Module Three: Anticipating Patterns
: Properties of the Normal distribution Using tables of the Normal distribution The Normal distribution as a model for measurements Sampling distributions: Sampling distribution of a sample proportion Sampling dist..
: Properties of the Normal distribution Using tables of the Normal distribution The Normal distribution as a model for measurements Sampling distributions: Sampling distribution of a sample proportion Sampling dist..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 s..
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 s..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 confidence intervals, and properties of conf..
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 confidence intervals, and properties of conf..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 univari..
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 univari..Random Variables and Probability Distributions
Random Variables and Probability Distributions - It is often very important to allocate a numerical value to an outcome of a random experiment. For example, consider an experiment of tossing a coin twice and note the number of heads (x) obtained. Outcome HH HT TH TT No. of heads (x) 2 1 1..
Random Variables and Probability Distributions
It is often very important to allocate a numerical value to an outcome of a random experiment. For example, consider an experiment of tossing a coin twice and note the number of heads (x) obtained. Outcome HH HT TH TT No. of heads (x) 2 1 1 0 x is called a random variable, which can assume the valu..
Random Variables and Probability Distributions
Let S be a sample space associated with a given random experiment. A real valued function X which assigns to each w i S, a unique real number, X( w i ) = x i is called a random variable . Two types of random variables are 1. Continuous random variable, 2. discrete ra..
Summary
>If A, B and C are mutually exclusive then Total Probability: P(A) = P(E 1 ) P(A|E 1 ) + P(E 2 ) P(A|E 2 )+ +P(E n ) P(A|E n ) Random variable: A real valued function 'X' defined on the sample space is called a random variable. Discrete random variable: A random variable which can assume ..
>If A, B and C are mutually exclusive then Total Probability: P(A) = P(E 1 ) P(A|E 1 ) + P(E 2 ) P(A|E 2 )+ +P(E n ) P(A|E n ) Random variable: A real valued function 'X' defined on the sample space is called a random variable. Discrete random variable: A random variable which can assume ..Frequency Distribution
The number which tells us how many times a particular data appears is called the frequency. For example, 2 marks have been scored by five students which means marks 2 occurs five times. Therefore, the frequency of score 2 is five. Similarly, the frequency of marks 5 is three because three stu..
Binomial Distribution
A trial, which has only two outcomes i.e., "a success" or "a failure", is called a Bernoulli trial . The probability distribution of the number of successes, so obtained is called the binomial distribution..
Result
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