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 distribution of a sample mean Central Limit Theorem Sampling distribution of a ..
: 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 distribution of a sample mean Central Limit Theorem Sampling distribution of a ..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 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: Measurin..
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: Measurin..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..Class limits for Table (a):
The lowest values of various classes are 11, 16, 21,. These values are called lower limits. The greatest values of various classes are 15, 20, 25,. These values are called upper limits. Therefore, Lower limit of class 11 - 15 is 11 U..
The lowest values of various classes are 11, 16, 21,. These values are called lower limits. The greatest values of various classes are 15, 20, 25,. These values are called upper limits. Therefore, Lower limit of class 11 - 15 is 11 U..Summary
There are two types of frequency distribution. (a) Inclusive (b) Exclusive In the inclusive type, the upper limit of one class does not coincide with the lower limit of the next class. In the exclusive type, the upper limit of one clas..
Concepts of Ogive or S shaped curve
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 (..
Discrete Variables
A discrete variable can assume only integral values which can be counted. Number of pupils in a school, persons working in a factory are examples of discrete variables. A list of some important terms is given below. (i) ungrouped data (ii) tabulation of data (iii) range (iv) frequency (v) frequenc..
To Plot an Ogive
(i) We plot the points with coordinates having abscissae as actual lower limits and ordinates as the cumulative frequencies, (70.5, 2), (60.5, 7), (50.5, 13), (40.5, 23), (30.5, 37), (20.5, 49), (10.5, 57), (0.5, 60) are the coordinates of the points. (ii) Join the points by a ..
Tally mark
It is a vertical stroke put to represent a particular score. After 4 vertical strokes the fifth stroke is drawn across them to make a bunch of five. This makes the totalling easier. If the number of scores is large, then the range is divided into a number of classes. The tally score is obtained fo..
It is a vertical stroke put to represent a particular score. After 4 vertical strokes the fifth stroke is drawn across them to make a bunch of five. This makes the totalling easier. If the number of scores is large, then the range is divided into a number of classes. The tally score is obtained fo.. Result
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