male, female that is qualitative data. Quantitative means quantitative data is like with quantity, the height of a person, weight of a person, the blood sugar level, the hemoglobin level. So all are coming under quantitative data. So this is qualitative data. So in quantitative data we can use histogram, frequency, polygon, frequency curve, line chart, normal curve we have already seen, cumulative curve of scatterplot. In qualitative data bar chart, pictogram, pie chart and map diagram can be used. So it is like how do we perform a tabulation? It's like how do we prepare a tables? The basic principles we need to number it and title it properly with proper headings and should not be very large. Types are simple and complex tables. So simple table is like this, just an example, infant mortality rate in 2004. So on left side we have various countries on right side infant mortality rate. It's a very simple table. But frequency distribution table it's like we need to make class interval and frequency because if it is a very large group we need to divide the group into class intervals that is convenient groups and the number of items, that is frequency. So class interval should not be very large or should not be very small and number of classes should be between 8 and 15. So all these are basic characteristics of a table. So frequency table it's like class intervals. So divided the age category into different interval that is 0 to 4, 5 to 9, 10 to 14, 15 to 19, 20 to 24 and this is frequency. Like how many people are belonging to each class interval. So this is a little more bigger class interval and frequency. So that is about tables. Now we have charts. Charts I mentioned you earlier it is for qualitative data. So this is quantitative data. Quantitative data we use histogram and qualitative data. We can use bar charts pictogram pie chart. So it can be a table or graphical. So graphical also we have two classification. So let's see what is chart and diagrams. Why it is important because it gives information at a glance. So it has a very powerful impact on imagination of people because it is very easy to remember an image rather than a table because it is better retained in memory than at a particular table. But the problem with this charts and diagrams is maybe some of the original data is lost when we are preparing charts and diagrams. So tables will give more information but a better representation will be done by these charts and diagrams. So common use diagrams of pie chart, simple, multiple, component, biograph, histogram, frequency curve, auger curve, scatter, line, pictogram. So bar charts are like this. So this is bar charts. So it is categorical data that is various categories are there like one, two, three, four, five, six, seven, eight, nine, ten, that is the different classes and number of students in each class for standard second, three, four, five, six. So we are representing a one each bar represent one attribute. So this bar represents number of students in the class one, class two, class three. Okay. So this is bar chart. So the width of the bar and the graphs between the bars, sorry, the gaps between the bars should be equal throughout and it can be vertical or horizontal. And this is the most important part. The length of the bar is proportional to the magnitude of frequency of variable. So you can see this is a little height that is 70, it is less and this you can see it is length is very high and it represents 300. So the length of the bar is directly proportional to the magnitude or frequency of variable. Okay. So the same bar charts can be multiple if we are representing two variable in a single bar chart because we are representing population and land like this. So various continents like Asia-Europe. So this is a population and this is a land available. Here we are representing only one variable in a single bar but here we are representing two variables in a single bar that is multiple bar chart because there will be a one sub attribute of variable. Component bar chart is like in a single bar we are representing different categories in a vertical fashion that is male and female like Pakistan and USA, Sweden how many female and males are there and it is proportional 100% maximum. So we can see that 48% is of male and remaining 52 will be female. In USA it is like 60 and 40 in Sweden it is males are very less. So that is component bar graph. Here we can represent many categories on X axis and they have further subcategories. So the bars may be divided into parts and each part are presenting a certain item and the proportional to the magnitude of that particular item. So this is component bar graph but multiple bar charts like different attributes of a same variable. So histogram is like a continuous bar graph without any gaps because in bars chart we can see there is a gap between the each variable or each attribute but in histogram there is no gap between this because it represents quantitative variable because it represents values. Here it represents different categories Asia Europe are different categories the variable is categorical variable but here we are representing the sugar