To introduce the concepts of data analysis, presentation,
counting techniques, probability and
decision making.
decision making.
Statistics and Data Analysis.Collection of Data.Measures of
Location.Measures of
Variability.Discrete and Continuous Data.Statistical Modeling. Scientific Inspection, and
Graphical, General Types of Statistical Studies. Probability.Random Variables and Probability
Distributions.Mathematical Expectation.Discrete Probability Distributions.Continuous
Probability Distributions.Fundamental Sampling Distributions and Data Descriptions.One- and
Two-Sample Estimation Problems.Single Sample Estimating.One- and Two-Sample Tests of
Hypotheses. Sample Tests. Simple Linear Regression and Correlation.Multiple Linear
Regression and Certain.
Variability.Discrete and Continuous Data.Statistical Modeling. Scientific Inspection, and
Graphical, General Types of Statistical Studies. Probability.Random Variables and Probability
Distributions.Mathematical Expectation.Discrete Probability Distributions.Continuous
Probability Distributions.Fundamental Sampling Distributions and Data Descriptions.One- and
Two-Sample Estimation Problems.Single Sample Estimating.One- and Two-Sample Tests of
Hypotheses. Sample Tests. Simple Linear Regression and Correlation.Multiple Linear
Regression and Certain.
1. Introduction to Statistics and Data Analysis:
Statistical Inference, Samples, Populations,
and the Role of Probability, Sampling Procedures;
Collection of Data, Measures of
Location: The Sample Mean and Median, Measures of Variability, Discrete and
Continuous Data, Statistical Modeling, Scientific Inspection, and Graphical, General
Types of Statistical Studies: Designed Experiment, Observational Study, and
Retrospective Study. [TB: Ch. 1]
Location: The Sample Mean and Median, Measures of Variability, Discrete and
Continuous Data, Statistical Modeling, Scientific Inspection, and Graphical, General
Types of Statistical Studies: Designed Experiment, Observational Study, and
Retrospective Study. [TB: Ch. 1]
2.
Probability: Sample Space, Events, Counting Sample Points,
Probability of an Event,
Additive Rules, Conditional Probability, Independence, and the Product Rule, Bayes'
Rule. [TB: Ch. 2]
Additive Rules, Conditional Probability, Independence, and the Product Rule, Bayes'
Rule. [TB: Ch. 2]
3.
Random Variables and Probability Distributions: Concept of
a Random Variable,
Discrete Probability Distributions, Continuous Probability Distributions, Joint Probability
Distributions. [TB: Ch. 3]
Discrete Probability Distributions, Continuous Probability Distributions, Joint Probability
Distributions. [TB: Ch. 3]
4.
Mathematical Expectation: Mean of a Random Variable, Variance
and Covariance of
Random Variables, Means and Variances of Linear Combinations of Random Variables,
Chebyshev's Theorem. [TB: Ch. 4]
Random Variables, Means and Variances of Linear Combinations of Random Variables,
Chebyshev's Theorem. [TB: Ch. 4]
5.
Discrete Probability Distributions: Binomial and
Multinomial Distributions,
Hypergeometric Distribution, Negative Binomial and Geometric Distributions, Poisson
Distribution and the Poisson Process. [TB: Ch. 5]
Hypergeometric Distribution, Negative Binomial and Geometric Distributions, Poisson
Distribution and the Poisson Process. [TB: Ch. 5]
6.
Continuous Probability Distributions: Continuous Uniform
Distribution, Normal
Distribution, Areas under the Normal Curve, Applications of the Normal Distribution,
Normal Approximation to the Binomial, Gamma and Exponential Distributions, Chi-
Squared Distribution, Beta Distribution. [TB: Ch. 6]
Distribution, Areas under the Normal Curve, Applications of the Normal Distribution,
Normal Approximation to the Binomial, Gamma and Exponential Distributions, Chi-
Squared Distribution, Beta Distribution. [TB: Ch. 6]
7.
Fundamental Sampling Distributions and Data Descriptions:
Random Sampling,
Sampling Distributions, Sampling Distribution of Means and the Central Limit Theorem.
