Disclaimer

Disclaimer for Shree GaneshA Statistics

Please feel free to contact us by email at ganeshsatpute623@gmail.com if you have any queries concerning site's .

Disclaimers for Shree GaneshA Statistics

All the information on this website - http://gsstats.blogspot.com/ - is published in good faith and for general information purpose only. Shree GaneshA Statistics does not make any warranties about the completeness, reliability and accuracy of this information. Any action you take upon the information you find on this website (Shree GaneshA Statistics), is strictly at your own risk. Shree GaneshA Statistics will not be liable for any losses and/or damages in connection with the use of our website.

From our website, you can visit other websites by following hyperlinks to such external sites. . These links to other websites do not imply a recommendation for all the content found on these sites. Site owners and content may change without notice and may occur before we have the opportunity to remove a link which may have gone 'bad'.

Consent

You hereby agree to our disclaimer and accept its conditions by using our website.

Update

Any updates, modifications, or other changes we make to this document will be clearly noted here.

Comments

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

MCQ'S based on Basic Statistics (For B. Com. II Business Statistics)

(MCQ Based on Probability, Index Number, Time Series and Statistical Quality Control Sem - IV) 1.The control chart were developed by …… A) Karl Pearson B) R.A. fisher C) W.A. Shewhart D) B. Benjamin 2.the mean = 4 and variance = 2 for binomial r.v. x then value of n is….. A) 7 B) 10 C) 8 D)9 3.the mean = 3 and variance = 2 for binomial r.v. x then value of n is….. A) 7 B) 10 C) 8 D)9 4. If sampl...

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...

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...

B. Com. I Practical No. 3 :Diagrammatic representation of data by using Pie Diagram and Bar Diagrams.

Practical No. 3 :Diagrammatic representation of data by using Pie Diagram and Bar Diagrams. Diagrammatic Presentation. We have observed the classification and tabulation method. We use this method to take a lot of information and make it fit into a small table. The reason we do this is to make the information more organized and easier to understand. Tabulation helps us arrange data neatly so that it's not messy and confusing. tabulation is a way to make big files of information look neat and tidy in a table. but better and beautiful way to represent data using diagrams and graphs. the diagram and graph have some advantages because that used to visualise the data. that helps to understand and give information easily to any common man or any one, following are the some advantages of diagram and graph. I. Advantages i. Data Representation: Diagrams and graphs are excellent for presenting data visually, making trends, comparisons, and statistical information easier to...

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...

Method of Moment & Maximum Likelihood Estimator: Method, Properties and Examples.

Statistical Inference I: Method Of Moment: One of the oldest method of finding estimator is Method of Moment, it was discovered by Karl Pearson in 1884. Method of Moment Estimator Let X1, X2, ........Xn be a random sample from a population with probability density function (pdf) f(x, θ) or probability mass function (pmf) p(x) with parameters θ1, θ2,……..θk. If μ r ' (r-th raw moment about the origin) then μ r ' = ∫ -∞ ∞ x r f(x,θ) dx for r=1,2,3,….k .........Equation i In general, μ 1 ' , μ 2 ' ,…..μ k ' will be functions of parameters θ 1 , θ 2 ,……..θ k . Let X 1 , X 2 ,……X n be the random sample of size n from the population. The method of moments consists of solving "k" equations (in Equation i) for θ 1 , θ 2 ,……..θ k to obtain estimators for the parameters by equating μ 1 ' , μ 2 ' ,…..μ k ' with the corresponding sample moments m 1 ' , m 2 ' ,…..m k ' . Where m r ' = sample m...

B. Com. I (OE) Basic Statistics Practical 6. Simple random sampling (with and without replacement) and stratified random sampling.

DEPARTMENT OF STATISTICS B. Com. I: Practical - I Expt. No. 6 Date: / / 2024 Title: Sampling: Simple random sampling (with and without replacement) and stratified random sampling. Q.1 In population of size N=20, the observation were 4, 7, 10, 14, 17, 18, 20, 24, 25, 27, 29, 30, 34, 37, 39, 40, 42, 45, 47, 48. Draw Random Sample of size 5 form given population using SRSWOR. Q.2 In population of size N= 10, the observation ...

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....

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...

Shree GaneshA Statistics

Search This Blog