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Shree Ganesha Statistics Blog - Random Number Generator

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Statistical Inference II Notes

Likelihood Ratio Test 
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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....
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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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B. Com. I Practical No. 4 :Graphical representation of data by using Histogram, Frequency Polygon, Frequency Curve and Locating Modal Value.

Practical No. 4 Graphical representation of data by using Histogram, Frequency Polygon, Frequency Curve and Locating Modal Value   Graphical Representation: The representation of numerical data into graphs is called graphical representation of data. following are the graphs to represent a data i.                     Histogram ii.                 Frequency Polygon    iii.                Frequency Curve iv.        Locating Modal Value i.     Histogram: Histogram is one of the simplest methods to representing the grouped (continuous) frequency distribution. And histogram is defined as A pictorial representation of grouped (or continuous) frequency distribution to drawing a...
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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...
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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...
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Statistical Inference Practical: Point Estimation by Method of Moment

 
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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 ...
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B. Com. I. Practical No. 2 : Classification, tabulation and frequency distribution –II. Quantitative 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....
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