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B. Com. -I Statistics Practical No. 1 Classification, tabulation and frequency distribution –I: Qualitative data.

Shree GaneshA B. Com. Part – I: Semester – I OE–I Semester – I (BASIC STATISTICS PRACTICAL-I) Practical: 60 Hrs. Marks: 50 (Credits: 02) Course Outcomes: After completion of this practical course, the student will be able to: i) apply sampling techniques in real life. ii) perform classification and tabulation of primary data. iii) represent the data by means of simple diagrams and graphs. iv) summarize data by computing measures of central tendency. LIST OF PRACTICALS: 1. Classification, tabulation and frequency distribution –I: Qualitative data. 2. Classification, tabulation and frequency distribution –II : Quantitative data. 3. Diagrammatic representation of data by using Pie Diagram and Bar Diagrams. 4. Graphical representation of data by using Histogram, Frequency Polygon, Frequency Curve and Locating Modal Value. 5. Graphical representation of data by using Ogive Curves and Locating Quartile Values....

Index Number

Index Number Introduction We seen in measures of central tendency the data can be reduced to a single figure by calculating an average and two series can be compared by their averages. But the data are homogeneous then the average is meaningful. (Data is homogeneous means data in same type). If the two series of the price of commodity for two years. It is clear that we cannot compare the cost of living for two years by using simple average of the price of the commodities. For that type of problem we need type of average is called Index number. Index number firstly defined or developed to study the effect of price change on the cost of living. But now days the theory of index number is extended to the field of wholesale price, industrial production, agricultural production etc. Index number is like barometers to measure the change in change in economics activities. An index may be defined as a " specialized average designed to measure the...

Non- Parametric Test: Run Test

Non- Parametric Test A Non-Parametric tests is a one of the part of Statistical tests that non-parametric test does not assume any particular distribution for analyzing the variable. unlike the parametric test are based on the assumption like normality or other specific distribution of the variable. Non-parametric test is based on the rank, order, signs, or other non-numerical data. we know both test parametric and non-parametric, but when use particular test? answer is that if the assumption of parametric test are violated such as data is not normally distributed or sample size is small. then we use Non-parametric test they can used to analyse categorical data or ordinal data and data are obtained form in field like psychology, sociology and biology. For the analysis use the some non-parametric test that are Wilcoxon signed-ranked test, mann-whiteny U test, sign test, Run test, Kruskal-wallis test. but the non-parametric test have lower statistical power than ...

Statistical Inference I ( Theory of Estimation) : Unbiased it's properties and examples

📚Statistical Inference I Notes The theory of estimation invented by Prof. R. A. Fisher in a series of fundamental papers in around 1930. Statistical inference is a process of drawing conclusions about a population based on the information gathered from a sample. It involves using statistical techniques to analyse data, estimate parameters, test hypotheses, and quantify uncertainty. In essence, it allows us to make inferences about a larger group (i.e. population) based on the characteristics observed in a smaller subset (i.e. sample) of that group. Notation of parameter: Let x be a random variable having distribution function F or f is a population distribution. the constant of distribution function of F is known as Parameter. In general the parameter is denoted as any Greek Letters as θ. now we see the some basic terms : i. Population : in a statistics, The group of individual under study is called Population. the population is may be a group of obj...

Statistical Inference Practical: Point Estimation by Method of Moment

B. Com. I OE - II Practical No. 3 Scatter Plot.

Practical No. 3 Scatter Plot. Correlation In previous blog we discussed about the measure central tendency and Dispersion to use to study the variable. the correlation is a statistical concept that allows us to measure and understand the relationship between two or more variables. it provided a valuable information about that variables. e.g. price and demand of commodity, income and expenditure of family, height and weight of group of persons. their we use the relation between this two variables. in above examples we see the one variable increases other variable is also changes in same or opposite direction. definition: Correlation is statistical tool which study the relationship between two or more variables. for analysis of correlation various method and techniques are used. example: i. Demand and supply of product ...

Non-Parametric Test 1.Sign test

Types of Non-Parametric Test : the non-parametric test is Broadly divided into three categories as: 1. One-sample Test 2.Two-sample test 3. K-sample test In this blog we discuss the one-sample tests, there are four types of one-sample non-parametric test as follows: 1. Run Test. 2.Sign Test 3. Wilcoxon Sing-Rank test 4. Kolmogorov-smirnov test. first we discuss the sign test. 1.Sign Test The sign test id one of the very simplest non-parametric test. the test is based on signs (i.e. plus and minus signs) hence is called sign test. the non-parametric test are alternative to the parametric one, therefore the sign test is alternative to parametric T-test, is used to test the median of population instead of mean of population. (i.e. Parametric T-test is used to testing mean of population and sign test is used to testing the median of the population they are alternative to each other when the data follows assumption of T-test we use parametric t-test, but i some situation th...

Median test

Non- Parametric test Median test Median test is also a Non-Parametric test and it is alternative to Parametric T test. The median test is used when we are interested to check the two independent sample have same median or not. It is useful when data is discrete or continuous and if data is in small size. Assumptions: I) the variable under study is ordinal scale II) the variable is random and Independent. The stepwise procedure for computation of median test for two independent sample : Step I :- firstly we define the hypothesis Null Hypothesis is the two independent sample have same median. Against Alternative Hypothesis is the two independent sample have different median. Step II :- In this step we combine two sample data. And calculating the median of combined data. Step III :- after that for testing hypothesis we constructing the (2x2) contingency table. For that table we divide the sample into two parts as number of observation above and below to the ...

Statistical Inference: Basic Terms and Definitions.

📚📖 Statistical Inference: Basic Terms. The theory of estimation is of paramount importance in statistics for several reasons. Firstly, it allows researchers to make informed inferences about population characteristics based on limited sample data. Since it is often impractical or impossible to measure an entire population, estimation provides a framework to generalize findings from a sample to the larger population. By employing various estimation methods, statisticians can estimate population parameters such as means, proportions, and variances, providing valuable insights into the population's characteristics. Second, the theory of estimating aids in quantifying the estimates' inherent uncertainty. Measures like standard errors, confidence intervals, and p-values are included with estimators to provide an idea of how accurate and reliable the estimates are. The range of possible values for the population characteristics and the degree of confidence attached to those est...

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