 Hello and welcome to the session. In this session we will discuss branches of statistics, types of data and levels of measurement. First of all we will discuss branches of statistics. There are two branches of statistics and these are descriptive and inferential. Now descriptive branch deals with collection of data, its presentation in various forms such as graphs, tables, diagrams etc. For example, in business statistics, industrial statistics etc., a businessman make use of descriptive statistics to present the annual reports, final accounts etc. Now inferential branch deals with techniques used for analysis of data. It is a branch that involves samples to draw conclusion about a population. For example, suppose we want to estimate the percentage of people below poverty line in our country. For this we take a sample from the population and find the proportion of people below poverty line in sample. Then this sample proportion with the help of probability enables us to make some inferences about population proportion. This study belongs to inferential statistics. Now let us discuss types of data. There are two types of data. One is quantitative data and the other is qualitative data. Quantitative data is the data that is given numerically. This is further of two types, discrete data and continuous data. Discrete data has specific numeric values like shoe size etc. And continuous data can take any numeric value for example height, weight etc. And qualitative data is the data that is not given numerically. For example, we have place of birth, favorite food etc. Now we are going to discuss levels of measurement. Level of measurement is another characteristic of data. The level of measurement determines which statistical calculations are meaningful. Now the four levels of measurement in order from lowest to highest are nominal, ordinal, interval and ratio. Nominal data have no order and thus only gives names or levels to various categories. Colors of a particular candy is an example of nominal level data. As it is distinguished by name only, there is no standard ordering scheme to this data. Next is ordinal data and ordinal data have ordered but the interval between measurements is not meaningful. For example, movies on a particular TV show are classified as two thumbs up, one thumb up or zero thumbs up. There is an order here so it is an example of ordinal level data. Then we have interval. Data at the interval level of measurement can be ordered and meaningful differences between data entries can be calculated. At the interval level a zero entry simply represents a position on a scale. The entry is not an inherent zero. The boiling temperatures of different liquids are listed. This is an example of interval level data. We can tell whether the temperature is higher or lower than another so we can put them in an order. Also if water boils at 212 degrees and another liquid boils at 294 degrees we see that the second temperature is 82 degrees higher than the first. So the difference between data are measurable and meaningful. And lastly we have ratio. Data at the ratio level of measurement are similar to the data at the interval level with an additional property that a zero entry is an inherent zero. A ratio of two data values can be formed so that one data value can be meaningfully expressed as a multiple of another. For example four persons are randomly selected and asked how much money they have with them and here are the results $20, $50, $63 and $300. Now the question is is there an order to this data and we say yes there is an order $20 is less than $50 is less than $63 is less than $300. Then our next question is are the differences between the data values meaningful? And our answer is yes the person who has $50 has $30 more than the person with $20. Now we can also calculate ratios based on this data because $0 is the absolute minimum amount of money a person could have with him. The person with $300 has six times as much as the person with $50. We should note that inherent zero is zero that means none. For example the amount of money in your savings account is $0 in this case zero represents no money but the temperature of zero degrees Celsius does not show a condition where no heat is present. Zero degrees Celsius is simply a position on Celsius scale it is not an inherent zero. Thus in this session we have discussed branches of statistics, types of data and levels of measurement. This completes our session hope you enjoyed this session.