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emphasizes intuitive understanding over formal proofs and includes numerous examples from diverse fields such as business, sports, health, and education. Below is a chapter-wise summary based on the available information:
Chapter 1: The Nature and Probability of Statistics
Introduction to statistics.
Descriptive and inferential statistics.
Variables and types of data.
Data collection and sampling techniques.
Observational and experimental studies.
Use of computers and calculators in statistics.
Chapter 2: Frequency Distribution and Graphs
Organizing data into frequency distributions.
Constructing histograms, frequency polygons, and ogives.
Other types of graphs.
Paired data and scatter plots.
Chapter 3: Data Description
Measures of central tendency (mean, median, mode).
Measures of variation (range, variance, standard deviation).
Measures of position (percentiles, quartiles).
Exploratory data analysis.
Chapter 4: Counting Techniques
Tree diagrams and the multiplication rule for counting.
Permutations and combinations.
Chapter 5: Probability
Sample spaces and probability.
The addition rules for probability.
The multiplication rules and conditional probability.
Chapter 6: Discrete Probability Distributions
Probability distributions.
Mean, variance, standard deviation, and expectation.
The binomial distribution.
Chapter 7: The Normal Distribution
Normal distributions.
Applications of the normal distribution.
The central limit theorem.
The normal approximation to the binomial distribution.
Chapter 8: Confidence Intervals and Sample Size
Confidence intervals for the mean when the population standard deviation is known.
Confidence intervals for the mean when the population standard deviation is unknown.
Confidence intervals and sample size for proportions.
Confidence intervals for variances and standard deviations.
Chapter 9: Hypothesis Testing
Steps in hypothesis testing using the traditional method.
z-test for a mean.
t-test for a mean.
z-test for a proportion.
Chi-square test for a variance or standard deviation.
Chapter 10: Testing the Difference Between Two Means, Two Variances, and Two Proportions
Testing the difference between two means using the z-test.
Testing the difference between two means of independent samples using the t-test.
Testing the difference between means for dependent samples.
Testing the difference between proportions.
Testing the difference between two variances.
Chapter 11: Correlation and Regression
Scatter plots and correlation.
Regression analysis.
Coefficient of determination and standard error of the estimate.
Chapter 12: Other Chi-Square Tests
Test for goodness of fit.
Tests using contingency tables.
Chapter 13: Analysis of Variance (ANOVA)
One-way analysis of variance.
The Scheffé test and the Tukey test.
Two-way analysis of variance.
Chapter 14: Nonparametric Statistics
Advantages and disadvantages of nonparametric methods.
The sign test.
The Wilcoxon rank-sum test.
The Wilcoxon signed-rank test.
The Kruskal-Wallis test.
The Spearman rank correlation coefficient and the runs test.
Chapter 15: Sampling and Simulation
Common sampling techniques.
Surveys and questionnaire design.
Simulation techniques and the Monte Carlo method. Appendices
Algebra review.
Writing the research report.
Bayes’ theorem.
Alternate approach to the standard normal distribution.
Bibliography.
This structure provides a foundational understanding of statistical concepts, methods, and applications, catering to students from various disciplines.
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than this, feel free to contact us for instant access and downloads, please contact us via email or WhatsApp.
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