Sampling Distribution of S2, t-Distribution, F-Quantile and Probability Plots. [TB: Ch. 8]
Sampling Distributions, Sampling Distribution of Means and the Central Limit Theorem.
Sampling Distribution of S2, t-Distribution, F-Quantile and Probability Plots. [TB: Ch. 8]
8.
One- and Two-Sample Estimation Problems: Introduction,
Statistical Inference, Classical
Methods of Single Sample: Estimating the Mean, Standard Error of a Point, Prediction
Intervals, Tolerance Limits, Estimating the Difference between Two Means. [TB: Ch. 9]
Methods of Single Sample: Estimating the Mean, Standard Error of a Point, Prediction
Intervals, Tolerance Limits, Estimating the Difference between Two Means. [TB: Ch. 9]
9.
Single Sample: Estimating a Proportion, Estimating the
Difference between Two
Proportions, Single Sample: Estimating the Variance, Estimating the Ratio of Two
Variances. [TB: Ch. 9]
Proportions, Single Sample: Estimating the Variance, Estimating the Ratio of Two
Variances. [TB: Ch. 9]
10.
One- and Two-Sample Tests of Hypotheses: Statistical
Hypotheses: General Concepts,
Testing a Statistical Hypothesis, The Use of P-Values for Decision Making in Testing
Hypotheses. [TB: Ch. 10]
Testing a Statistical Hypothesis, The Use of P-Values for Decision Making in Testing
Hypotheses. [TB: Ch. 10]
11.
Single Sample: Tests Concerning a Single Mean, Two Samples:
Tests on Two Means,
Choice of Sample Size for Testing Means, Graphical Methods for Comparing Means,
One Sample: Test on a Single Proportion, Two Samples: Tests on Two Proportions. [TB:
Ch. 10]
Choice of Sample Size for Testing Means, Graphical Methods for Comparing Means,
One Sample: Test on a Single Proportion, Two Samples: Tests on Two Proportions. [TB:
Ch. 10]
12.
One- and Two-Sample Tests Concerning Variances,
Goodness-of-Fit Test, Test for
Independence (Categorical Data), Test for Homogeneity [TB: Ch. 10]
Independence (Categorical Data), Test for Homogeneity [TB: Ch. 10]
13.
Simple Linear Regression and Correlation: Introduction to
Linear Regression, The
Simple Linear Regression Model, Least Squares and the Fitted Model, Properties of the
Least Squares Estimators. [TB: Ch. 11]
Simple Linear Regression Model, Least Squares and the Fitted Model, Properties of the
Least Squares Estimators. [TB: Ch. 11]
14.
Multiple Linear Regression and Certain: Nonlinear
Regression Models, Introduction,
Estimating the Coefficients, Linear Regression Model Using Matrices, Properties of the
Least Squares Estimators. [TB: Ch. 12]
Estimating the Coefficients, Linear Regression Model Using Matrices, Properties of the
Least Squares Estimators. [TB: Ch. 12]
•
Probability and Statistics for Engineers and Scientists by
Ronald E. Walpole, Raymond
H. Myers, Sharon L. Myers and Keying E. Ye, Pearson; 9th Edition (January 6, 2011).
ISBN-10: 0321629116
H. Myers, Sharon L. Myers and Keying E. Ye, Pearson; 9th Edition (January 6, 2011).
ISBN-10: 0321629116
•
Probability and Statistics for Engineers and Scientists by
Anthony J. Hayter, Duxbury
Press; 3rd Edition (February 3, 2006), ISBN-10: 0495107573
Press; 3rd Edition (February 3, 2006), ISBN-10: 0495107573
•
Schaum's Outline of Probability and Statistics, by John
Schiller, R. AluSrinivasan and
Murray Spiegel, McGraw-Hill; 3rd Edition (2008). ISBN-10: 0071544259
Murray Spiegel, McGraw-Hill; 3rd Edition (2008). ISBN-10: 0071544259
•
Probability: A Very Short Introduction by John Haigh,
Oxford University Press (2012).
ISBN-10: 0199588481
ISBN-10: 0199588481
Note: This content is obtained from official documents of University
of Sargodha and applied on BS Computer Science for Main Campus,
Sub Campuses, and Affiliated Colleges.
0 comments:
Post a Comment