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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...

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

Measures of Central Tendency :Mean, Median and Mode

Changing Color Blog Name Measures of Central Tendency I. Introduction. II. Requirements of good measures. III. Mean Definition. IV . Properties V. Merits and Demerits. VI. Examples VII. Weighted Arithmetic Mean VIII. Median IX. Quartiles I. Introduction Everybody is familiar with the word Average. and everybody are used the word average in daily life as, average marks, average of bike, average speed etc. In real life the average is used to represent the whole data, or it is a single figure is represent the whole data. the average value is lies around the centre of the data. consider the example if we are interested to measure the height of the all student and remember the heights of all student, in that case there are 2700 students then it is not possible to remember the all 2700 students height so we find out the one value that represent the height of the all 2700 students in college. therefore the single value represent ...

Business Statistics Notes ( Meaning, Scope, Limitations of statistics and sampling Methods)

Business Statistics Paper I Notes. Welcome to our comprehensive collection of notes for the Business Statistics! my aim is to provided you with the knowledge you need as you begin your journey to comprehend the essential ideas of this subject. Statistics is a science of collecting, Presenting, analyzing, interpreting data to make informed business decisions. It forms the backbone of modern-day business practices, guiding organizations in optimizing processes, identifying trends, and predicting outcomes. I will explore several important topics through these notes, such as: 1. Introduction to Statistics. : meaning definition and scope of Statistics. 2. Data collection methods. 3. Sampling techniques. 4. Measures of central tendency : Mean, Median, Mode. 5. Measures of Dispersion : Relative and Absolute Measures of dispersion, Range, Q.D., Standard deviation, Variance. coefficient of variation. 6.Analysis of bivariate data: Correlation, Regr...

Classification, Tabulation, Frequency Distribution, Diagrams & Graphical Presentation.

Business Statistics I Classification, Tabulation, Frequency Distribution , Diagrams & Graphical Presentation. In this section we study the following point : i. Classification and it types. ii. Tabulation. iii. Frequency and Frequency Distribution. iv. Some important concepts. v. Diagrams & Graphical Presentation I. Classification and it's types: Classification:- The process of arranging data into different classes or groups according to their common characteristics is called classification. e.g. we dividing students into age, gender and religion. It is a classification of students into age, gender and religion. Or Classification is a method used to categorize data into different groups based on the values of specific variable. The purpose of classification is to condenses the data, simplifies complexities, it useful to comparison and helps to analysis. The following are some criteria to classi...

Measures of Dispersion : Range , Quartile Deviation, Standard Deviation and Variance.

Measures of Dispersion : I. Introduction. II. Requirements of good measures. III. Uses of Measures of Dispersion. IV. Methods Of Studying Dispersion: i. Absolute Measures of Dispersions : i. Range (R) ii. Quartile Deviation (Q.D.) iii. Mean Deviation (M.D.) iv. Standard Deviation (S. D.) v. Variance ii. Relative Measures of Dispersions : i. Coefficient of Range ii. Coefficient of Quartile Deviation (Q.D.) iii. Coefficient of Mean Deviation (M.D.) iv. Coefficient of Standard Deviation (S. D.) v. Coefficien...

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 I ( Theory of estimation : Efficiency)

🔖Statistical Inference I ( Theory of estimation : Efficiency) In this article we see the terms: I. Efficiency. II. Mean Square Error. III. Consistency. 📚 Efficiency: We know that two unbiased estimator of parameter gives rise to infinitely many unbiased estimators of parameter. there if one of parameter have two estimators then the problem is to choose one of the best estimator among the class of unbiased estimators. in that case we need to some other criteria to to find out best estimator. therefore, that situation we check the variability of that estimator, the measure of variability of estimator T around it mean is Var(T). hence If T is an Unbiased estimator of parameter then it's variance gives good precision. the variance is smaller then it give's greater precision. 📑 i. Efficient estimator: An estimator T is said to be an Efficient Estimator of 𝚹, if T is unbiased estimator of 𝛉. and it's variance is less than any other estima...

The Power of Statistics: A Gateway to Exciting Opportunities

My Blog The Power of Statistics: A Gateway to Exciting Opportunities Hey there, future statistician! Ever wondered how Netflix seems to know exactly what shows you'll love, how sports teams break down player performance, or how businesses figure out their pricing strategies? The answer is statistics—a fascinating field that helps us make sense of data in our everyday lives. Let's dive into why choosing statistics for your B.Sc. Part First can lead you to some exciting opportunities. Why Statistics Matters in Everyday Life From predicting election outcomes and analyzing social media trends to understanding consumer behavior and optimizing public transport routes, statistics are crucial. It's the backbone of modern decision-making, helping us sift through complex data to uncover meaningful insights that drive innovation and progress. The Role of Statistics in Future Opportunities ...

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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