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As you are reading this article (or attempting to read!), you would most probably know that Quantitative Methods section is worth 12% of your CFA L1 exam score. Probability distribution is denoted by p(x) & is the probability that a random variable X takes on the value x, or p(x) = p(X = x).
Discrete and Continuous Distributions Probability distribution can be discrete or continuous. Trusted by Fortune 500 Companies and 10,000 Students from 40+ countries across the globe, it is one of the leading International Training providers for Finance Certifications like FRM®, CFA®, PRM®, Business Analytics, HR Analytics, Financial Modeling, and Operational Risk Modeling. CFA Institute does not endorse, promote, or warrant the accuracy or quality of the products or services offered by EduPristine.
Utmost care has been taken to ensure that there is no copyright violation or infringement in any of our content. Objective:This section will explain the meaning of the Confidence Interval (CI) in statistical analysis. The CI states that with 92% confidence, the proportion of all similar companies with the plan will between 46% and 56%. 15-degree angle translates into a 3.5% error in the measurement of catheter depth on axial CT images.
DdBM correspond to scenario III, depending on prostate and catheter movement relative to the bony marker. 2-fraction HDR prostate brachytherapy, with the use of dental putty for fixation of catheters. Discrete Uniform Distribution It is the simplest type of probability distribution, where all outcomes have equal probability of occurring.


CFA Institute, CFA®, Claritas® and Chartered Financial Analyst® are trademarks owned by CFA Institute. Often, statistics are not expressed in terms of one number but rather as a range or an interval with a given level of confidence. Whereas, if a random variable is continuous, for example, the amount of rain per day (in cm or inches), the distribution is known as continuous probability distribution. Further, GARP® is not responsible for any fees or costs paid by the user to EduPristine nor is GARP® responsible for any fees or costs of any person or entity providing any services to EduPristine Study Program.
For proportions, the normal distribution approximates the binomial for n x P(hat) is greater than or equal to 5.Most common confidence interval selections are 90%, 95%, or 99% but are dependent on the voice of the customer, your company, project, and other factors. The larger your sample size, the more confidence one can be that their answers represent the population.
If the sample is not then one cannot rely on the confidence intervals calculated, because you can no longer rely on the measures of central tendency and dispersion.Sampling plans are an important step to ensure the data taken within is reflective and meaningful to represent the population. Click here for information regarding sampling plans.PercentageThe accuracy of the CI also depends on the percentage of your sample that picks a particular answer. Binomial Distribution A binary variable that takes on one of two values (usually 1 for success or 0 for failure) is known as a Bernoulli random variable.
For example, a single toss of a coin is a Bernoulli random variable where we can denote H as success and T as failure or vice versa. It is easier to be sure of extreme answers than those aren't, thus the interval is not linear.
It is represented mathematically as p(success) = p(1) = P(X=1) = p p(failure) = p(0) = P(X=0) = 1 - p Now, suppose you toss 10 coins and ask me the probability of 4 heads occurring.


But, the probability of the amount of rain between a range (say 40-50cm) has some positive value.
A Binomial random variableX ~ B(n, p) is defined is the number of successes in n Bernoulli random trials where p is the probability of success on any one Bernoulli trial. Mathematically, It is important to note that the probability (p) of success is 0.5 here, but may not always be the case. But, in case of continuous distribution, probability of a single point will be zero and probability is always given as a range.
This is the Expected value of a binomial trail and is given mathematically as E(X) = np In case of 10 unbiased coins, E(X) = 10 x A? = 5 Similarly, the variance is given as Var(X) = np(1-p) In case of 10 unbiased coins, Var(X) = 10 x A? x (1 - A?) = 2.5 Continuous distributions 3. Continuous Uniform Distribution This is another simple distribution, where the probability of occurrence all the events is constant and equal. Amount of rainfall (in cm) occurring on a particular day Now, let's proceed to the distributions. Given below are the 4 common types of probability distributions that can be asked in CFA level 1 examination. We can see that the distribution is skewed to the right and is bounded from below by 0, which means that it cannot assume negative values.



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