Statistics By Rajat: Statistics and Research to Help

                                       M Measurement Scales in SPSS 1.Nominal: It is measurement scale in which numbers are used as "tags"or "labels" which is used to classify an object.It deals with non-numeric variables.It is used for the purpose of classification. for example, in case of gender scale,it can be classified as Male=1 and Female=2. It is used for only for counting purposes. 2.Ordinal: It is the second level of measurement in which ranking of data without actually establishing the degree of variation between them. Likert Scale through which we can measure the order. Scale: Divided into two parts: 1. Interval Scale 2. Ratio Scale 1.Interval Scale: In this measurement variables are measured in exact manner not in relative manner. Example: Likert Scale Net Promoter Score(NPS) Bipolar Matrix Table 2.Ratio Scale It allows researchers to compare the differences or intervals. The ratio scale has a unique feature. It possesses the character of the origin or z

                              SPSS – What Is It S PSS  means “ S tatistical  P ackage for the  S ocial  S ciences” and was first launched in 1968. Since SPSS was acquired by IBM in 2009, it's officially known as IBM SPSS.Generally it is used to analyze various types of data of sectors and most researcher uses this tool as it is open source anybody can use it for analysis purpose. How to Read data in SPSS It can be read through various sources: 1. Statistical file of SPSS. 2. Excel/CSV 3. Database Application 4. Text file 1.Reading Statistical file of SPSS. Statistical files are saved with the .sav extension. following are the steps. Open the “ Menu ” and go to “ File ” option. Select “ Open ”. Click on “ Data ”. Search for the file sample.sav and open it. The Data Editor displays the data in the file. 2. Reading data from   Excel/CSV 1. Open the "Menu"  and go to "file" menu. 2. Click on Import Data. 3.Click on Excel/CSV 3. Reading data from  Database Applicatio

                                                   FFrequency & Frequency Distribution It tells how often something happened.The frequency of an observation tells you the number of times observation occur in the data. For e.g, the frequency of number  5 is 4. ,5,2,5,6,5,2,4,5,8,5. Frequency Distribution: It is representation of data or you can say observation within the given interval. Example: Newspapers These are the numbers of newspapers sold at a local shop over the last 10 days: 22, 20, 18, 23, 20, 25, 22, 20, 18, 20 Let us count how many of each number there is: Papers Sold Frequency 18 2 19 0 20 4 21 0 22 2 23 1 24 0 25 1 Papers Sold Frequency 15-19 2 20-24 7 25-29 1 Grouped Frequency Data: When the data is sorted and classified into classes, then it is called as Grouped frequency data. For e.g. you can refer to this:   https://www.statisticshowto.com/grouped-data/ .

Range: It is the difference between the highest and the lowest value. for example, 1. Range(team 1): 17.5 - 10.5 = 7 2.  Range(team 2): 27.7 - 0 = 27.7 As ranges takes only the count of extreme values sometimes it may not give you a good impact on variability.  In this case, you can go for another measure of variability called interquartile range (IQR). Interquartile Range (IQR):  It is a better measure of dispersion than  range  because  it leaves out the extreme values . It equally divides the distribution into four equal parts called quartiles. First 25% is 1 st  quartile (Q1), last one is 3 rd  quartile (Q3) and middle one is 2 nd  quartile (Q2). 2 nd  quartile (Q2) divides the distribution into two equal parts of 50%. So, basically it is same as  Median. How to calculate IQR: Step 1: Order from low to high Step 2: Find the median or in other words Q2 Step 3: Then find Q1 by looking the median of the left side of Q2 Steps 4: Similarly find Q3 by looking the median of the right of Q

MMEAN,MEDIAN AND MODE In order to summarize your data, you can do this using two methods : Graphs. Central Tendency methods. 1 . Graphs: Using various graphs we can easily summarize our data and get into the result. 2. Central Tendency Method: It represents the center point or typical value of dataset. It represents the data to cluster to a middle value.   Measures of Central Tendency I t can be measured through three different: 1. Mean 2. Median 3. Mode 1. Mean: It is one of the common central tendency method used.It can used for discrete and continuous data.It is given by sum of all values of dataset divided by number of values in the dataset. So if we have n values in dataset and they have values like x1,x2,x3,x4 etc.                                               Mean Formula – Example #1 Let say you have a data set with 10 data points and we want to calculate mean for that.  Data set : {4,6,8,9,22,83,98,45,87,10} 2.  Median: The median is the middle score for a set of data that h

STATISTICS It is science of  data which is mainly concerned with collection,organizing,analysis and interpretation of data by providing suitable conclusion. For eg. data from healthcare, data of company sales. Some terminology of Statistics: 1. Data: It is collection of meaningful information presented in proper format.                                                                                           1.Primary Data: The data which is collected by  directly asking the questions related to research to people or respondent  and recording the responses. Some research do this by going on ground and asking to respondent and collecting data.For e.g. Take the election exit-pool where the company asks voters whom they voted,collect all data and prepare necessary conclusion,then they sell their conclusion to various news agency whom they  had tie-up. 2. Secondary  Data: T he data which can be collected through other sources, as the company collect it from research or data-collection wh