In this following article we share PDF link of CLINICAL MEDICINE LECTURE NOTES 8TH Edition free with a quick review and features. You can easily download the PDF from the end section of this post by clicking the link.
Clinical Medicine Lecture Notes is a concise guide with an updated content throughout the text. Featuring guide lecture notes for history taking and examination as well as essentials of clinical medicine on a system-by-system basis.
Two section separation of this notes, part one exploring communication and physical examination techniques while the second part of the text covers a range of common diseases. A comprehensive guide to provide core knowledge required for assessing and diagnosing diseases in the main systems of the body.
Features Of Lectures Notes Clinical Medicine 8th Edition PDF:
Following are the few important feature are given below;
- High-yield summary and evidence-based medicine boxes covering both the clinical approach and the essential background knowledge
- OSCE exam summaries that medical students and junior doctors need to know.
- Full-colour illustrations and photographs with updated content throughout the text.
TABLE OF CONTENTS:
Following are the complete contents of Clinical Medicine Lecture Notes;
1 Data and Case Studies 1
- 1.1 Case Study: Flight Delays 1
- 1.2 Case Study: BirthWeights of Babies 2
- 1.3 Case Study: Verizon Repair Times 3
- 1.4 Case Study: Iowa Recidivism 4
- 1.5 Sampling 5
- 1.6 Parameters and Statistics 6
- 1.7 Case Study: General Social Survey 7
- 1.8 Sample Surveys 8
- 1.9 Case Study: Beer and HotWings 9
- 1.10 Case Study: Black Spruce Seedlings 10
- 1.11 Studies 10
- 1.12 Google Interview Question: Mobile Ads Optimization 12
- Exercises 16
2 Exploratory Data Analysis 21
- 2.1 Basic Plots 21
- 2.2 Numeric Summaries 25
- 2.2.1 Center 25
- 2.2.2 Spread 26
- 2.2.3 Shape 27
- 2.3 Boxplots 28
- 2.4 Quantiles and Normal Quantile Plots 29
- 2.5 Empirical Cumulative Distribution Functions 35
- 2.6 Scatter Plots 38
- 2.7 Skewness and Kurtosis 40
3 Introduction to Hypothesis Testing: Permutation Tests 47
- 3.1 Introduction to Hypothesis Testing 47
- 3.2 Hypotheses 48
- 3.3 Permutation Tests 50
- 3.3.1 Implementation Issues 55
- 3.3.2 One-sided and Two-sided Tests 61
- 3.3.3 Other Statistics 62
- 3.3.4 Assumptions 64
- 3.3.5 Remark on Terminology 68
- 3.4 Matched Pairs 68
- Exercises 70
4 Sampling Distributions 75
- 4.1 Sampling Distributions 75
- 4.2 Calculating Sampling Distributions 80
- 4.3 The Central LimitTheorem 84
- 4.3.1 CLT for Binomial Data 86
- 4.3.2 Continuity Correction for Discrete Random Variables 89
- 4.3.3 Accuracy of the Central Limit Theorem∗ 91
- 4.3.4 CLT for SamplingWithout Replacement 92
- Exercises 93
5 Introduction to Confidence Intervals: The Bootstrap 103
- 5.1 Introduction to the Bootstrap 103
- 5.2 The Plug-in Principle 110
- 5.2.1 Estimating the Population Distribution 112
- 5.2.2 How Useful Is the Bootstrap Distribution? 113
- 5.3 Bootstrap Percentile Intervals 118
- 5.4 Two-Sample Bootstrap 119
- 5.4.1 Matched Pairs 124
- 5.5 Other Statistics 128
- 5.6 Bias 131
- 5.7 Monte Carlo Sampling: The “Second Bootstrap Principle” 134
- 5.8 Accuracy of Bootstrap Distributions 135
- 5.8.1 Sample Mean: Large Sample Size 135
- 5.8.2 Sample Mean: Small Sample Size 137
- 5.8.3 Sample Median 138
- 5.8.4 Mean–Variance Relationship 138
- 5.9 HowMany Bootstrap Samples Are Needed? 140
- Exercises 141
6 Estimation 149
- 6.1 Maximum Likelihood Estimation 149
- 6.1.1 Maximum Likelihood for Discrete Distributions 150
- 6.1.2 Maximum Likelihood for Continuous Distributions 153
- 6.1.3 Maximum Likelihood for Multiple Parameters 157
- 6.2 Method of Moments 161
- 6.3 Properties of Estimators 163
- 6.3.1 Unbiasedness 164
- 6.3.2 Efficiency 167
- 6.3.3 Mean Square Error 171
- 6.3.4 Consistency 173
- 6.3.5 Transformation Invariance∗ 175
- 6.3.6 Asymptotic Normality of MLE∗ 177
- 6.4 Statistical Practice 178
- 6.4.1 Are You Asking the Right Question? 179
- 6.4.2 Weights 179
- Exercises 180
- 7 More Confidence Intervals 187
- 7.1 Confidence Intervals for Means 187
- 7.1.1 Confidence Intervals for a Mean, Variance Known 187
- 7.1.2 Confidence Intervals for a Mean, Variance Unknown 192
- 7.1.3 Confidence Intervals for a Difference in Means 198
- 7.1.4 Matched Pairs, Revisited 204
- 7.2 Confidence Intervals in General 204
- 7.2.1 Location and Scale Parameters∗ 208
- 7.3 One-sided Confidence Intervals 212
- 7.4 Confidence Intervals for Proportions 214
- 7.4.1 Agresti–Coull Intervals for a Proportion 217