cholesterol level. So that is a quantitative variable. So when we have quantitative variable we can use histogram. So quantitative continuous variable like age, weight, height, blood pressure, blood sugar can be expressed in this way that is histogram. So the basic difference between histogram and bar chart is a variable we used to represent in bar chart it is categorical or categories here we are using continuous variable. So you will understand better about variable in the previous video that is types of variables. So basic thing is quantitative variables is like numbers and called categorical or qualitative variable is like categories like Asia like the continents or other variables. So this is a continuous histogram. So if we mark the middle point of each histogram and connect it by a line it becomes frequency polygon. So this we connect if we mark the central point it is not the same but if we connect like that we get frequency polygon. So it is getting by joining the midpoint of histogram blocks. So sorry this one S is missing here. So it is by joining the midpoints of histogram blocks. So if you connect the midpoint and join by a line it becomes frequency polygon. So normal curve we have already seen what is a normal curve and standard deviation and mean it is a bell shaped curve and it is also known as question distribution so I am not explaining in detail about the normal curve. Line diagram is nothing but we are representing using a line it is showing the malaria cases reported throughout the world excluding African region. So this is like cases on the y-axis this is like here. So 1972 it is 3 cases 73 like 3 cases 74 4 cases so it is x and y axis represented by line. Pi charts are like pi so we know how a pi looks like it will be 360 degree a total. So we can represent 2 variables or 3 or 4 variables by percentage and ultimately the entire percentage should be 100 percentage. So we have seen this in our k-diagram example k-diagram will be a pie chart ultimately. So we get the color of green sector by adding all the remaining colors and subtracting it from 100 because the total percentage will be 100 or the degree will be 360. You know the entire circle will be 360 degree. So it is indicating percentages of particular segment. So it is like developed countries are 26 percentage and developing countries are developing at 26 and 74 developed countries world population. So it is pie chart it looks like a pie and total will be 360 degree and 100 percentage. So we can use this like various factors doctors went to Lahore this is like 50 percentage doctors retired is like 25 percentage and the remaining are doctors only or working doctors. So 4 categories are expressed here but if we add all we get 100 percentage. So each color represent a proportion of the 100 so total 100 and each will represent a certain percentage which belong to that particular category and all we add we get 100 percentage. So pictogram is like representing using pictures. So it is small pictures or symbols we are using. So it is like how many cupcakes we eat per day. So Monday we 6 Tuesday 3 Wednesday 4 so instead of numbers we use pictures. So it is like if you have big data. So each cupcake is represented by 6 cupcakes so Monday it is like if you are selling not eating if you are selling it it becomes 1 2 3 4 5 30 cupcakes we are selling on Monday Tuesday it is 3 so 18 cupcakes so Saturday 3 4 5 6 7 8 9 10. So on Saturday we are selling 60 cupcakes because it is not easy to represent 60 here. So we represent 1 cupcake as 6 cupcakes so it is another way of representing data that is known as pictogram and scatter diagram it is like x and y axis we have 2 continuous variables so it is usually used for correlation that is positive correlation it goes like upward if it is negative correlation it goes downward. So it is like putting dots comparing one variable on the x axis and another variable on y axis x axis and y axis so each dot represents the relationship between one variable on here and one variable here. So that's all about our data collection and presentation. So we collect data using interviews, questionnaires, hospital records and presentation by tabular or graphical. Graphical can be quantitative and qualitative. So we have seen bar charts, histogram by charts and maps bar charts and histograms are very confusing bar charts we use for qualitative data there will be gap between bar charts because it is representing different categories but quantitative data will be represented by histogram it will be continuous there will not be any gap between the vertical columns so that is the only confusing part others as very easy like frequency polygon we connecting the middle points of histogram then frequency curve line charts normal curve we have seen scatter charts pie chart it's like our histogram pictogram it's like that cupcake but I have seen so it is a very simple portion of my statistics it's like how do we present our data more conveniently so that the viewer can get the gist of the comprehensive picture of a particular research particular data collection. Okay so I'll come up with new topic and identity statement thank you.