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..Introduction
Today graphics, tables, reasoning, estimation and prediction are playing a very important role in stating the facts in terms of figures. Statistics deals mainly in communicating facts and figures in terms of a method called statistical method. Collection, classificat..
Introduction
The scientific methods of collection of data, its classification and application to commerce and everyday life is called statistics. A list of some important terms as follows: (i) ungrouped data (ii) tabulation of data (iii) range (iv) frequency (v) frequency distribution (vi) tally..
Introduction
From our earlier chapter we know that, in statistical experiments, if the events A and B are independent, then But suppose the two events are not independent, that is the occurrence of one depends on the occurrence of other, then how do we compute This can be explained by conditional prob..
From our earlier chapter we know that, in statistical experiments, if the events A and B are independent, then But suppose the two events are not independent, that is the occurrence of one depends on the occurrence of other, then how do we compute This can be explained by conditional prob..Introduction
Facts and figures as such do not catch our attention unless they are presented in an interesting way. Graphical representation of data is one of the most commonly used modes of presentation. Graphs can not only be made attractive, but they are also easy to comprehend and do not take much time to re..
Introduction
We are already familiar with graphical reorientation of linear equations and inequations. This chapter describes the application of linear equations and inequations in solving different kinds of practical problems. Let us consider few examples to understand what we mean by linear programming proble..
Introduction
When some data is colleted, we have certain numbers which represent the characteristics of the data, around which the data appears to be concentrated, we call such numbers as the measures of central tendency. There are three measures of central tendency. (i) Mean (ii) Median and (iii) Mode. In this..
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