Module Four: Statistical Inference
Module Four: Statistical Inference - Estimation (point estimators and confidence intervals): Estimating population parameters and margins of error Properties of point estimators, including unbiasedness and variability Logic of confidence intervals, meaning of confidence level and confiden..
Module Four: Statistical Inference - Estimation (point estimators and confidence intervals): Estimating population parameters and margins of error Properties of point estimators, including unbiasedness and variability Logic of confidence intervals, meaning of confidence level and confiden..Module Three: Anticipating Patterns Probability: Interpreting probability, including long-run relative frequency interpretation 'Law of Large Numbers' concept Addition rule, multiplication rule, conditional probability, and independence Discrete random variables and their probability distributions, including binomial and ..
Probability: Interpreting probability, including long-run relative frequency interpretation 'Law of Large Numbers' concept Addition rule, multiplication rule, conditional probability, and independence Discrete random variables and their probability distributions, including binomial and ..Module Two: Sampling and Experimentation Overview of methods of data collection: Census Sample survey Experiment Observational study Planning and conducting surveys: Characteristics of a well-designed and well-conducted survey Populations, samples, and random selection Sources of bias in sampling and surveys Samp..
Overview of methods of data collection: Census Sample survey Experiment Observational study Planning and conducting surveys: Characteristics of a well-designed and well-conducted survey Populations, samples, and random selection Sources of bias in sampling and surveys Samp..Module One: Exploring Data Module One: Exploring Data - Constructing and interpreting graphical displays of distributions of univariate data: Dotplot, stemplot, histogram, cumulative frequency plot Center and spread Clusters and gaps Outliners and other unusual features Shape Summarizing distributions of univariate data: Mea..
Module One: Exploring Data - Constructing and interpreting graphical displays of distributions of univariate data: Dotplot, stemplot, histogram, cumulative frequency plot Center and spread Clusters and gaps Outliners and other unusual features Shape Summarizing distributions of univariate data: Mea..Variance of X:
If X is a discrete random variable then variance of X denoted by V(X) is defined as V(X) = E [X - E(X)] 2 V(X) = E(X 2 ) - [E(X)] 2 ..
If X is a discrete random variable then variance of X denoted by V(X) is defined as V(X) = E [X - E(X)] 2 V(X) = E(X 2 ) - [E(X)] 2 ..To find the variance:
We have = n(n-1) p 2 (p+q) n - 2 + np = n 2 p 2 - np 2 + np = n 2 p 2 + np(1-p) = n 2 p 2 + npq Now V(x) = E(x 2 ) - [E(x)] 2 = n 2 p 2 + npq - n 2 p 2 = n..
We have = n(n-1) p 2 (p+q) n - 2 + np = n 2 p 2 - np 2 + np = n 2 p 2 + np(1-p) = n 2 p 2 + npq Now V(x) = E(x 2 ) - [E(x)] 2 = n 2 p 2 + npq - n 2 p 2 = n..Mean and Variance of a Discrete Random Variable
Let X be a discrete random variable which can assume values x 1 , x 2 , x 3 ,x n with probabilities p 1 , p 2 , p 3 .. p n respectively then (a) Mean of X or expectation of X denoted by E(X) or m is given by (b) Variance of X denoted by s 2 is given ..
Let X be a discrete random variable which can assume values x 1 , x 2 , x 3 ,x n with probabilities p 1 , p 2 , p 3 .. p n respectively then (a) Mean of X or expectation of X denoted by E(X) or m is given by (b) Variance of X denoted by s 2 is given ..Example:
Calculate the standard deviation and the variance for the following data 7, 8, 11, 6, 13, 8, ..
Statistics XI Summary
Summary - The mean deviation (M.D) of a statistical data is defined as the arithmetic mean of the numerical values of the deviations of the items from some average. For an individual series, For a frequency distribution, If M.D is calculated about mean, then it is written as M.D (x). The ..
Summary - The mean deviation (M.D) of a statistical data is defined as the arithmetic mean of the numerical values of the deviations of the items from some average. For an individual series, For a frequency distribution, If M.D is calculated about mean, then it is written as M.D (x). The ..Example:
From the following data, compute the value of standard deviations and variance..
From the following data, compute the value of standard deviations and variance..See what our Users say :
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