CLINICAL MEDICINE LECTURE NOTES 8TH EDITION PDF:2022

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 pdf

 

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 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

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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 𝛼 and 𝛽 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

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

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