- 7.4.2 Confidence Intervals for a Difference of Proportions 218
- 7.5 Bootstrap Confidence Intervals 219
- 7.5.1 t Confidence Intervals Using Bootstrap Standard Errors 219
- 7.5.2 Bootstrap t Confidence Intervals 220
- 7.5.3 Comparing Bootstrap t and Formula t Confidence Intervals 224
- 7.6 Confidence Interval Properties 226
- 7.6.1 Confidence Interval Accuracy 226
- 7.6.2 Confidence Interval Length 227
- 7.6.3 Transformation Invariance 227
- 7.6.4 Ease of Use and Interpretation 227
- 7.6.5 Research Needed 228
- Exercises 228
8 More Hypothesis Testing 241
- 8.1 Hypothesis Tests for Means and Proportions: One Population 241
- 8.1.1 A Single Mean 241
- 8.1.2 One Proportion 244
- 8.2 Bootstrap t-Tests 246
- 8.3 Hypothesis Tests for Means and Proportions: Two Populations 248
- 8.3.1 Comparing Two Means 248
- 8.3.2 Comparing Two Proportions 251
- 8.3.3 Matched Pairs for Proportions 254
- 8.4 Type I and Type II Errors 255
- 8.4.1 Type I Errors 257
- 8.4.2 Type II Errors and Power 261
- 8.4.3 P-Values versus Critical Regions 266
- 8.5 Interpreting Test Results 267
- 8.5.1 P-Values 267
- 8.5.2 On Significance 268
- 8.5.3 Adjustments for Multiple Testing 269
- 8.6 Likelihood Ratio Tests 271
- 8.6.1 Simple Hypotheses and the Neyman–Pearson Lemma 271
- 8.6.2 Likelihood Ratio Tests for Composite Hypotheses 275
- 8.7 Statistical Practice 279
- 8.7.1 More Campaigns with No Clicks and No Conversions 284
- Exercises 285
9 Regression 297
- 9.1 Covariance 297
- 9.2 Correlation 301
- 9.3 Least-Squares Regression 304
- 9.3.1 Regression Toward the Mean 308
- 9.3.2 Variation 310
- 9.3.3 Diagnostics 311
- 9.3.4 Multiple Regression 317
- 9.4 The Simple LinearModel 317
- 9.4.1 Inference for ? and ? 322
- 9.4.2 Inference for the Response 326
- 9.4.3 Comments about Assumptions for the Linear Model 330
- 9.5 Resampling Correlation and Regression 332
- 9.5.1 Permutation Tests 335
- 9.5.2 Bootstrap Case Study: Bushmeat 336
- 9.6 Logistic Regression 340
- 9.6.1 Inference for Logistic Regression 346
- Exercises 350
10 Categorical Data 359
- 10.1 Independence in Contingency Tables 359
- 10.2 Permutation Test of Independence 361
- 10.3 Chi-square Test of Independence 365
- 10.3.1 Model for Chi-square Test of Independence 366
- 10.3.2 2 × 2 Tables 368
- 10.3.3 Fisher’s Exact Test 370
- 10.3.4 Conditioning 371
- 10.4 Chi-square Test of Homogeneity 372
- 10.5 Goodness-of-fit Tests 374
- 10.5.1 All Parameters Known 374
- 10.5.2 Some Parameters Estimated 377
- 10.6 Chi-square and the Likelihood Ratio∗ 379
- Exercises 380
11 Bayesian Methods 391
- 11.1 Bayes Theorem 392
- 11.2 Binomial Data: Discrete Prior Distributions 392
- 11.3 Binomial Data: Continuous Prior Distributions 400
- 11.4 Continuous Data 406
- 11.5 Sequential Data 409
- Exercises 414
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10 Glaucoma, 102
12 One-way ANOVA 419
- 12.1 Comparing Three or More Populations 419
- 12.1.1 The ANOVA F-test 419
- 12.1.2 A Permutation Test Approach 428
- Exercises 429
13 Additional Topics 433
- 13.1 Smoothed Bootstrap 433
- 13.1.1 Kernel Density Estimate 435
- 13.2 Parametric Bootstrap 437
- 13.3 The Delta Method 441
- 13.4 Stratified Sampling 445
- 13.5 Computational Issues in Bayesian Analysis 446
- 13.6 Monte Carlo Integration 448
- 13.7 Importance Sampling 452
- 13.7.1 Ratio Estimate for Importance Sampling 458
- 13.7.2 Importance Sampling in Bayesian Applications 461
- 13.8 The EM Algorithm 467
- 13.8.1 General Background 469
- Exercises 472
Appendix A Review of Probability 477
- A.1 Basic Probability 477
- A.2 Mean and Variance 478
- A.3 The Normal Distribution 480
- A.4 The Mean of a Sample of RandomVariables 481
- A.5 Sums of Normal Random Variables 482
- A.6 The Law of Averages 483
- A.7 Higher Moments and the Moment-generating Function 484
Appendix B Probability Distributions 487
- B.1 The Bernoulli and Binomial Distributions 487
- B.2 The Multinomial Distribution 488
- B.3 The Geometric Distribution 490
- B.4 The Negative Binomial Distribution 491
- B.5 The Hypergeometric Distribution 492
- B.6 The Poisson Distribution 493
- B.7 The Uniform Distribution 495
- B.8 The Exponential Distribution 495
- B.9 The Gamma Distribution 497
- B.10 The Chi-square Distribution 499
- B.11 The Student’s t Distribution 502
- B.12 The Beta Distribution 504
- B.13 The F Distribution 505
- Exercises 507
Appendix C Distributions Quick Reference 509
- Solutions to Selected Exercises 513
- References 525
- Index 531
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