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Page 1 PROBABILITY DISTRIBUTIONS BINOMIAL, POISSON, NORMAL Page 2 PROBABILITY DISTRIBUTIONS BINOMIAL, POISSON, NORMAL DISTRIBUTION ? Frequency Distribution: It is a listing of observed / actual frequencies of all the outcomes of an experiment that actually occurred when experiment was done. ? Probability Distribution: It is a listing of the probabilities of all the possible outcomes that could occur if the experiment was done. ? It can be described as: ? A diagram (Probability Tree) ? A table ? A mathematical formula 2 Page 3 PROBABILITY DISTRIBUTIONS BINOMIAL, POISSON, NORMAL DISTRIBUTION ? Frequency Distribution: It is a listing of observed / actual frequencies of all the outcomes of an experiment that actually occurred when experiment was done. ? Probability Distribution: It is a listing of the probabilities of all the possible outcomes that could occur if the experiment was done. ? It can be described as: ? A diagram (Probability Tree) ? A table ? A mathematical formula 2 TYPES OF PROBABILITY DISTRIBUTION Probability Distribution Discrete PD Binomial Distribution Poisson Distribution Continuous PD Normal Distribution 3 Page 4 PROBABILITY DISTRIBUTIONS BINOMIAL, POISSON, NORMAL DISTRIBUTION ? Frequency Distribution: It is a listing of observed / actual frequencies of all the outcomes of an experiment that actually occurred when experiment was done. ? Probability Distribution: It is a listing of the probabilities of all the possible outcomes that could occur if the experiment was done. ? It can be described as: ? A diagram (Probability Tree) ? A table ? A mathematical formula 2 TYPES OF PROBABILITY DISTRIBUTION Probability Distribution Discrete PD Binomial Distribution Poisson Distribution Continuous PD Normal Distribution 3 PROBABILITY DISTRIBUTION ? Discrete Distribution: Random Variable can take only limited number of values. Ex: No. of heads in two tosses. ? Continuous Distribution: Random Variable can take any value. Ex: Height of students in the class. 4 Page 5 PROBABILITY DISTRIBUTIONS BINOMIAL, POISSON, NORMAL DISTRIBUTION ? Frequency Distribution: It is a listing of observed / actual frequencies of all the outcomes of an experiment that actually occurred when experiment was done. ? Probability Distribution: It is a listing of the probabilities of all the possible outcomes that could occur if the experiment was done. ? It can be described as: ? A diagram (Probability Tree) ? A table ? A mathematical formula 2 TYPES OF PROBABILITY DISTRIBUTION Probability Distribution Discrete PD Binomial Distribution Poisson Distribution Continuous PD Normal Distribution 3 PROBABILITY DISTRIBUTION ? Discrete Distribution: Random Variable can take only limited number of values. Ex: No. of heads in two tosses. ? Continuous Distribution: Random Variable can take any value. Ex: Height of students in the class. 4 H H H T T T HH HT TH TT 2 nd 1 st Possible Outcomes TREE DIAGRAM – A FAIR COIN IS TOSSED TWICERead More
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FAQs on PPT - Theoretical Distribution - Quantitative Aptitude for CA Foundation
| 1. What is a theoretical distribution? |  |
Ans. A theoretical distribution is a mathematical function that describes the probability of occurrence of different values in a given dataset. It is a theoretical representation of the data and helps in understanding the underlying pattern or model that generates the observed values.
| 2. What is the significance of theoretical distributions in CA Foundation? | |
Ans. Theoretical distributions are significant in CA Foundation as they provide a framework for understanding and analyzing various financial and statistical concepts. They help in making informed decisions based on probability and enable the prediction of future outcomes using mathematical models.
| 3. What are some common examples of theoretical distributions used in CA Foundation? | |
Ans. Some common examples of theoretical distributions used in CA Foundation include the normal distribution, binomial distribution, Poisson distribution, and exponential distribution. These distributions have specific characteristics and are suitable for different types of data analysis and modeling.
| 4. How are theoretical distributions applied in CA Foundation exams? | |
Ans. In CA Foundation exams, theoretical distributions are applied to solve problems related to probability, hypothesis testing, and statistical inference. Students are expected to understand the properties and applications of different theoretical distributions to answer questions accurately and efficiently.
| 5. Can you provide an example of how theoretical distributions are used in CA Foundation? | |
Ans. Sure! Let's consider an example where a CA Foundation student wants to analyze the sales data of a company. By applying the normal distribution, the student can calculate the probability of sales falling within a certain range, estimate the average sales value, and determine the likelihood of achieving specific sales targets. Theoretical distributions allow for data-driven decision making in such scenarios.
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