Statistical Inference II Notes

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...

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...

Random Number Generator

Shree Ganesha Statistics Blog - Random Number Generator Shree Ganesha Statistics Blog Random Number Generator Number of Random Numbers: Minimum Limit: Maximum Limit: Type of Random Number: Integer Decimal Generate

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....

Basic Concepts of Probability and Binomial Distribution , Poisson Distribution.

Probability: Basic concepts of Probability: Probability is a way to measure hoe likely something is to happen. Probability is number between 0 and 1, where probability is 0 means is not happen at all and probability is 1 means it will be definitely happen, e.g. if we tossed coin there is a 50% chance to get head and 50% chance to get tail, it can be represented in probability as 0.5 for each outcome to get head and tail. Probability is used to help us taking decision and predicting the likelihood of the event in many areas, that are science, finance and Statistics. Now we learn the some basic concepts that used in Probability: i) Random Experiment OR Trail: A Random Experiment is an process that get one or more possible outcomes. examples of random experiment include tossing a coin, rolling a die, drawing a card from pack of card etc. using this we specify the possible outcomes known as sample pace. ii)Outcome: An outcome is a result of experi...

Time Series

Time series Introduction:- We see the many variables are changes over period of time that are population (I.e. population are changes over time means population increase day by day), monthly demand of commodity, food production, agriculture production increases and that can be observed over period of times known as time series. Time series is defined as a set of observation arranged according to time is called time series. Or a time Series is a set of statistical observation arnging chronological order. ( Chronological order means it is arrangements of variable according to time) and it gives information about variable. Also we draw the graph of time series to see the behaviour of variable over time. It can be used of forecasting. The analysis of time series is helpful to economist, business men, also for scientist etc. Because it used to forecasting the future, observing the past behaviour of that variable or items. Also planning for future...

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...

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 Quality Control

Statistical Quality Control Statistical quality control (S. Q. C.) is a branch of Statistics it deals with the application of statistical methods to control and improve that quality of product. In this use statistical methods of sampling and test of significance to monitoring and controlling than quality of product during the production process. The most important word in statistical Quality control is quality The quality of product is the most important property while purchasing that product the product fulfill or meets the requirements and required specification we say it have good quality or quality product other wise not quality. Quality Control is the powerful technique to diagnosis the lack of quality in material, process of production. Causes of variation: When the product are produced in large scale there are variation in the size or composition the variation is inherent and inevitable in the quality of product these variation are clas...

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